arXiv - CS - Neural and Evolutionary Computing最新文献_第2页

SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning SHIRE：在强化学习中利用人类直觉提高采样效率

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-16 DOI: arxiv-2409.09990

Amogh Joshi, Adarsh Kumar Kosta, Kaushik Roy

{"title":"SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning","authors":"Amogh Joshi, Adarsh Kumar Kosta, Kaushik Roy","doi":"arxiv-2409.09990","DOIUrl":"https://doi.org/arxiv-2409.09990","url":null,"abstract":"The ability of neural networks to perform robotic perception and control\u0000tasks such as depth and optical flow estimation, simultaneous localization and\u0000mapping (SLAM), and automatic control has led to their widespread adoption in\u0000recent years. Deep Reinforcement Learning has been used extensively in these\u0000settings, as it does not have the unsustainable training costs associated with\u0000supervised learning. However, DeepRL suffers from poor sample efficiency, i.e.,\u0000it requires a large number of environmental interactions to converge to an\u0000acceptable solution. Modern RL algorithms such as Deep Q Learning and Soft\u0000Actor-Critic attempt to remedy this shortcoming but can not provide the\u0000explainability required in applications such as autonomous robotics. Humans\u0000intuitively understand the long-time-horizon sequential tasks common in\u0000robotics. Properly using such intuition can make RL policies more explainable\u0000while enhancing their sample efficiency. In this work, we propose SHIRE, a\u0000novel framework for encoding human intuition using Probabilistic Graphical\u0000Models (PGMs) and using it in the Deep RL training pipeline to enhance sample\u0000efficiency. Our framework achieves 25-78% sample efficiency gains across the\u0000environments we evaluate at negligible overhead cost. Additionally, by teaching\u0000RL agents the encoded elementary behavior, SHIRE enhances policy\u0000explainability. A real-world demonstration further highlights the efficacy of\u0000policies trained using our framework.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification COSCO：用于少镜头多变量时间序列分类的锐度感知训练框架

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-15 DOI: arxiv-2409.09645

Jesus Barreda, Ashley Gomez, Ruben Puga, Kaixiong Zhou, Li Zhang

引用次数: 0

TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification TX-Gen：时间序列分类稀疏反事实解释的多目标优化

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-14 DOI: arxiv-2409.09461

Qi Huang, Sofoklis Kitharidis, Thomas Bäck, Niki van Stein

{"title":"TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification","authors":"Qi Huang, Sofoklis Kitharidis, Thomas Bäck, Niki van Stein","doi":"arxiv-2409.09461","DOIUrl":"https://doi.org/arxiv-2409.09461","url":null,"abstract":"In time-series classification, understanding model decisions is crucial for\u0000their application in high-stakes domains such as healthcare and finance.\u0000Counterfactual explanations, which provide insights by presenting alternative\u0000inputs that change model predictions, offer a promising solution. However,\u0000existing methods for generating counterfactual explanations for time-series\u0000data often struggle with balancing key objectives like proximity, sparsity, and\u0000validity. In this paper, we introduce TX-Gen, a novel algorithm for generating\u0000counterfactual explanations based on the Non-dominated Sorting Genetic\u0000Algorithm II (NSGA-II). TX-Gen leverages evolutionary multi-objective\u0000optimization to find a diverse set of counterfactuals that are both sparse and\u0000valid, while maintaining minimal dissimilarity to the original time series. By\u0000incorporating a flexible reference-guided mechanism, our method improves the\u0000plausibility and interpretability of the counterfactuals without relying on\u0000predefined assumptions. Extensive experiments on benchmark datasets demonstrate\u0000that TX-Gen outperforms existing methods in generating high-quality\u0000counterfactuals, making time-series models more transparent and interpretable.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ORS: A novel Olive Ridley Survival inspired Meta-heuristic Optimization Algorithm ORS：新颖的 Olive Ridley 生存启发元启发式优化算法

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-13 DOI: arxiv-2409.09210

Niranjan Panigrahi, Sourav Kumar Bhoi, Debasis Mohapatra, Rashmi Ranjan Sahoo, Kshira Sagar Sahoo, Anil Mohapatra

