αILP: thinking visual scenes as differentiable logic programs
Deep neural learning has shown remarkable performance at learning representations for visual object categorization. However, deep neural networks such as CNNs do not explicitly encode objects and relations among them. This limits their success on tasks that require a deep logical understanding of vi...
Uloženo v:
| Vydáno v: | Machine learning Ročník 112; číslo 5; s. 1465 - 1497 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer US
01.05.2023
Springer Nature B.V |
| Témata: | |
| ISSN: | 0885-6125, 1573-0565 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Deep neural learning has shown remarkable performance at learning representations for visual object categorization. However, deep neural networks such as CNNs do not explicitly encode objects and relations among them. This limits their success on tasks that require a deep logical understanding of visual scenes, such as Kandinsky patterns and Bongard problems. To overcome these limitations, we introduce
α
ILP
, a novel differentiable inductive logic programming framework that learns to represent scenes as logic programs—intuitively, logical atoms correspond to objects, attributes, and relations, and clauses encode high-level scene information.
α
ILP has an end-to-end reasoning architecture from visual inputs. Using it,
α
ILP performs differentiable inductive logic programming on complex visual scenes, i.e., the logical rules are learned by gradient descent. Our extensive experiments on
Kandinsky patterns
and
CLEVR-Hans
benchmarks demonstrate the accuracy and efficiency of
α
ILP
in learning complex visual-logical concepts. |
|---|---|
| AbstractList | Deep neural learning has shown remarkable performance at learning representations for visual object categorization. However, deep neural networks such as CNNs do not explicitly encode objects and relations among them. This limits their success on tasks that require a deep logical understanding of visual scenes, such as Kandinsky patterns and Bongard problems. To overcome these limitations, we introduce
α
ILP
, a novel differentiable inductive logic programming framework that learns to represent scenes as logic programs—intuitively, logical atoms correspond to objects, attributes, and relations, and clauses encode high-level scene information.
α
ILP has an end-to-end reasoning architecture from visual inputs. Using it,
α
ILP performs differentiable inductive logic programming on complex visual scenes, i.e., the logical rules are learned by gradient descent. Our extensive experiments on
Kandinsky patterns
and
CLEVR-Hans
benchmarks demonstrate the accuracy and efficiency of
α
ILP
in learning complex visual-logical concepts. Deep neural learning has shown remarkable performance at learning representations for visual object categorization. However, deep neural networks such as CNNs do not explicitly encode objects and relations among them. This limits their success on tasks that require a deep logical understanding of visual scenes, such as Kandinsky patterns and Bongard problems. To overcome these limitations, we introduce $$\alpha {\textit{ILP}}$$ α ILP , a novel differentiable inductive logic programming framework that learns to represent scenes as logic programs—intuitively, logical atoms correspond to objects, attributes, and relations, and clauses encode high-level scene information. $$\alpha$$ α ILP has an end-to-end reasoning architecture from visual inputs. Using it, $$\alpha$$ α ILP performs differentiable inductive logic programming on complex visual scenes, i.e., the logical rules are learned by gradient descent. Our extensive experiments on Kandinsky patterns and CLEVR-Hans benchmarks demonstrate the accuracy and efficiency of $$\alpha {\textit{ILP}}$$ α ILP in learning complex visual-logical concepts. Deep neural learning has shown remarkable performance at learning representations for visual object categorization. However, deep neural networks such as CNNs do not explicitly encode objects and relations among them. This limits their success on tasks that require a deep logical understanding of visual scenes, such as Kandinsky patterns and Bongard problems. To overcome these limitations, we introduce αILP, a novel differentiable inductive logic programming framework that learns to represent scenes as logic programs—intuitively, logical atoms correspond to objects, attributes, and relations, and clauses encode high-level scene information. αILP has an end-to-end reasoning architecture from visual inputs. Using it, αILP performs differentiable inductive logic programming on complex visual scenes, i.e., the logical rules are learned by gradient descent. Our extensive experiments on Kandinsky patterns and CLEVR-Hans benchmarks demonstrate the accuracy and efficiency of αILP in learning complex visual-logical concepts. |
| Author | Kersting, Kristian Shindo, Hikaru Dhami, Devendra Singh Pfanschilling, Viktor |
| Author_xml | – sequence: 1 givenname: Hikaru surname: Shindo fullname: Shindo, Hikaru email: hikaru.shindo@cs.tu-darmstadt.de organization: TU Darmstadt – sequence: 2 givenname: Viktor surname: Pfanschilling fullname: Pfanschilling, Viktor organization: TU Darmstadt – sequence: 3 givenname: Devendra Singh surname: Dhami fullname: Dhami, Devendra Singh organization: TU Darmstadt, Hessian Center for AI (hessian.AI) – sequence: 4 givenname: Kristian surname: Kersting fullname: Kersting, Kristian organization: TU Darmstadt, Hessian Center for AI (hessian.AI), Centre for Cognitive Science, TU Darmstadt |
