Výsledky vyhledávání - Computing methodologies Machine learning Learning paradigms Supervised learning*

PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning Autor 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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PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models Autor Lee, Yunjae, Kim, Hyeseong, Rhu, Minsoo

“…Training recommendation systems (RecSys) faces several challenges as it requires the "data preprocessing" stage to preprocess an ample amount of raw data and…”
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Dissecting Global Search: A Simple Yet Effective Method to Boost Individual Discrimination Testing and Repair Autor Quan, Lili, Li, Tianlin, Xie, Xiaofei, Chen, Zhenpeng, Chen, Sen, Jiang, Lingxiao, Li, Xiaohong

“…Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns…”
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Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers Autor Stevens, Jacob R., Venkatesan, Rangharajan, Dai, Steve, Khailany, Brucek, Raghunathan, Anand

“…Transformers have transformed the field of natural language processing. Their superior performance is largely attributed to the use of stacked "self-attention"…”
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Code Difference Guided Adversarial Example Generation for Deep Code Models Autor 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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Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software Autor 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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Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models Autor Sheng, Yi, Yang, Junhuan, Yang, Lei, Shi, Yiyu, Hu, Jingtong, Jiang, Weiwen

“…Model fairness (a.k.a., bias) has become one of the most critical problems in a wide range of AI applications. An unfair model in autonomous driving may cause…”
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An Extension to Basis-Hypervectors for Learning from Circular Data in Hyperdimensional Computing Autor 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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RL-CCD: Concurrent Clock and Data Optimization using Attention-Based Self-Supervised Reinforcement Learning Autor Lu, Yi-Chen, Chan, Wei-Ting, Guo, Deyuan, Kundu, Sudipto, Khandelwal, Vishal, Lim, Sung Kyu

“… In this paper, we overcome this issue by presenting RL-CCD, a Reinforcement Learning (RL) agent that selects endpoints for useful skew prioritization using the proposed EP-GNN, an endpoint-oriented Graph Neural Network (GNN…”
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RegHD: Robust and Efficient Regression in Hyper-Dimensional Learning System Autor Hernandez-Cano, Alejandro, Zhuo, Cheng, Yin, Xunzhao, Imani, Mohsen

“…Machine learning (ML) algorithms are key enablers to effectively assimilate and extract information from many generated data in the Internet of Things…”
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SSpMV: A Sparsity-aware SpMV Framework Empowered by Multimodal Machine Learning Autor Lin, Shengle, Liu, Chubo, Ding, Yan, Zhou, Joey Tianyi, Li, Kenli, Yang, Wangdong

“…Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse operation in scientific computing and artificial intelligence…”
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Late Breaking Results: Less Sense Makes More Sense: In-Sensor Compressive Learning for Efficient Machine Vision Autor Liang, Yiwen, Cao, Weidong

“…Integrating deep learning and image sensors has significantly transformed machine vision applications…”
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UniGenCoder: Merging SEQ2SEQ and SEQ2TREE Paradigms for Unified Code Generation Autor 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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CascadeHD: Efficient Many-Class Learning Framework Using Hyperdimensional Computing Autor Kim, Yeseong, Kim, Jiseung, Imani, Mohsen

“…The brain-inspired hyperdimensional computing (HDC) gains attention as a light-weight and extremely parallelizable learning solution alternative to deep neural networks…”
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ZeroBN: Learning Compact Neural Networks For Latency-Critical Edge Systems Autor Huai, Shuo, Zhang, Lei, Liu, Di, Liu, Weichen, Subramaniam, Ravi

“…Edge devices have been widely adopted to bring deep learning applications onto low power embedded systems, mitigating the privacy and latency issues of accessing cloud servers…”
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Toward Individual Fairness Testing with Data Validity Autor Kitamura, Takashi, Amasaki, Sousuke, Inoue, Jun, Isobe, Yoshinao, Toda, Takahisa

“…Individual fairness testing (Ift) is a framework to find discriminatory instances within a given classifier. In this paper, we show our idea of a Ift…”
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Online Human Activity Recognition using Low-Power Wearable Devices Autor Bhat, Ganapati, Deb, Ranadeep, Chaurasia, Vatika Vardhan, Shill, Holly, Ogras, Umit Y.

“…Human activity recognition (HAR) has attracted significant research interest due to its applications in health monitoring and patient rehabilitation. Recent…”
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Fairquant: Certifying and Quantifying Fairness of Deep Neural Networks Autor Kim, Brian Hyeongseok, Wang, Jingbo, Wang, Chao

“…We propose a method for formally certifying and quantifying individual fairness of deep neural networks (DNN). Individual fairness guarantees that any two…”
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A Unified DNN Weight Pruning Framework Using Reweighted Optimization Methods Autor Zhang, Tianyun, Ma, Xiaolong, Zhan, Zheng, Zhou, Shanglin, Ding, Caiwen, Fardad, Makan, Wang, Yanzhi

“…To address the large model size and intensive computation requirement of deep neural networks (DNNs), weight pruning techniques have been proposed and…”
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Neurally-Inspired Hyperdimensional Classification for Efficient and Robust Biosignal Processing Autor Ni, Yang, Lesica, Nicholas, Zeng, Fan-Gang, Imani, Mohsen

“…The biosignals consist of several sensors that collect time series information. Since time series contain temporal dependencies, they are difficult to process by existing machine learning algorithms…”
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