Search Results - "Computing methodologies Machine learning Learning paradigms"

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…”
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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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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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Dissecting Global Search: A Simple Yet Effective Method to Boost Individual Discrimination Testing and Repair by 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…”
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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

“…After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive…”
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Code Prediction by Feeding Trees to Transformers by Kim, Seohyun, Zhao, Jinman, Tian, Yuchi, Chandra, Satish

“…Code prediction, more specifically autocomplete, has become an essential feature in modern IDEs. Autocomplete is more effective when the desired next token is…”
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Twin Graph-Based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System by Huang, Jun, Yang, Yang, Yu, Hang, Li, Jianguo, Zheng, Xiao

“…Microservice architecture has sprung up over recent years for managing enterprise applications, due to its ability to independently deploy and scale services…”
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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. Existing approaches to code generation have focused…”
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A Unified DNN Weight Pruning Framework Using Reweighted Optimization Methods by 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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CascadeHD: Efficient Many-Class Learning Framework Using Hyperdimensional Computing by 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…”
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Segmented Angular Pre-Processing for Accurate and Efficient In-Memory Vector Similarity Search by Huang, Chi-Tse, Wang, Jen-Chieh, Cheng, Hsiang-Yun, Wu, An-Yeu Andy

“…Vector similarity search (VSS) is a fundamental operation in modern AI applications, including few-shot learning (FSL) and approximate nearest neighbor search…”
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On-the-fly Improving Performance of Deep Code Models via Input Denoising by Tian, Zhao, Chen, Junjie, Zhang, Xiangyu

“…Deep learning has been widely adopted to tackle various code-based tasks by building deep code models based on a large amount of code snippets. While these…”
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Enabling On-Tiny-Device Model Personalization via Gradient Condensing and Alternant Partial Update by Jia, Zhenge, Shi, Yiyang, Bao, Zeyu, Wang, Zirui, Pang, Xin, Liu, Huiguo, Duan, Yu, Shen, Zhaoyan, Zhao, Mengying

“…On-device training enables the model to adapt to user-specific data by fine-tuning a pre-trained model locally. As embedded devices become ubiquitous,…”
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Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining by Shen, Weijun, Li, Yanhui, Chen, Lin, Han, Yuanlei, Zhou, Yuming, Xu, Baowen

“…With the increasing application of deep learning (DL) models in many safety-critical scenarios, effective and efficient DL testing techniques are much in…”
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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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Neurally-Inspired Hyperdimensional Classification for Efficient and Robust Biosignal Processing by 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…”
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AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference by Li, Min, Li, Yu, Tian, Ye, Jiang, Li, Xu, Qiang

“…This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art…”
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Online Human Activity Recognition using Low-Power Wearable Devices by 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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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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