| BookMark | eNp9kM1KAzEUhYNUsK2-gKsB16M3mUwmcSFI8adQ0IWuQyaTjKnTTE2mgo_li_hMplYQXHR1uXC-e849EzTyvTcInWI4xwDVRcQgBM2BFDmwgkCOD9AYl1VaS1aO0Bg4L3OGSXmEJjEuAYAwzsbo6utzvni8zIYX51-db7N3Fzeqy6I23sRMxaxx1ppg_OBU3Zms61uns3Xo26BW8RgdWtVFc_I7p-j59uZpdp8vHu7ms-tFrknFcc6xFZwKDZYwI2xNFLY1TZkKxWvLS10DrRk3uGCKikY3VcmZUFVDDbVgRTFFZ7u7yfhtY-Igl_0m-GQpCU-vM8oYTiqyU-nQxxiMlevgVip8SAxy25Pc9SRTT_KnJ7mF-D9Iu0ENrvdDUK7bjxY7NCYf35rwl2oP9Q11LH6H |
| CitedBy_id | crossref_primary_10_1007_s10994_025_06798_x crossref_primary_10_1007_s10994_024_06610_2 crossref_primary_10_1177_02783649251353181 crossref_primary_10_1007_s10994_025_06780_7 crossref_primary_10_1016_j_knosys_2025_113906 crossref_primary_10_1016_j_artint_2023_104062 crossref_primary_10_3390_math13111707 |
| Cites_doi | 10.24963/ijcai.2017/371 10.1016/j.artint.2021.103602 10.1007/BF03037089 10.1609/aaai.v35i6.16637 10.1007/978-3-319-78090-0_10 10.1016/j.artint.2015.08.011 10.1007/BF00117105 10.24963/ijcai.2020/243 10.1007/978-3-642-96826-6 10.1609/aimag.v43i1.19122 10.24963/ijcai.2020/688 10.1098/rsfs.2018.0011 10.1016/j.artint.2021.103649 10.1007/3-540-62927-0 10.1016/j.artint.2021.103504 10.1007/s10994-021-06089-1 10.1016/j.artint.2021.103546 10.1109/ICCV.2017.322 10.1007/978-3-540-78652-8 10.1007/BF03037227 10.1109/CVPR46437.2021.00362 10.1007/978-3-030-29726-8_1 10.1145/35043.35046 10.7551/mitpress/1192.001.0001 10.1109/CVPR.2016.91 10.1609/aaai.v32i1.11806 10.1609/aaai.v35i18.17931 10.1007/s10994-019-05862-7 10.3233/FAIA210359 10.1109/CVPR.2016.90 10.1109/ICCV.2015.279 10.1613/jair.5714 10.24963/ijcai.2017/221 10.1007/978-3-319-11558-0_22 10.1007/s10994-018-5750-0 10.1017/S1471068413000689 10.1007/s10994-020-05934-z 10.1613/jair.1.11203 10.1109/CVPR.2017.215 10.1609/aaai.v36i8.20795 10.24963/ijcai.2019/847 10.1007/978-3-031-01574-8 10.1007/978-3-540-78469-2_23 10.1609/aaai.v35i6.16639 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2023 The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2023 – notice: The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION 3V. 7SC 7XB 88I 8AL 8AO 8FD 8FE 8FG 8FK ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- L7M L~C L~D M0N M2P P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U |
| DOI | 10.1007/s10994-023-06320-1 |
| DatabaseName | Springer Nature OA Free Journals CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central ProQuest Technology Collection ProQuest One Community College ProQuest Central Korea ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic |
| DatabaseTitle | CrossRef Computer Science Database ProQuest Central Student Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Pharma Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Collection ProQuest Computing ProQuest Science Journals (Alumni Edition) ProQuest Central Basic ProQuest Science Journals ProQuest Computing (Alumni Edition) ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition ProQuest One Academic ProQuest Central (Alumni) ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Computer Science Database |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central Database Suite (ProQuest) url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1573-0565 |
| EndPage | 1497 |
| ExternalDocumentID | 10_1007_s10994_023_06320_1 |
| GrantInformation_xml | – fundername: Technische Universität Darmstadt (3139) – fundername: TAILOR grantid: 952215 – fundername: SPAICER grantid: 01MK20015E – fundername: AICO |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 199 1N0 1SB 2.D 203 28- 29M 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 6TJ 78A 88I 8AO 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAEWM AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIVO ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACGOD ACHSB ACHXU ACKNC ACMDZ ACMLO ACNCT ACOKC ACOMO ACPIV ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BPHCQ BSONS C6C CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBLON EBS EIOEI EJD ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITG ITH ITM IWAJR IXC IZIGR IZQ I~X I~Y I~Z J-C J0Z JBSCW JCJTX JZLTJ K6V K7- KDC KOV KOW LAK LLZTM M0N M2P M4Y MA- MVM N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF- PQQKQ PROAC PT4 Q2X QF4 QM1 QN7 QO4 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZC RZE S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TAE TEORI TN5 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW VXZ W23 W48 WH7 WIP WK8 XJT YLTOR Z45 Z7R Z7S Z7U Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z85 Z86 Z87 Z88 Z8M Z8N Z8O Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z8Z Z91 Z92 ZMTXR AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP AMVHM ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7SC 7XB 8AL 8FD 8FK JQ2 L7M L~C L~D PKEHL PQEST PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c2781-81f9849c0f26e9fb2a1fb40883a8bf85cb04b68e136a49dcd75869a7d4e4f0f93 |
| IEDL.DBID | RSV |
| ISSN | 0885-6125 |
| IngestDate | Wed Nov 05 00:48:50 EST 2025 Sat Nov 29 01:43:29 EST 2025 Tue Nov 18 22:24:58 EST 2025 Fri Feb 21 02:45:46 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | Object-centric learning Neuro-symbolic AI Inductive logic programming Differentiable reasoning |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2781-81f9849c0f26e9fb2a1fb40883a8bf85cb04b68e136a49dcd75869a7d4e4f0f93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://link.springer.com/10.1007/s10994-023-06320-1 |
| PQID | 2809964661 |
| PQPubID | 54194 |
| PageCount | 33 |
| ParticipantIDs | proquest_journals_2809964661 crossref_primary_10_1007_s10994_023_06320_1 crossref_citationtrail_10_1007_s10994_023_06320_1 springer_journals_10_1007_s10994_023_06320_1 |
| PublicationCentury | 2000 |
| PublicationDate | 20230500 2023-05-00 20230501 |
| PublicationDateYYYYMMDD | 2023-05-01 |
| PublicationDate_xml | – month: 5 year: 2023 text: 20230500 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York – name: Dordrecht |
