Search Results - "Computing methodologies Machine learning Machine learning algorithms"

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

“…Reinforcement Learning (RL) is a powerful technology to solve decision-making problems such as robotics control. Modern RL algorithms, i.e., Deep Q-Learning,…”
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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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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) having huge social impact. But sometimes the…”
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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…”
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Lightning Talk: The New Era of Computational Cognitive Intelligence by Dutt, Nikil, Donyanavard, Bryan

“…The triple whammy of variability in platforms (e.g., process variability), applications (e.g., dynamic use cases in autonomous systems), and the environment…”
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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, there has…”
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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). They have shown promising…”
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DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification by Wang, Junyao, Huang, Sitao, Imani, Mohsen

“…The Internet of Things (IoT) has become an emerging trend that connects heterogeneous devices and enables them with new capabilities. Many applications exploit…”
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Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems by Wan, Zishen, Anwar, Aqeel, Hsiao, Yu-Shun, Jia, Tianyu, Reddi, Vijay Janapa, Raychowdhury, Arijit

“…Learning-based navigation systems are widely used in autonomous applications, such as robotics, unmanned vehicles and drones. Specialized hardware accelerators…”
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Llanimation: Llama Driven Gesture Animation by Windle, J, Matthews, I, Taylor, S

“…Co‐speech gesturing is an important modality in conversation, providing context and social cues. In character animation, appropriate and synchronised gestures…”
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Understanding and optimizing asynchronous low-precision stochastic gradient descent by De Sa, Christopher, Feldman, Matthew, Re, Christopher, Olukotun, Kunle

“…Stochastic gradient descent (SGD) is one of the most popular numerical algorithms used in machine learning and other domains. Since this is likely to continue…”
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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. However, it is critical to…”
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FF-INT8: Efficient Forward-Forward DNN Training on Edge Devices with INT8 Precision by Ma, Jingxiao, Panda, Priyadarshini, Reda, Sherief

“…Backpropagation has been the cornerstone of neural network training for decades, yet its inefficiencies in time and energy consumption limit its suitability…”
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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…”
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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…”
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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…”
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BERRY: Bit Error Robustness for Energy-Efficient Reinforcement Learning-Based Autonomous Systems by Wan, Zishen, Chandramoorthy, Nandhini, Swaminathan, Karthik, Chen, Pin-Yu, Reddi, Vijay Janapa, Raychowdhury, Arijit

“…Autonomous systems, such as Unmanned Aerial Vehicles (UAVs), are expected to run complex reinforcement learning (RL) models to execute fully autonomous…”
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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

“…Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves…”
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REMU: Memory-aware Radiation Emulation via Dual Addressing for In-orbit Deep Learning System by Xu, Longnv, Wang, Meiqi, Qiu, Han, Liu, Jun, Li, Yuanjie, Li, Hewu

“…The deployment of commercial-off-the-shelf (COTS) GPUs in space has emerged as a promising approach for supporting inorbit deep neural network (DNN) inference…”
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