Search Results - learning with errors (problem OR (problemsys OR problemsys))

Learning from Errors by Metcalfe, Janet

“…Although error avoidance during learning appears to be the rule in American classrooms, laboratory studies suggest that it may be a counterproductive strategy, at least for neurologically typical students…”
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Learning about structural errors in models of complex dynamical systems by Wu, Jin-Long, Levine, Matthew E., Schneider, Tapio, Stuart, Andrew

“… Building on such closure models and correcting them through learning the structural errors can be an effective way of fusing data with domain knowledge…”
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Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning by Allen, Kelsey R, Smith, Kevin A, Tenenbaum, Joshua B

“… But human beings remain distinctive in their capacity for flexible, creative tool use-using objects in new ways to act on the world, achieve a goal, or solve a problem…”
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Noise resilient quantum learning: Adaptive policy guided error mitigation in quantum reinforcement learning for the traveling salesman problem by Majid, Bisma, Sofi, Shabir Ahmed, Jabeen, Zamrooda

“…Quantum Reinforcement Learning (QRL) has emerged as a promising paradigm for solving combinatorial optimization problems by leveraging quantum parallelism and interference to enhance learning efficiency…”
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Post-Quantum Key Exchange for the TLS Protocol from the Ring Learning with Errors Problem by Bos, Joppe W., Costello, Craig, Naehrig, Michael, Stebila, Douglas

“…) protocol that provide key exchange based on the ring learning with errors (R-LWE) problem, we accompany these cipher suites with a rigorous proof of security…”
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Effects of self-explaining feedback on learning from problem-solving errors by Zhang, Qian, Fiorella, Logan

“…•Modifying the layout of the feedback did not affect learning. We tested two potential ways to help students learn from feedback on their problem-solving errors in physics…”
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Learning and correcting non-Gaussian model errors by Smyl, Danny, Tallman, Tyler N., Black, Jonathan A., Hauptmann, Andreas, Liu, Dong

“… In this work, we address this challenge by proposing a neural network approach capable of accurately approximating and compensating for such modeling errors in augmented direct and inverse problems…”
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Generalization of machine learning for problem reduction: a case study on travelling salesman problems by Sun, Yuan, Ernst, Andreas, Li, Xiaodong, Weiner, Jake

“… In this paper, we examine the generalization capability of a machine learning model for problem reduction on the classic travelling salesman problems (TSP…”
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Learning to construct a solution for UAV path planning problem with positioning error correction by Chun, Jie, Chen, Ming, Liu, Xiaolu, Xiang, Shang, Du, Yonghao, Wu, Guohua, Xing, Lining

“… This study seeks to solve the UAV path planning problem with positioning error correction (UPEC) with an end-to-end method…”
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Solving a Higgs optimization problem with quantum annealing for machine learning by Mott, Alex, Job, Joshua, Vlimant, Jean-Roch, Lidar, Daniel, Spiropulu, Maria

“… Here, Alex Mott and colleagues implement a 'signal versus background' machine learning optimization problem that can be used in the search for Higgs bosons at the Large Hadron Collider at CERN and at future colliders…”
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Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review by Chan, Jireh Yi-Le, Leow, Steven Mun Hong, Bea, Khean Thye, Cheng, Wai Khuen, Phoong, Seuk Wai, Hong, Zeng-Wei, Chen, Yen-Lin

“… Although this presents opportunities to better model the relationship between predictors and the response variables, this also causes serious problems during data analysis, one of which is the multicollinearity problem…”
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Zero-Error Tracking Control Under Unified Quantized Iterative Learning Framework via Encoding-Decoding Method by Shen, Dong, Zhang, Chao

“…This article considers the zero-error tracking problem of quantized iterative learning control for a general networked structure where the data are quantized and transmitted through limited…”
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Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems by Cao, Lianghao, O'Leary-Roseberry, Thomas, Jha, Prashant K., Oden, J. Tinsley, Ghattas, Omar

“…We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs…”
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Fostering Learning from Errors—Computer-Based Adaptivity at the Transition Between Problem Solving and Explicit Instruction by Boomgaarden, Antje, Loibl, Katharina, Leuders, Timo

“…When learners acquire new content by working on a problem-solving task prior to explicit instruction, their attempts to solve the problem usually represent only partial steps on the way to the target concept…”
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Using meta‐learning to predict performance metrics in machine learning problems by Carneiro, Davide, Guimarães, Miguel, Carvalho, Mariana, Novais, Paulo

“…Machine learning has been facing significant challenges over the last years, much of which stem from the new characteristics of machine learning problems, such as learning from streaming data…”
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Understanding error patterns in students' solutions to linear function problems to design learning interventions by Elagha, Noor, Pellegrino, James W.

“… There were two overarching aims of the reported studies. One was to assess students’ understanding of LF and discern the cognitive underpinnings of common errors they make in these types of problems…”
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Jumping to conclusions: Implications for reasoning errors, false belief, knowledge corruption, and impeded learning by Sanchez, Carmen, Dunning, David

“… In five studies, we examined whether jumping to conclusions (JTC) was similarly associated with decision impairments in a nonclinical sample, such as reasoning errors, false belief, overconfidence, and diminished learning…”
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The Learning with Errors Problem (Invited Survey) by Regev, Oded

“…In this survey we describe the Learning with Errors (LWE) problem, discuss its properties, its hardness, and its cryptographic applications…”
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Distributed Identity Authentication with Lenstra–Lenstra–Lovász Algorithm–Ciphertext Policy Attribute-Based Encryption from Lattices: An Efficient Approach Based on Ring Learning with Errors Problem by Yuan, Qi, Yuan, Hao, Zhao, Jing, Zhou, Meitong, Shao, Yue, Wang, Yanchun, Zhao, Shuo

“…In recent years, research on attribute-based encryption (ABE) has expanded into the quantum domain. Because a traditional single authority can cause the…”
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DNNRec: A novel deep learning based hybrid recommender system by R, Kiran, Kumar, Pradeep, Bhasker, Bharat

“…•A novel hybrid deep learning based recommender system ‘DNNRec’ is proposed.•DNNRec leverages embeddings, combines side information and a very deep network…”
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