| PublicationTitle | Machine learning |
| PublicationTitleAbbrev | Mach Learn |
| PublicationYear | 2023 |
| Publisher | Springer US Springer Nature B.V |
| Publisher_xml | – name: Springer US – name: Springer Nature B.V |
| References | Sourek, G., Svatos, M., Zelezný, F., Schockaert, S., & Kuzelka, O. (2017). Stacked structure learning for lifted relational neural networks. In N. Lachiche, C. Vrain (Eds.), Proceedings of the 27th international conference on inductive logic programming. Lecture notes in computer science (Vol. 10759, pp. 140–151). Holzinger, A., Saranti, A., & Müller, H. (2021). Kandinsky patterns: An experimental exploration environment for pattern analysis and machine intelligence. In CoRRarXiv:2103.00519. KautzHThe third AI summer: AAAI Robert S. Engelmore memorial lectureAI Magazine20224319310410.1609/aimag.v43i1.19122 Goyal, K., Neubig, G., Dyer, C., & Berg-Kirkpatrick, T. (2018). A continuous relaxation of beam search for end-to-end training of neural sequence models. In Proceedings of the 32th AAAI conference on artificial intelligence (AAAI) (Vol. 32, No 1). Lloyd, J. W. (1984). Foundations of logic programming. Dai, W.-Z., Xu, Q., Yu, Y., & Zhou, Z.-H. (2019). Bridging machine learning and logical reasoning by abductive learning. In Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 32). LocatelloFWeissenbornDUnterthinerTMahendranAHeigoldGUszkoreitJDosovitskiyAKipfTObject-centric learning with slot attentionProceedings of the Advances in Neural Information Processing Systems (NeurIPS)2020331152511538 Santoro, A., Faulkner, R., Raposo, D., Rae, J., Chrzanowski, M., Weber, T., Wierstra, D., Vinyals, O., Pascanu, R., & Lillicrap, T. (2018). Relational recurrent neural networks. In: Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 31). Holzinger, A., Kickmeier-Rust, M., & Müller, H. (2019). Kandinsky patterns as IQ-test for machine learning. In Proceedings of the 3rd international cross-domain conference for machine learning and knowledge extraction (CD-MAKE) (pp. 1–14). Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., & De Raedt, L. (2018). Deepproblog: Neural probabilistic logic programming. In Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 31). Vedantam, R., Szlam, A., Nickel, M., Morcos, A., & Lake, B. M. (2021). Curi: A benchmark for productive concept learning under uncertainty. In Proceedings of the 38th international conference on machine learning (ICML) (Vol. 139, pp. 10519–10529). Pietruszka, M., Borchmann, L., & Gralinski, F. (2021). Successive halving top-k operator. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI) (Vol. 35, No. 18, pp. 15869–15870). QuinlanJRLearning logical definitions from relationsMachine Learning1990523926610.1007/BF00117105 KowalskiRAThe early years of logic programmingCommunications of the ACM1988311384384141510.1145/35043.35046 Redmon, J., Divvala, S. K., Girshick, R. B., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 779–788). Cropper, A., & Muggleton, S. H. (2016). Metagol system. https://github.com/metagol/metagol. Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., & Teh, Y. W. (2019). Set transformer: A framework for attention-based permutation-invariant neural networks. In Proceedings of the 36th international conference on machine learning (ICML) (Vol. 97, pp. 3744–3753). Raedt, L. d., Dumančić, S., Manhaeve, R., & Marra, G. (2020). From statistical relational to neuro-symbolic artificial intelligence. In Proceedings of the 29th international joint conference on artificial intelligence (IJCAI) (pp. 4943–4950). CropperAMorelRMuggletonSLearning higher-order logic programsMachine Learning201910912891322412506010.1007/s10994-019-05862-707255749 Cuturi, M., & Blondel, M. (2017). Soft-DTW: A differentiable loss function for time-series. In Proceedings of the 34th international conference on machine learning (ICML) (Vol. 70, pp. 894–903). Ray, O., & Inoue, K. (2007). Mode-directed inverse entailment for full clausal theories. In Proceedings of the 17th international conference on inductive logic programming (ILP). Lecture notes in computer science (Vol. 4894, pp. 225–238). Stammer, W., Schramowski, P., & Kersting, K. (2021). Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3619–3629). He, K., Gkioxari, G., Dollár, P., & Girshick, R. B. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 2980–2988). RussellSNorvigPArtificial intelligence: A modern approach20093Hoboken, NJPrentice Hall Press0835.68093 BongardMMHawkinsJKPattern recognition1970New YorkSpartan Books0205.21201 Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. B. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1988–1997). KimJRicciMSerreTNot-So-CLEVR: Learning same-different relations strains feedforward neural networksInterface Focus201882018001110.1098/rsfs.2018.0011 Nguembang FadjaARiguzziFLifted discriminative learning of probabilistic logic programsMachine Learning2019108711111135395999310.1007/s10994-018-5750-01493.68310 De Raedt, L., Frasconi, P., Kersting, K., & Muggleton, S. H. (Eds.) (2008). Probabilistic inductive logic programming—theory and applications. Lecture Notes in Computer Science (Vol. 4911). Berlin: Springer. ManhaeveRDumančićSKimmigADemeesterTDe RaedtLNeural probabilistic logic programming in DeepProbLogArtificial Intelligence2021298103504424460810.1016/j.artint.2021.10350407418672 Rocktäschel, T., & Riedel, S. (2017). End-to-end differentiable proving. In Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 30). Ruder, S. (2016). An overview of gradient descent optimization algorithms. In CoRRarXiv:1609.04747. Tsamoura, E., Hospedales, T. M., & Michael, L. (2021). Neural-symbolic