Search Results - Computing methodologies → Machine Learning → Machine Learning algorithm

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…”
Get full text

Toward Individual Fairness Testing with Data Validity by Kitamura, Takashi, Amasaki, Sousuke, Inoue, Jun, Isobe, Yoshinao, Toda, Takahisa

“…)". We develop a solid foundation of Ift-v and demonstrate the feasibility of Ift-v. Our preliminary evaluation with Ift-v reveals the possibility that many of discriminatory instances detected by state-of-the-art Ift algorithms are considered invalid…”
Get full text

DARL: Distributed Reconfigurable Accelerator for Hyperdimensional Reinforcement Learning by Chen, Hanning, Issa, Mariam, Ni, Yang, Imani, Mohsen

“… Modern RL algorithms, i.e., Deep Q-Learning, are based on costly and resource hungry deep neural networks…”
Get full text

GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems by Liang, Linfeng, Deng, Yao, Morton, Kye, Kallinen, Valtteri, James, Alice, Seth, Avishkar, Kuantama, Endrowednes, Mukhopadhyay, Subhas, Han, Richard, Zheng, Xi

“… To address these issues, we introduce GARL, a framework combining a genetic algorithm (GA) and reinforcement learning (RL…”
Get full text

A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning by Fu, Yonggan, Zhang, Yongan, Li, Chaojian, Yu, Zhongzhi, Lin, Yingyan

“…Driven by the explosive interest in applying deep reinforcement learning (DRL) agents to numerous real-time control and decision-making applications…”
Get full text

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…”
Get full text

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…”
Get full text

Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation by Wang, Yixuan, Huang, Chao, Wang, Zhilu, Xu, Shichao, Wang, Zhaoran, Zhu, Qi

“…Neural networks are being increasingly applied to control and decision making for learning-enabled cyber-physical systems (LE-CPSs…”
Get full text

Lightning Talk: The New Era of Computational Cognitive Intelligence by Dutt, Nikil, Donyanavard, Bryan

“…), and the environment (e.g., context) renders ineffective the classical computational/algorithmic/numerical computing paradigm in dealing with the inherent runtime dynamism and uncertainty faced by emerging systems…”
Get full text

SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments by Putra, Rachmad Vidya Wicaksana, Shafique, Muhammad

“…Spiking Neural Networks (SNNs) bear the potential of efficient unsupervised and continual learning capabilities because of their biological plausibility, but their complexity still poses a serious research challenge to enable…”
Get full text

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…”
Get full text

FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection Traffic Signal Control by Ye, Yutong, Zhao, Wupan, Wei, Tongquan, Hu, Shiyan, Chen, Mingsong

“…Although Reinforcement Learning (RL) has been successfully applied in traffic control, it suffers from the problems of high average vehicle travel time and slow convergence to optimized solutions…”
Get full text

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…”
Get full text

RegHD: Robust and Efficient Regression in Hyper-Dimensional Learning System by 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…”
Get full text

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…”
Get full text

FF-INT8: Efficient Forward-Forward DNN Training on Edge Devices with INT8 Precision by Ma, Jingxiao, Panda, Priyadarshini, Reda, Sherief

“… Recently, the Forward-Forward (FF) algorithm has emerged as a promising alternative to backpropagation, replacing the backward pass with an additional forward pass…”
Get full text

RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs by Yin, Jiaqi, Li, Yingjie, Robinson, Daniel, Yu, Cunxi

“…, computation, I/O, and memory-bound) edge computing systems. While efficient execution of their computational graph requires an effective scheduling algorithm, generating the optimal scheduling solution is a challenging NP-hard problem…”
Get full text

On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning Implementations by Hundal, Rajdeep Singh, Xiao, Yan, Cao, Xiaochun, Dong, Jin Song, Rigger, Manuel

“…Deep Reinforcement Learning (DRL) is a paradigm of artificial intelligence where an agent uses a neural network to learn which actions to take in a given environment…”
Get full text

MUTEN: Mutant-Based Ensembles for Boosting Gradient-Based Adversarial Attack by Hu, Qiang, Guo, Yuejun, Cordy, Maxime, Papadakis, Mike, Traon, Yves Le

“…Mutation testing (MT) for deep learning (DL) has gained huge attention in the past few years…”
Get full text

ADVeRL-ELF: ADVersarial ELF Malware Generation using Reinforcement Learning by Ravi, Akshara, Chaturvedi, Vivek, Shafique, Muhammad

“…Deep learning models are now pervasive in the malware detection domain owing to their high accuracy and performance efficiency…”
Get full text