Výsledky vyhľadávania - Computing methodologies→Machine learning algorithms*

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

“…Reinforcement Learning (RL) is a powerful technology to solve decision-making problems such as robotics control…”
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Llanimation: Llama Driven Gesture Animation Autor 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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Lightning Talk: The New Era of Computational Cognitive Intelligence Autor 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…”
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A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning Autor 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…”
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Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation Autor 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…”
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Benchmark Movement Data Set for Trust Assessment in Human Robot Collaboration Autor Rehm, Matthias, Hald, Kasper, Pontikis, Ioannis

“…* Human-centered computing → Human computer interaction (HCI); * Computing methodologies → Machine learning…”
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GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems Autor 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…”
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Resilient linear classification: an approach to deal with attacks on training data Autor Park, Sangdon, Weimer, James, Lee, Insup

“… However, if the training data is maliciously altered by attackers, the effect of these attacks on the learning algorithms underpinning data-driven CPS have yet to be considered…”
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Making Fair ML Software using Trustworthy Explanation Autor Chakraborty, Joymallya, Peng, Kewen, Menzies, Tim

“…Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice…”
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SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments Autor 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…”
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Preference-Conditioned Language-Guided Abstraction Autor Peng, Andi, Bobu, Andreea, Li, Belinda Z., Sumers, Theodore R., Sucholutsky, Ilia, Kumar, Nishanth, Griffiths, Thomas L., Shah, Julie A.

“…Learning from demonstrations is a common way for users to teach robots, but it is prone to spurious feature correlations…”
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DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classification Autor 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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Towards Scalable and Efficient Spiking Reinforcement Learning for Continuous Control Tasks Autor Tahmid, Tokey, Gates, Mark, Luszczek, Piotr, Schuman, Catherine

“… Even though SNNs are efficient by their design and structure, they lack many of the optimizations known from deep reinforcement learning (DeepRL) algorithms…”
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Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems Autor 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…”
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Stellaris: Staleness-Aware Distributed Reinforcement Learning with Serverless Computing Autor Yu, Hanfei, Wang, Hao, Tiwari, Devesh, Li, Jian, Park, Seung-Jong

“… This paper proposes Stellaris, the first to introduce a generic asynchronous learning paradigm for distributed DRL training with serverless computing…”
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ADVeRL-ELF: ADVersarial ELF Malware Generation using Reinforcement Learning Autor 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…”
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FF-INT8: Efficient Forward-Forward DNN Training on Edge Devices with INT8 Precision Autor 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 Autor 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…”
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