integration: A compositional perspective. In Proceedings of the 35th AAAI conference on artificial intelligence (AAAI) (pp. 5051–5060). Jiang, Z., & Luo, S. (2019). Neural logic reinforcement learning. In Proceedings of the 36th international conference on machine learning (ICML) (Vol. 97, pp. 3110–3119). Law, M., Russo, A., & Broda, K. (2014). Inductive learning of answer set programs. In E. Fermé, J. Leite (Eds.), Logics in artificial intelligence—14th European Conference (JELIA). Lecture Notes in Computer Science (Vol. 8761, pp. 311–325). Donadello, I., Serafini, L., & d’Avila Garcez, A. (2017). Logic tensor networks for semantic image interpretation. In Proceedings of the 26th international joint conference on artificial intelligence (IJCAI) (pp. 1596–1602). Sen, P., Carvalho, B. W. S. R. D., Riegel, R., & Gray, A. (2022). Neuro-symbolic inductive logic programming with logical neural networks. In Proceedings of the AAAI conference on artificial intelligence (AAAI) (Vol. 36, No 8, pp. 8212–8219). Shindo, H., Nishino, M., & Yamamoto, A. (2021). Differentiable inductive logic programming for structured examples. In Proceedings of the 35th AAAI conference on artificial intelligence (AAAI) (pp. 5034–5041). Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015). Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision (ICCV). van KriekenEAcarEvan HarmelenFAnalyzing differentiable fuzzy logic operatorsArtificial Intelligence2022302103602432734410.1016/j.artint.2021.1036021490.68233 Bošnjak, M., Rocktäschel, T., Naradowsky, J., & Riedel, S. (2017). Programming with a differentiable forth interpreter. In Proceedings of the 34th international conference on machine learning (ICML) (Vol. 70, pp. 547–556). CropperAMorelRLearning programs by learning from failuresMachine Learning20211104801856425808210.1007/s10994-020-05934-z07432822 Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 28). Mensch, A., & Blondel, M. (2018). Differentiable dynamic programming for structured prediction and attention. In Proceedings of the 35th international conference on machine learning (ICML) (Vol. 80, pp. 3462–3471). MüllerHHolzingerAKandinsky patternsArtificial Intelligence2021300103546427559810.1016/j.artint.2021.10354607418690 De Raedt, L., Kersting, K., Natarajan, S., & Poole, D. (2016). Statistical relational artificial intelligence: Logic, probability, and computation. In Synthesis lectures on artificial intelligence and machine learning (Vol. 32). San Rafael, CA: Morgan & Claypool. Mao, J., Gan, C., Kohli, P., Tenenbaum, J. B., & Wu, J. (2019). The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. In Proceedings of the 7th international conference on learning representations (ICLR). BadreddineSd’Avila GarcezASerafiniLSprangerMLogic tensor networksArtificial Intelligence2022303103649435321810.1016/j.artint.2021.10364907482899 CropperADumancicSEvansRMuggletonSHInductive logic programming at 30Machine Learning20221111147172437584810.1007/s10994-021-06089-107510309 ShapiroEYAlgorithmic program debugging1983CambridgeMIT Press0589.68003 Dittadi, A., Papa, S., De Vita, M., Schölkopf, B., Winther, O., & Locatello, F. (2022). Generalization and robustness implications in object-centric learning. In Proceedings of the 39th international conference on machine learning (ICML). MuggletonSInverse Entailment and ProgolNew Generation Computing, Special issue on Inductive Logic Programming1995133–424528610.1007/BF03037227 Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M. M., & Lerchner, A. (2019). Monet: Unsupervised scene decomposition and representation. CoRR arXiv:1901.11390. Plotkin, G. (1971). A further note on inductive F Locatello (6320_CR37) 2020; 33 6320_CR70 MM Bongard (6320_CR6) 1970 EY Shapiro (6320_CR63) 1983 6320_CR72 H Kautz (6320_CR30) 2022; 43 6320_CR35 6320_CR34 6320_CR36 6320_CR74 E Bellodi (6320_CR4) 2015; 15 6320_CR32 W Nie (6320_CR48) 2020; 33 6320_CR38 6320_CR1 6320_CR2 6320_CR5 6320_CR7 6320_CR8 6320_CR9 6320_CR40 J Kim (6320_CR31) 2018; 8 SH Muggleton (6320_CR43) 1991; 8 JR Quinlan (6320_CR52) 1990; 5 S Russell (6320_CR60) 2009 6320_CR47 6320_CR42 6320_CR41 RA Kowalski (6320_CR33) 1988; 31 F Petersen (6320_CR49) 2021; 34 S Muggleton (6320_CR44) 1995; 13 A Cropper (6320_CR11) 2021; 110 R Evans (6320_CR22) 2018; 61 A Cropper (6320_CR12) 2019; 109 6320_CR51 6320_CR50 6320_CR13 6320_CR57 6320_CR56 6320_CR15 6320_CR59 6320_CR14 6320_CR58 6320_CR53 6320_CR55 6320_CR54 6320_CR17 6320_CR16 6320_CR19 G Sourek (6320_CR68) 2018; 62 A Nguembang Fadja (6320_CR46) 2019; 108 H Müller (6320_CR45) 2021; 300 6320_CR62 6320_CR61 Y Xie (6320_CR73) 2020; 33 M Diligenti (6320_CR18) 2017; 244 6320_CR24 R Manhaeve (6320_CR39) 2021; 298 6320_CR23 6320_CR67 6320_CR26 6320_CR25 6320_CR69 6320_CR20 6320_CR64 S Badreddine (6320_CR3) 2022; 303 6320_CR66 6320_CR21 6320_CR65 E van Krieken (6320_CR71) 2022; 302 A Cropper (6320_CR10) 2022; 111 6320_CR28 6320_CR27 6320_CR29 |
