Search Results - Computing methodologies Machine learning Machine learning approaches

PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning by Roy, Rajarshi, Raiman, Jonathan, Kant, Neel, Elkin, Ilyas, Kirby, Robert, Siu, Michael, Oberman, Stuart, Godil, Saad, Catanzaro, Bryan

“…In this work, we present a reinforcement learning (RL) based approach to designing parallel prefix circuits such as adders or priority encoders that are fundamental to high-performance digital design…”
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UniGenCoder: Merging SEQ2SEQ and SEQ2TREE Paradigms for Unified Code Generation by Shao, Liangying, Yan, Yanfu, Poshyvanyk, Denys, Su, Jinsong

“…Deep learning-based code generation has completely transformed the way developers write programs today…”
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Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software by Chen, Zhenpeng, Li, Xinyue, Zhang, Jie M., Sarro, Federica, Liu, Yang

“…Intersectional fairness is a critical requirement for Machine Learning (ML) software, demanding fairness across subgroups defined by multiple protected attributes…”
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An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing by Nunes, Igor, Heddes, Mike, Givargis, Tony, Nicolau, Alexandru

“…Hyperdimensional Computing (HDC) is a computation framework based on random vector spaces, particularly useful for machine learning in resource-constrained environments…”
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Code Difference Guided Adversarial Example Generation for Deep Code Models by Tian, Zhao, Chen, Junjie, Jin, Zhi

“…Adversarial examples are important to test and enhance the robustness of deep code models. As source code is discrete and has to strictly stick to complex…”
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IFHE: Intermediate-Feature Heterogeneity Enhancement for Image Synthesis in Data-Free Knowledge Distillation by Chen, Yi, Liu, Ning, Ren, Ao, Yang, Tao, Liu, Duo

“…Data-free knowledge distillation (DFKD) explores training a compact student network only by a pre-trained teacher without real data. Prevailing DFKD methods…”
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PertNAS: Architectural Perturbations for Memory-Efficient Neural Architecture Search by Ahmad, Afzal, Xie, Zhiyao, Zhang, Wei

“… This leads to GPU-memory bottlenecks that hamper the algorithm's scalability. To resolve these bottlenecks, we propose a perturbations-based evolutionary approach…”
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Enabling On-Device Self-Supervised Contrastive Learning with Selective Data Contrast by Wu, Yawen, Wang, Zhepeng, Zeng, Dewen, Shi, Yiyu, Hu, Jingtong

“… Contrastive learning has demonstrated its great potential in learning from unlabeled data. However, the online input data are usually none independent and identically distributed (non-iid…”
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QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learning by Sanchez-Stern, Alex, Varghese, Abhishek, Kaufman, Zhanna, Zhang, Dylan, Ringer, Talia, Brun, Yuriy

“… To address this problem, we create QEDCartographer, an automated proofsynthesis tool that combines supervised and reinforcement learning to more effectively explore the proof space…”
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DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification by Wang, Junyao, Huang, Sitao, Imani, Mohsen

“… Many applications exploit machine learning methodology to dissect collected data, and edge computing was introduced to enhance the efficiency and scalability in resource-constrained computing environments…”
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Making Fair ML Software using Trustworthy Explanation by Chakraborty, Joymallya, Peng, Kewen, Menzies, Tim

“…Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice…”
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Finding Ethereum Smart Contracts Security Issues by Comparing History Versions by Chen, Jiachi

“… In this paper, we propose a deep learning-based method to find security issues of Ethereum smart contracts by finding the updated version of a destructed contract…”
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GENESYS: A novel evolutionary program synthesis tool with continuous optimization by Mandal, Shantanu, Anderson, Todd A, Turek, Javier, Gottschlich, Justin, Muzahid, Abdullah

“… Most existing approaches formulate program synthesis as a search problem with discrete parameters…”
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MAGIKA: AI-Powered Content-Type Detection by Fratantonio, Yanick, Invernizzi, Luca, Farah, Loua, Thomas, Kurt, Zhang, Marina, Albertini, Ange, Galilee, Francois, Metitieri, Giancarlo, Cretin, Julien, Petit-Bianco, Alex, Tao, David, Bursztein, Elie

“… Under the hood, Magika employs a deep learning model that can execute on a single CPU with just 1MB of memory to store the model's weights…”
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FactorHD: A Hyperdimensional Computing Model for Multi-Object Multi-Class Representation and Factorization by Zhou, Yifei, Huang, Xuchu, Ni, Chenyu, Zhou, Min, Yan, Zheyu, Yin, Xunzhao, Zhuo, Cheng

“… Hyperdimensional Computing (HDC), a promising braininspired computational model, is integral to neuro-symbolic AI…”
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CAE-DFKD: Bridging the Transferability Gap in Data-Free Knowledge Distillation by Zhang, Zherui, Wang, Changwei, Xu, Rongtao, Xu, Wenhao, Xu, Shibiao, Zhang, Yu, Zhou, Jie, Guo, Li

“…Data-Free Knowledge Distillation (DFKD) enables the knowledge transfer from the given pre-trained teacher network to the target student model without access to…”
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The Devil is in the Tails: How Long-Tailed Code Distributions Impact Large Language Models by Zhout, Xin, Kim, Kisub, Xu, Bowen, Liu, Jiakun, Han, DongGyun, Lo, David

“…Learning-based techniques, especially advanced Large Language Models (LLMs) for code, have gained considerable popularity in various software engineering (SE) tasks…”
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Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning by Wang, Liang, Lu, Kai, Zhang, Nan, Qu, Xiaoyang, Wang, Jianzong, Wan, Jiguang, Li, Guokuan, Xiao, Jing

“… At the edge, we design adaptive training using small batches to adapt models under limited computing power, and adaptive sampling of training frames for robustness and reducing bandwidth…”
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Iterative Generation of Adversarial Example for Deep Code Models by Huang, Li, Sun, Weifeng, Yan, Meng

“…Deep code models are vulnerable to adversarial attacks, making it possible for semantically identical inputs to trigger different responses. Current black-box…”
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RLCoder: Reinforcement Learning for Repository-Level Code Completion by Wang, Yanlin, Wang, Yanli, Guo, Daya, Chen, Jiachi, Zhang, Ruikai, Ma, Yuchi, Zheng, Zibin

“… Existing approaches mainly rely on retrievalaugmented generation strategies due to limitations in input sequence length…”
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