{"title":"ORS: A novel Olive Ridley Survival inspired Meta-heuristic Optimization Algorithm","authors":"Niranjan Panigrahi, Sourav Kumar Bhoi, Debasis Mohapatra, Rashmi Ranjan Sahoo, Kshira Sagar Sahoo, Anil Mohapatra","doi":"arxiv-2409.09210","DOIUrl":"https://doi.org/arxiv-2409.09210","url":null,"abstract":"Meta-heuristic algorithmic development has been a thrust area of research\u0000since its inception. In this paper, a novel meta-heuristic optimization\u0000algorithm, Olive Ridley Survival (ORS), is proposed which is inspired from\u0000survival challenges faced by hatchlings of Olive Ridley sea turtle. A major\u0000fact about survival of Olive Ridley reveals that out of one thousand Olive\u0000Ridley hatchlings which emerge from nest, only one survive at sea due to\u0000various environmental and other factors. This fact acts as the backbone for\u0000developing the proposed algorithm. The algorithm has two major phases:\u0000hatchlings survival through environmental factors and impact of movement\u0000trajectory on its survival. The phases are mathematically modelled and\u0000implemented along with suitable input representation and fitness function. The\u0000algorithm is analysed theoretically. To validate the algorithm, fourteen\u0000mathematical benchmark functions from standard CEC test suites are evaluated\u0000and statistically tested. Also, to study the efficacy of ORS on recent complex\u0000benchmark functions, ten benchmark functions of CEC-06-2019 are evaluated.\u0000Further, three well-known engineering problems are solved by ORS and compared\u0000with other state-of-the-art meta-heuristics. Simulation results show that in\u0000many cases, the proposed ORS algorithm outperforms some state-of-the-art\u0000meta-heuristic optimization algorithms. The sub-optimal behavior of ORS in some\u0000recent benchmark functions is also observed.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Training Spiking Neural Networks via Augmented Direct Feedback Alignment 通过增强直接反馈排列训练尖峰神经网络

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-12 DOI: arxiv-2409.07776

Yongbo Zhang, Katsuma Inoue, Mitsumasa Nakajima, Toshikazu Hashimoto, Yasuo Kuniyoshi, Kohei Nakajima

{"title":"Training Spiking Neural Networks via Augmented Direct Feedback Alignment","authors":"Yongbo Zhang, Katsuma Inoue, Mitsumasa Nakajima, Toshikazu Hashimoto, Yasuo Kuniyoshi, Kohei Nakajima","doi":"arxiv-2409.07776","DOIUrl":"https://doi.org/arxiv-2409.07776","url":null,"abstract":"Spiking neural networks (SNNs), the models inspired by the mechanisms of real\u0000neurons in the brain, transmit and represent information by employing discrete\u0000action potentials or spikes. The sparse, asynchronous properties of information\u0000processing make SNNs highly energy efficient, leading to SNNs being promising\u0000solutions for implementing neural networks in neuromorphic devices. However,\u0000the nondifferentiable nature of SNN neurons makes it a challenge to train them.\u0000The current training methods of SNNs that are based on error backpropagation\u0000(BP) and precisely designing surrogate gradient are difficult to implement and\u0000biologically implausible, hindering the implementation of SNNs on neuromorphic\u0000devices. Thus, it is important to train SNNs with a method that is both\u0000physically implementatable and biologically plausible. In this paper, we\u0000propose using augmented direct feedback alignment (aDFA), a gradient-free\u0000approach based on random projection, to train SNNs. This method requires only\u0000partial information of the forward process during training, so it is easy to\u0000implement and biologically plausible. We systematically demonstrate the\u0000feasibility of the proposed aDFA-SNNs scheme, propose its effective working\u0000range, and analyze its well-performing settings by employing genetic algorithm.\u0000We also analyze the impact of crucial features of SNNs on the scheme, thus\u0000demonstrating its superiority and stability over BP and conventional direct\u0000feedback alignment. Our scheme can achieve competitive performance without\u0000accurate prior knowledge about the utilized system, thus providing a valuable\u0000reference for physically training SNNs.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classifying Images with CoLaNET Spiking Neural Network -- the MNIST Example 使用 CoLaNET 尖峰神经网络进行图像分类 - MNIST 示例

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-12 DOI: arxiv-2409.07833

Mikhail Kiselev

引用次数: 0

Optimizing Neural Network Performance and Interpretability with Diophantine Equation Encoding 用二叉方程编码优化神经网络性能和可解释性

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-11 DOI: arxiv-2409.07310

Ronald Katende

引用次数: 0

Y-Drop: A Conductance based Dropout for fully connected layers Y-Drop：基于电导的全连接层滤除器

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-11 DOI: arxiv-2409.09088

Efthymios Georgiou, Georgios Paraskevopoulos, Alexandros Potamianos

引用次数: 0

Advanced LSTM Neural Networks for Predicting Directional Changes in Sector-Specific ETFs Using Machine Learning Techniques 利用机器学习技术预测特定行业 ETF 方向性变化的高级 LSTM 神经网络

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-09 DOI: arxiv-2409.05778

Rifa Gowani, Zaryab Kanjiani

引用次数: 0

A Comprehensive Comparison Between ANNs and KANs For Classifying EEG Alzheimer's Data ANN 与 KAN 在脑电图阿尔茨海默病数据分类方面的综合比较

arXiv - CS - Neural and Evolutionary Computing Pub Date : 2024-09-09 DOI: arxiv-2409.05989

Akshay Sunkara, Sriram Sattiraju, Aakarshan Kumar, Zaryab Kanjiani, Himesh Anumala

引用次数: 0