| References_xml | – reference: Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M. M., & Lerchner, A. (2019). Monet: Unsupervised scene decomposition and representation. CoRR arXiv:1901.11390. – reference: Goyal, K., Neubig, G., Dyer, C., & Berg-Kirkpatrick, T. (2018). A continuous relaxation of beam search for end-to-end training of neural sequence models. In Proceedings of the 32th AAAI conference on artificial intelligence (AAAI) (Vol. 32, No 1). – reference: XieYDaiHChenMDaiBZhaoTZhaHWeiWPfisterTDifferentiable top-k with optimal transportProceedings of the Advances in Neural Information Processing Systems (NeurIPS)2020332052020531 – reference: CropperAMorelRMuggletonSLearning higher-order logic programsMachine Learning201910912891322412506010.1007/s10994-019-05862-707255749 – reference: CropperADumancicSEvansRMuggletonSHInductive logic programming at 30Machine Learning20221111147172437584810.1007/s10994-021-06089-107510309 – reference: Stammer, W., Schramowski, P., & Kersting, K. (2021). Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3619–3629). – reference: BellodiERiguzziFStructure learning of probabilistic logic programs by searching the clause spaceTheory and Practice of Logic Programming201515216921210.1017/S14710684130006891379.68269 – reference: De Raedt, L., Kersting, K., Natarajan, S., & Poole, D. (2016). Statistical relational artificial intelligence: Logic, probability, and computation. In Synthesis lectures on artificial intelligence and machine learning (Vol. 32). San Rafael, CA: Morgan & Claypool. – reference: MuggletonSInverse Entailment and ProgolNew Generation Computing, Special issue on Inductive Logic Programming1995133–424528610.1007/BF03037227 – reference: Sourek, G., Svatos, M., Zelezný, F., Schockaert, S., & Kuzelka, O. (2017). Stacked structure learning for lifted relational neural networks. In N. Lachiche, C. Vrain (Eds.), Proceedings of the 27th international conference on inductive logic programming. Lecture notes in computer science (Vol. 10759, pp. 140–151). – reference: d’Avila Garcez, A., & Lamb, L. C. (2020). Neurosymbolic AI: The 3rd wave. In CoRRarXiv:2012.05876. – reference: Raedt, L. d., Dumančić, S., Manhaeve, R., & Marra, G. (2020). From statistical relational to neuro-symbolic artificial intelligence. In Proceedings of the 29th international joint conference on artificial intelligence (IJCAI) (pp. 4943–4950). – reference: Rocktäschel, T., & Riedel, S. (2017). End-to-end differentiable proving. In Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 30). – reference: Holzinger, A., Saranti, A., & Müller, H. (2021). Kandinsky patterns: An experimental exploration environment for pattern analysis and machine intelligence. In CoRRarXiv:2103.00519. – reference: BadreddineSd’Avila GarcezASerafiniLSprangerMLogic tensor networksArtificial Intelligence2022303103649435321810.1016/j.artint.2021.10364907482899 – reference: Law, M., Russo, A., & Broda, K. (2014). Inductive learning of answer set programs. In E. Fermé, J. Leite (Eds.), Logics in artificial intelligence—14th European Conference (JELIA). Lecture Notes in Computer Science (Vol. 8761, pp. 311–325). – reference: Ray, O., & Inoue, K. (2007). Mode-directed inverse entailment for full clausal theories. In Proceedings of the 17th international conference on inductive logic programming (ILP). Lecture notes in computer science (Vol. 4894, pp. 225–238). – reference: He, K., Gkioxari, G., Dollár, P., & Girshick, R. B. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (ICCV) (pp. 2980–2988). – reference: SourekGAschenbrennerVZeleznýFSchockaertSKuzelkaOLifted relational neural networks: Efficient learning of latent relational structuresJournal of Artificial Intelligence Research20186269100381750110.1613/jair.1.112031444.68163 – reference: Sen, P., Carvalho, B. W. S. R. D., Riegel, R., & Gray, A. (2022). Neuro-symbolic inductive logic programming with logical neural networks. In Proceedings of the AAAI conference on artificial intelligence (AAAI) (Vol. 36, No 8, pp. 8212–8219). – reference: LocatelloFWeissenbornDUnterthinerTMahendranAHeigoldGUszkoreitJDosovitskiyAKipfTObject-centric learning with slot attentionProceedings of the Advances in Neural Information Processing Systems (NeurIPS)2020331152511538 – reference: Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In Proceedings of the 3rd international conference on learning representation (ICLR). – reference: Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., & Teh, Y. W. (2019). Set transformer: A framework for attention-based permutation-invariant neural networks. In Proceedings of the 36th international conference on machine learning (ICML) (Vol. 97, pp. 3744–3753). – reference: Ross, A. S., Hughes, M. C., & Doshi-Velez, F. (2017). Right for the right reasons: Training differentiable models by constraining their explanations. In Proceedings of the 26 international joint conference on artificial intelligence (IJCAI) (pp. 2662–2670). – reference: MüllerHHolzingerAKandinsky patternsArtificial Intelligence2021300103546427559810.1016/j.artint.2021.10354607418690 – reference: CropperAMorelRLearning programs by learning from failuresMachine Learning20211104801856425808210.1007/s10994-020-05934-z07432822 – reference: Holzinger, A., Kickmeier-Rust, M., & Müller, H. (2019). Kandinsky patterns as IQ-test for machine learning. In Proceedings of the 3rd international cross-domain conference for machine learning and knowledge extraction (CD-MAKE) (pp. 1–14). – reference: KimJRicciMSerreTNot-So-CLEVR: Learning same-different relations strains feedforward neural networksInterface Focus201882018001110.1098/rsfs.2018.0011 – reference: KautzHThe third AI summer: AAAI Robert S. Engelmore memorial lectureAI Magazine20224319310410.1609/aimag.v43i1.19122 – reference: QuinlanJRLearning logical definitions from relationsMachine Learning1990523926610.1007/BF00117105 – reference: Mao, J., Gan, C., Kohli, P., Tenenbaum, J. B., & Wu, J. (2019). The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. In Proceedings of the 7th international conference on learning representations (ICLR). – reference: KowalskiRAThe early years of logic programmingCommunications of the ACM1988311384384141510.1145/35043.35046 – reference: Lloyd, J. W. (1984). Foundations of logic programming. – reference: Yang, Z., Ishay, A., & Lee, J. (2020). Neurasp: Embracing neural networks into answer set programming. In Proceedings of the 29th international joint conference on artificial intelligence (IJCAI) (pp. 1755–1762). – reference: DiligentiMGoriMSaccàCSemantic-based regularization for learning and inferenceArtificial Intelligence2017244143165360599510.1016/j.artint.2015.08.0111404.68100 – reference: Vedantam, R., Szlam, A., Nickel, M., Morcos, A., & Lake, B. M. (2021). Curi: A benchmark for productive concept learning under uncertainty. In Proceedings of the 38th international conference on machine learning (ICML) (Vol. 139, pp. 10519–10529). – reference: Tsamoura, E., Hospedales, T. M., & Michael, L. (2021). Neural-symbolic integration: A compositional perspective. In Proceedings of the 35th AAAI conference on artificial intelligence (AAAI) (pp. 5051–5060). – reference: Pietruszka, M., Borchmann, L., & Gralinski, F. (2021). Successive halving top-k operator. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI) (Vol. 35, No. 18, pp. 15869–15870). – reference: ManhaeveRDumančićSKimmigADemeesterTDe RaedtLNeural probabilistic logic programming in DeepProbLogArtificial Intelligence2021298103504424460810.1016/j.artint.2021.10350407418672 – reference: RussellSNorvigPArtificial intelligence: A modern approach20093Hoboken, NJPrentice Hall Press0835.68093 – reference: Ruder, S. (2016). An overview of gradient descent optimization algorithms. In CoRRarXiv:1609.04747. – reference: Cuturi, M., & Blondel, M. (2017). Soft-DTW: A differentiable loss function for time-series. In Proceedings of the 34th international conference on machine learning (ICML) (Vol. 70, pp. 894–903). – reference: Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015). Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision (ICCV). – reference: Dai, W.-Z., Xu, Q., Yu, Y., & Zhou, Z.-H. (2019). Bridging machine learning and logical reasoning by abductive learning. In Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 32). – reference: van KriekenEAcarEvan HarmelenFAnalyzing differentiable fuzzy logic operatorsArtificial Intelligence2022302103602432734410.1016/j.artint.2021.1036021490.68233 – reference: BongardMMHawkinsJKPattern recognition1970New YorkSpartan Books0205.21201 – reference: NieWYuZMaoLPatelABZhuYAnandkumarABongard-logo: A new benchmark for human-level concept learning and reasoningProceedings of the Advances in Neural Information Processing Systems (NeurIPS)2020331646816480 – reference: Si, X., Raghothaman, M., Heo, K., & Naik, M. (2019). Synthesizing datalog programs using numerical relaxation. In Proceedings of the 28th international joint conference on artificial intelligence (IJCAI) (pp. 6117–6124). – reference: Solar-Lezama, A. (2008). Program synthesis by sketching. Ph.D. Thesis. – reference: Donadello, I., Serafini, L., & d’Avila Garcez, A. (2017). Logic tensor networks for semantic image interpretation. In Proceedings of the 26th international joint conference on artificial intelligence (IJCAI) (pp. 1596–1602). – reference: Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. B. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1988–1997). – reference: Shindo, H., Nishino, M., & Yamamoto, A. (2021). Differentiable inductive logic programming for structured examples. In Proceedings of the 35th AAAI conference on artificial intelligence (AAAI) (pp. 5034–5041). – reference: MuggletonSHInductive logic programmingNew Generation Computing19918429531810.1007/BF030370890712.68022 – reference: Nienhuys-Cheng, S.-H., Wolf, R. D., Siekmann, J., & Carbonell, J. G. (1997). Foundations of inductive logic programming. – reference: He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 770–778). – reference: EvansRGrefenstetteELearning explanatory rules from noisy dataJournal of Artificial Intelligence Research201861164376619810.1613/jair.57141426.68235 – reference: Jiang, Z., & Luo, S. (2019). Neural logic reinforcement learning. In Proceedings of the 36th international conference on machine learning (ICML) (Vol. 97, pp. 3110–3119). – reference: Nguembang FadjaARiguzziFLifted discriminative learning of probabilistic logic programsMachine Learning2019108711111135395999310.1007/s10994-018-5750-01493.68310 – reference: Cropper, A., & Muggleton, S. H. (2016). Metagol system. https://github.com/metagol/metagol. – reference: Dittadi, A., Papa, S., De Vita, M., Schölkopf, B., Winther, O., & Locatello, F. (2022). Generalization and robustness implications in object-centric learning. In Proceedings of the 39th international conference on machine learning (ICML). – reference: PetersenFBorgeltCKuehneHDeussenOLearning with algorithmic supervision via continuous relaxationsProceedings of the Advances in Neural Information Processing Systems (NeurIPS)2021341652016531 – reference: Redmon, J., Divvala, S. K., Girshick, R. B., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 779–788). – reference: Bošnjak, M., Rocktäschel, T., Naradowsky, J., & Riedel, S. (2017). Programming with a differentiable forth interpreter. In Proceedings of the 34th international conference on machine learning (ICML) (Vol. 70, pp. 547–556). – reference: Engelcke, M., Kosiorek, A. R., Jones, O. P., & Posner, I. (2020). Genesis: Generative scene inference and sampling with object-centric latent representations. In Proceedings of the 8th international conference on learning representations (ICLR). – reference: Minervini, P., Riedel, S., Stenetorp, P., Grefenstette, E., & Rocktäschel, T. (2020). Learning reasoning strategies in end-to-end differentiable proving. In Proceedings of the 37th international conference on machine learning (ICML). – reference: Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., & De Raedt, L. (2018). Deepproblog: Neural probabilistic logic programming. In Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 31). – reference: Amizadeh, S., Palangi, H., Polozov, A., Huang, Y., & Koishida, K. (2020). Neuro-symbolic