Search Results - Computing methodologies Machine learning

Muffin: A Framework Toward Multi-Dimension AI Fairness by Uniting Off-the-Shelf Models by 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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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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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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Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers by 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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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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Toward Individual Fairness Testing with Data Validity by 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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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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PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models by 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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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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Code Prediction by Feeding Trees to Transformers by Kim, Seohyun, Zhao, Jinman, Tian, Yuchi, Chandra, Satish

“… This is where the strength of the underlying machine learning system that produces a ranked order of potential completions comes into play…”
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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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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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DARL: Distributed Reconfigurable Accelerator for Hyperdimensional Reinforcement Learning by Chen, Hanning, Issa, Mariam, Ni, Yang, Imani, Mohsen

“…) has been introduced as a promising solution for lightweight and efficient machine learning, particularly…”
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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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Fast Adversarial Training with Dynamic Batch-level Attack Control by Jung, Jaewon, Song, Jaeyong, Jang, Hongsun, Lee, Hyeyoon, Choi, Kanghyun, Park, Noseong, Lee, Jinho

“…Despite the fact that adversarial training provides an effective protection against adversarial attacks, it suffers from a huge computational overhead. To…”
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Fairquant: Certifying and Quantifying Fairness of Deep Neural Networks by 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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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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Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning by Fu, Yonggan, Yuan, Ye, Wu, Shang, Yuan, Jiayi, Lin, Yingyan Celine

“…Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks…”
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MMDFL: Multi-Model-based Decentralized Federated Learning for Resource-Constrained AIoT Systems by Yan, Dengke, Yang, Yanxin, Hu, Ming, Fu, Xin, Chen, Mingsong

“…) applications adopt Federated Learning (FL) to enable collaborative learning without compromising the privacy of devices…”
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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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