visual reasoning: Disentangling visual from reasoning. Proceedings of the 37th international conference on machine learning (ICML) (Vol. 119, pp. 279–290). – reference: Plotkin, G. (1971). A further note on inductive generalization. In Machine intelligence (Vol. 6). – reference: Mensch, A., & Blondel, M. (2018). Differentiable dynamic programming for structured prediction and attention. In Proceedings of the 35th international conference on machine learning (ICML) (Vol. 80, pp. 3462–3471). – reference: Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 28). – reference: Santoro, A., Faulkner, R., Raposo, D., Rae, J., Chrzanowski, M., Weber, T., Wierstra, D., Vinyals, O., Pascanu, R., & Lillicrap, T. (2018). Relational recurrent neural networks. In: Proceedings of the advances in neural information processing systems (NeurIPS) (Vol. 31). – reference: Besold, T. R., d’Avila Garcez, A. S., Bader, S., Bowman, H., Domingos, P. M., Hitzler, P., Kühnberger, K., Lamb, L. C., Lowd, D., Lima, P. M. V., de Penning, L., Pinkas, G., Poon, H., & Zaverucha, G. (2017). Neural-symbolic learning and reasoning: A survey and interpretation. In CoRRarXiv:1711.03902. – reference: De Raedt, L., Frasconi, P., Kersting, K., & Muggleton, S. H. (Eds.) (2008). Probabilistic inductive logic programming—theory and applications. Lecture Notes in Computer Science (Vol. 4911). Berlin: Springer. – reference: ShapiroEYAlgorithmic program debugging1983CambridgeMIT Press0589.68003 – ident: 6320_CR58 doi: 10.24963/ijcai.2017/371 – volume: 302 start-page: 103602 year: 2022 ident: 6320_CR71 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2021.103602 – volume: 8 start-page: 295 issue: 4 year: 1991 ident: 6320_CR43 publication-title: New Generation Computing doi: 10.1007/BF03037089 – ident: 6320_CR64 doi: 10.1609/aaai.v35i6.16637 – ident: 6320_CR40 – volume-title: Pattern recognition year: 1970 ident: 6320_CR6 – ident: 6320_CR67 doi: 10.1007/978-3-319-78090-0_10 – ident: 6320_CR28 – volume: 244 start-page: 143 year: 2017 ident: 6320_CR18 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2015.08.011 – ident: 6320_CR21 – volume: 5 start-page: 239 year: 1990 ident: 6320_CR52 publication-title: Machine Learning doi: 10.1007/BF00117105 – ident: 6320_CR74 doi: 10.24963/ijcai.2020/243 – ident: 6320_CR36 doi: 10.1007/978-3-642-96826-6 – volume: 33 start-page: 20520 year: 2020 ident: 6320_CR73 publication-title: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) – ident: 6320_CR14 – volume: 43 start-page: 93 issue: 1 year: 2022 ident: 6320_CR30 publication-title: AI Magazine doi: 10.1609/aimag.v43i1.19122 – ident: 6320_CR53 doi: 10.24963/ijcai.2020/688 – volume: 8 start-page: 20180011 year: 2018 ident: 6320_CR31 publication-title: Interface Focus doi: 10.1098/rsfs.2018.0011 – ident: 6320_CR35 – volume: 303 start-page: 103649 year: 2022 ident: 6320_CR3 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2021.103649 – ident: 6320_CR47 doi: 10.1007/3-540-62927-0 – ident: 6320_CR7 – ident: 6320_CR41 – ident: 6320_CR66 – volume: 298 start-page: 103504 year: 2021 ident: 6320_CR39 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2021.103504 – volume: 111 start-page: 147 issue: 1 year: 2022 ident: 6320_CR10 publication-title: Machine Learning doi: 10.1007/s10994-021-06089-1 – ident: 6320_CR27 – ident: 6320_CR72 – volume: 300 start-page: 103546 year: 2021 ident: 6320_CR45 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2021.103546 – volume: 33 start-page: 16468 year: 2020 ident: 6320_CR48 publication-title: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) – volume-title: Artificial intelligence: A modern approach year: 2009 ident: 6320_CR60 – ident: 6320_CR24 doi: 10.1109/ICCV.2017.322 – volume: 33 start-page: 11525 year: 2020 ident: 6320_CR37 publication-title: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) – ident: 6320_CR38 – ident: 6320_CR51 – volume: 34 start-page: 16520 year: 2021 ident: 6320_CR49 publication-title: Proceedings of the Advances in Neural Information Processing Systems (NeurIPS) – ident: 6320_CR16 doi: 10.1007/978-3-540-78652-8 – ident: 6320_CR59 – ident: 6320_CR13 – volume: 13 start-page: 245 issue: 3–4 year: 1995 ident: 6320_CR44 publication-title: New Generation Computing, Special issue on Inductive Logic Programming doi: 10.1007/BF03037227 – ident: 6320_CR69 doi: 10.1109/CVPR46437.2021.00362 – ident: 6320_CR26 doi: 10.1007/978-3-030-29726-8_1 – volume: 31 start-page: 38 issue: 1 year: 1988 ident: 6320_CR33 publication-title: Communications of the ACM doi: 10.1145/35043.35046 – volume-title: Algorithmic program debugging year: 1983 ident: 6320_CR63 doi: 10.7551/mitpress/1192.001.0001 – ident: 6320_CR8 – ident: 6320_CR55 doi: 10.1109/CVPR.2016.91 – ident: 6320_CR23 doi: 10.1609/aaai.v32i1.11806 – ident: 6320_CR50 doi: 10.1609/aaai.v35i18.17931 – ident: 6320_CR61 – ident: 6320_CR1 – volume: 109 start-page: 1289 year: 2019 ident: 6320_CR12 publication-title: Machine Learning doi: 10.1007/s10994-019-05862-7 – ident: 6320_CR42 doi: 10.3233/FAIA210359 – ident: 6320_CR25 doi: 10.1109/CVPR.2016.90 – ident: 6320_CR2 doi: 10.1109/ICCV.2015.279 – volume: 61 start-page: 1 year: 2018 ident: 6320_CR22 publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.5714 – ident: 6320_CR56 – ident: 6320_CR20 doi: 10.24963/ijcai.2017/221 – ident: 6320_CR9 – ident: 6320_CR34 doi: 10.1007/978-3-319-11558-0_22 – volume: 108 start-page: 1111 issue: 7 year: 2019 ident: 6320_CR46 publication-title: Machine Learning doi: 10.1007/s10994-018-5750-0 – ident: 6320_CR5 – volume: 15 start-page: 169 issue: 2 year: 2015 ident: 6320_CR4 publication-title: Theory and Practice of Logic Programming doi: 10.1017/S1471068413000689 – volume: 110 start-page: 801 issue: 4 year: 2021 ident: 6320_CR11 publication-title: Machine Learning doi: 10.1007/s10994-020-05934-z – volume: 62 start-page: 69 year: 2018 ident: 6320_CR68 publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.1.11203 – ident: 6320_CR15 – ident: 6320_CR29 doi: 10.1109/CVPR.2017.215 – ident: 6320_CR62 doi: 10.1609/aaai.v36i8.20795 – ident: 6320_CR65 doi: 10.24963/ijcai.2019/847 – ident: 6320_CR32 – ident: 6320_CR57 – ident: 6320_CR17 doi: 10.1007/978-3-031-01574-8 – ident: 6320_CR54 doi: 10.1007/978-3-540-78469-2_23 – ident: 6320_CR19 – ident: 6320_CR70 doi: 10.1609/aaai.v35i6.16639 |
| SSID | ssj0002686 |
| Score | 2.567561 |
| Snippet | Deep neural learning has shown remarkable performance at learning representations for visual object categorization. However, deep neural networks such as CNNs... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1465 |
| SubjectTerms | Artificial Intelligence Artificial neural networks Computer Science Confounding (Statistics) Control Logic programming Logic programs Machine Learning Mechatronics Natural Language Processing (NLP) Neural networks Robotics Simulation and Modeling Special Issue on Learning and Reasoning 2022 Visual perception |
| SummonAdditionalLinks | – databaseName: Computer Science Database dbid: K7- link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1NSwMxEB20evBi_cRqlRy8abDJZnezHhQRi6KUHhR6W5LsBgulrd22_8s_4m8ySbNdFOzFczYh5M28ySaZeQDnStvrpoxhRp2EmRI4kaHExPiSkgGzgcGJTcSdDu_1kq4_cCv8s8qSEx1RZyNlz8ivKDd7mYiZcHI7_sBWNcrernoJjXXYIJQSa-fPMV4yMY2c0qNxpBDbSO6TZnzqnCuKS622gc0iJj8DU7Xb_HVB6uJOu_7fGe_Att9xoruFiezCWj7cg3qp5oC8c-_Dzdfn00v3Gk3fF3IKaN4vZqajLfeUF0gUqBRTMaQgBzlyrIn8A6_iAN7aD6_3j9irK2BFY04wJzrhBpyWplGeaEkF0ZKZtQoEl5qHSraYjHhOgkiwJFOZ-bOIEhFnLGe6pZPgEGrD0TA_AiSCQKhYaNNiiNdwII1CM4i0CnmatIIGkHJpU-VLj1sFjEFaFU22cKQGjtTBkZIGXCz7jBeFN1Z-3SwxSL0TFmkFQAMuSxSr5r9HO1492glsUWc49tljE2rTySw_hU01n_aLyZkzwW9LyN9m priority: 102 providerName: ProQuest |
| Title | αILP: thinking visual scenes as differentiable logic programs |
| URI | https://link.springer.com/article/10.1007/s10994-023-06320-1 https://www.proquest.com/docview/2809964661 |
| Volume | 112 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1573-0565 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0002686 issn: 0885-6125 databaseCode: P5Z dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1573-0565 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0002686 issn: 0885-6125 databaseCode: K7- dateStart: 20230101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central Database Suite (ProQuest) customDbUrl: eissn: 1573-0565 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0002686 issn: 0885-6125 databaseCode: BENPR dateStart: 20230101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 1573-0565 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0002686 issn: 0885-6125 databaseCode: M2P dateStart: 20230101 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1573-0565 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002686 issn: 0885-6125 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LSwMxEB60evBifWK1lhy86UI3m93NehBUWhS1LPVB8bIk6QYLpUq37f_yj_ibnKS7rYoKesklyRBmMo-QmfkADpQ2301d5jBqIcyUcCLpS8dFXVLSY8YxWLCJsNXinU4U50VhWZHtXnxJWkv9odjNtrGlBo3A1P3im2cJ3R036ti-fZjZXxpYfEdUH98x_jsvlfmexmd3NI8xv3yLWm_TLP_vnGuwmkeX5HR6HdZhIR1sQLlAbiC5Im_Cydvr5XV8TEZPU-gEMullY9xoWjulGREZKYBT0ADIfkqshSR5Mle2BffNxt35hZMjKTiKhtx1uKsjjoKoaxqkkZZUuFoy5JAnuNTcV7LOZMBT1wsEi7qqi6-IIBJhl6VM13XkbUNp8DxId4AIzxMqFBpn0MiivaOBj0SkQcPTbt2rgFswNFF5m3GDdtFP5g2SDYMSZFBiGZS4FTic7XmZNtn4dXW1kFOSK1yWUI7LAobRRgWOCrnMp3-mtvu35XuwQq1oTcpjFUqj4Tjdh2U1GfWyYQ2WzhqtuF2DxavQwfGGxjjG_mPNXtF3ZxPZ4Q |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1NTxsxEB2hgNRemtIPEQjFh_bUWo29zq4XCaqqEBEljThQKbet7bXVSFECbBLEn0Lij_CbGDu7RK1Ubhw4ez3a3Rm_mV173gP4aJzfbsoFFTxImBlFU93WlOFaMjoSPjEEsYlkMJDDYXq6BjdVL4w_VllhYgDqfGr8P_KvXGItEwtMJ9_OL6hXjfK7q5WExjIsevb6Cj_ZioPuEfr3E-ed47MfJ7RUFaCGJ5JRyVwq8aZajsc2dZor5rTAxRYpqZ1sG90SOpaWRbESaW5yrKjjVCW5sMK1nCdfQshfF5FMPFd_L6EPyM_joCyJttrUVw5lk07ZqhdIeLnXUvBdy-zvRLiqbv_ZkA15rlN_bm_oNbwqK2ryfbkENmHNTt5AvVKrICV4vYXDu9tu_3SfzP4s5SLIYlTMcaKns7IFUQWpxGIQ9PTYkpAVSHmArXgHv57kKd5DbTKd2C0gKoqUSZTDEUwsiPE8bqMR7RUAHWtFDWCVKzNTUqt7hY9xtiKF9u7P0P1ZcH_GGvD5Yc75kljk0aublc-zEmSKbOXwBnypomY1_H9r249b24MXJ2c_-1m_O-jtwEsegtYf8WxCbXY5t7uwYRazUXH5IYQ_gd9PHU33THY8Ww |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NThsxEB4hqFAvpEArArT4QE9gEXu9f0hQVaURUVCUA0jctrbXFkgoCWwI4rG49iH6TIwdL1GR4MaBs9ej3Z1vfnY9Mx_AtrbuuKkUVHBPYaYlzVWsKENb0ioSLjB4som018vOz_P-HPyte2FcWWXtE72jLofa_SPf4xnmMonAcLJnQ1lE_6j9Y3RNHYOUO2mt6TSmEOma-zv8fKsOOkeo6--ct3-f_jqmgWGAap5mjGbM5hneYMvyxORWccmsEmh4kcyUzWKtWkIlmWFRIkVe6hKz6ySXaSmMsC3rBjGh-1_AKBw7G-um9CkK8MSzTKKsmLosIjTshLY9P5CXO14F18HM_g-Ks0z32eGsj3ntxnt-W59gKWTa5OfUNJZhzgxWoFGzWJDg1Fbh8N9D56S_T8YXUxoJMrmsbnGjG3NlKiIrUpPIoDNUV4b4aEFCYVv1Gc7e5Cm-wPxgODBrQGQUSZ1KiysYcND38yRGIcoxA1rWiprAarUWOoxcd8wfV8VsWLSDQoFQKDwUCtaEnac9o-nAkVev3qz1XwTnUxUz5Tdht0bQbPllaeuvS9uCRQRRcdLpdTfgI_f4dZWfmzA_vrk1X-GDnowvq5tv3hII_HlrMD0Cc5ZFFQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=alpha%24%24ILP%3A+thinking+visual+scenes+as+differentiable+logic+programs&rft.jtitle=Machine+learning&rft.au=Shindo%2C+Hikaru&rft.au=Pfanschilling%2C+Viktor&rft.au=Dhami%2C+Devendra+Singh&rft.au=Kersting%2C+Kristian&rft.date=2023-05-01&rft.issn=0885-6125&rft.eissn=1573-0565&rft.volume=112&rft.issue=5&rft.spage=1465&rft.epage=1497&rft_id=info:doi/10.1007%2Fs10994-023-06320-1&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10994_023_06320_1 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0885-6125&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0885-6125&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0885-6125&client=summon |