arXiv - CS - Machine Learning最新文献_第3页

The Impact of Element Ordering on LM Agent Performance 元素排序对 LM Agent 性能的影响

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.12089

Wayne Chi, Ameet Talwalkar, Chris Donahue

{"title":"The Impact of Element Ordering on LM Agent Performance","authors":"Wayne Chi, Ameet Talwalkar, Chris Donahue","doi":"arxiv-2409.12089","DOIUrl":"https://doi.org/arxiv-2409.12089","url":null,"abstract":"There has been a surge of interest in language model agents that can navigate\u0000virtual environments such as the web or desktop. To navigate such environments,\u0000agents benefit from information on the various elements (e.g., buttons, text,\u0000or images) present. It remains unclear which element attributes have the\u0000greatest impact on agent performance, especially in environments that only\u0000provide a graphical representation (i.e., pixels). Here we find that the\u0000ordering in which elements are presented to the language model is surprisingly\u0000impactful--randomizing element ordering in a webpage degrades agent performance\u0000comparably to removing all visible text from an agent's state representation.\u0000While a webpage provides a hierarchical ordering of elements, there is no such\u0000ordering when parsing elements directly from pixels. Moreover, as tasks become\u0000more challenging and models more sophisticated, our experiments suggest that\u0000the impact of ordering increases. Finding an effective ordering is non-trivial.\u0000We investigate the impact of various element ordering methods in web and\u0000desktop environments. We find that dimensionality reduction provides a viable\u0000ordering for pixel-only environments. We train a UI element detection model to\u0000derive elements from pixels and apply our findings to an agent\u0000benchmark--OmniACT--where we only have access to pixels. Our method completes\u0000more than two times as many tasks on average relative to the previous\u0000state-of-the-art.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261781","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

Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning 在安全强化学习中处理长期安全性和不确定性

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.12045

Jonas Günster, Puze Liu, Jan Peters, Davide Tateo

{"title":"Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning","authors":"Jonas Günster, Puze Liu, Jan Peters, Davide Tateo","doi":"arxiv-2409.12045","DOIUrl":"https://doi.org/arxiv-2409.12045","url":null,"abstract":"Safety is one of the key issues preventing the deployment of reinforcement\u0000learning techniques in real-world robots. While most approaches in the Safe\u0000Reinforcement Learning area do not require prior knowledge of constraints and\u0000robot kinematics and rely solely on data, it is often difficult to deploy them\u0000in complex real-world settings. Instead, model-based approaches that\u0000incorporate prior knowledge of the constraints and dynamics into the learning\u0000framework have proven capable of deploying the learning algorithm directly on\u0000the real robot. Unfortunately, while an approximated model of the robot\u0000dynamics is often available, the safety constraints are task-specific and hard\u0000to obtain: they may be too complicated to encode analytically, too expensive to\u0000compute, or it may be difficult to envision a priori the long-term safety\u0000requirements. In this paper, we bridge this gap by extending the safe\u0000exploration method, ATACOM, with learnable constraints, with a particular focus\u0000on ensuring long-term safety and handling of uncertainty. Our approach is\u0000competitive or superior to state-of-the-art methods in final performance while\u0000maintaining safer behavior during training.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269707","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

Enhancing PM2.5 Data Imputation and Prediction in Air Quality Monitoring Networks Using a KNN-SINDy Hybrid Model 利用 KNN-SINDy 混合模型加强空气质量监测网络中的 PM2.5 数据推算和预测

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.11640

Yohan Choi, Boaz Choi, Jachin Choi

引用次数: 0

A Unified Framework for Neural Computation and Learning Over Time 神经计算和随时间学习的统一框架

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.12038

Stefano Melacci, Alessandro Betti, Michele Casoni, Tommaso Guidi, Matteo Tiezzi, Marco Gori

{"title":"A Unified Framework for Neural Computation and Learning Over Time","authors":"Stefano Melacci, Alessandro Betti, Michele Casoni, Tommaso Guidi, Matteo Tiezzi, Marco Gori","doi":"arxiv-2409.12038","DOIUrl":"https://doi.org/arxiv-2409.12038","url":null,"abstract":"This paper proposes Hamiltonian Learning, a novel unified framework for\u0000learning with neural networks \"over time\", i.e., from a possibly infinite\u0000stream of data, in an online manner, without having access to future\u0000information. Existing works focus on the simplified setting in which the stream\u0000has a known finite length or is segmented into smaller sequences, leveraging\u0000well-established learning strategies from statistical machine learning. In this\u0000paper, the problem of learning over time is rethought from scratch, leveraging\u0000tools from optimal control theory, which yield a unifying view of the temporal\u0000dynamics of neural computations and learning. Hamiltonian Learning is based on\u0000differential equations that: (i) can be integrated without the need of external\u0000software solvers; (ii) generalize the well-established notion of gradient-based\u0000learning in feed-forward and recurrent networks; (iii) open to novel\u0000perspectives. The proposed framework is showcased by experimentally proving how\u0000it can recover gradient-based learning, comparing it to out-of-the box\u0000optimizers, and describing how it is flexible enough to switch from fully-local\u0000to partially/non-local computational schemes, possibly distributed over\u0000multiple devices, and BackPropagation without storing activations. Hamiltonian\u0000Learning is easy to implement and can help researches approach in a principled\u0000and innovative manner the problem of learning over time.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261827","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

Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility 更强大的基线模型 -- 将机器学习研究与临床实用性相结合的关键要求

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.12116

Nathan Wolfrath, Joel Wolfrath, Hengrui Hu, Anjishnu Banerjee, Anai N. Kothari

{"title":"Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility","authors":"Nathan Wolfrath, Joel Wolfrath, Hengrui Hu, Anjishnu Banerjee, Anai N. Kothari","doi":"arxiv-2409.12116","DOIUrl":"https://doi.org/arxiv-2409.12116","url":null,"abstract":"Machine Learning (ML) research has increased substantially in recent years,\u0000due to the success of predictive modeling across diverse application domains.\u0000However, well-known barriers exist when attempting to deploy ML models in\u0000high-stakes, clinical settings, including lack of model transparency (or the\u0000inability to audit the inference process), large training data requirements\u0000with siloed data sources, and complicated metrics for measuring model utility.\u0000In this work, we show empirically that including stronger baseline models in\u0000healthcare ML evaluations has important downstream effects that aid\u0000practitioners in addressing these challenges. Through a series of case studies,\u0000we find that the common practice of omitting baselines or comparing against a\u0000weak baseline model (e.g. a linear model with no optimization) obscures the\u0000value of ML methods proposed in the research literature. Using these insights,\u0000we propose some best practices that will enable practitioners to more\u0000effectively study and deploy ML models in clinical settings.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269705","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

Consistent Estimation of a Class of Distances Between Covariance Matrices 协方差矩阵间一类距离的一致性估计

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.11761

Roberto Pereira, Xavier Mestre, Davig Gregoratti

{"title":"Consistent Estimation of a Class of Distances Between Covariance Matrices","authors":"Roberto Pereira, Xavier Mestre, Davig Gregoratti","doi":"arxiv-2409.11761","DOIUrl":"https://doi.org/arxiv-2409.11761","url":null,"abstract":"This work considers the problem of estimating the distance between two\u0000covariance matrices directly from the data. Particularly, we are interested in\u0000the family of distances that can be expressed as sums of traces of functions\u0000that are separately applied to each covariance matrix. This family of distances\u0000is particularly useful as it takes into consideration the fact that covariance\u0000matrices lie in the Riemannian manifold of positive definite matrices, thereby\u0000including a variety of commonly used metrics, such as the Euclidean distance,\u0000Jeffreys' divergence, and the log-Euclidean distance. Moreover, a statistical\u0000analysis of the asymptotic behavior of this class of distance estimators has\u0000also been conducted. Specifically, we present a central limit theorem that\u0000establishes the asymptotic Gaussianity of these estimators and provides closed\u0000form expressions for the corresponding means and variances. Empirical\u0000evaluations demonstrate the superiority of our proposed consistent estimator\u0000over conventional plug-in estimators in multivariate analytical contexts.\u0000Additionally, the central limit theorem derived in this study provides a robust\u0000statistical framework to assess of accuracy of these estimators.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261838","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

NPAT Null-Space Projected Adversarial Training Towards Zero Deterioration NPAT 零空间预测对抗训练，实现零恶化

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.11754

Hanyi Hu, Qiao Han, Kui Chen, Yao Yang

{"title":"NPAT Null-Space Projected Adversarial Training Towards Zero Deterioration","authors":"Hanyi Hu, Qiao Han, Kui Chen, Yao Yang","doi":"arxiv-2409.11754","DOIUrl":"https://doi.org/arxiv-2409.11754","url":null,"abstract":"To mitigate the susceptibility of neural networks to adversarial attacks,\u0000adversarial training has emerged as a prevalent and effective defense strategy.\u0000Intrinsically, this countermeasure incurs a trade-off, as it sacrifices the\u0000model's accuracy in processing normal samples. To reconcile the trade-off, we\u0000pioneer the incorporation of null-space projection into adversarial training\u0000and propose two innovative Null-space Projection based Adversarial\u0000Training(NPAT) algorithms tackling sample generation and gradient optimization,\u0000named Null-space Projected Data Augmentation (NPDA) and Null-space Projected\u0000Gradient Descent (NPGD), to search for an overarching optimal solutions, which\u0000enhance robustness with almost zero deterioration in generalization\u0000performance. Adversarial samples and perturbations are constrained within the\u0000null-space of the decision boundary utilizing a closed-form null-space\u0000projector, effectively mitigating threat of attack stemming from unreliable\u0000features. Subsequently, we conducted experiments on the CIFAR10 and SVHN\u0000datasets and reveal that our methodology can seamlessly combine with\u0000adversarial training methods and obtain comparable robustness while keeping\u0000generalization close to a high-accuracy model.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261837","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

Unraveling the Hessian: A Key to Smooth Convergence in Loss Function Landscapes 揭开赫塞斯的面纱：损失函数景观平滑收敛的关键

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.11995

Nikita Kiselev, Andrey Grabovoy

引用次数: 0

Symmetry-Enriched Learning: A Category-Theoretic Framework for Robust Machine Learning Models 对称丰富学习：稳健机器学习模型的类别理论框架

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.12100

Ronald Katende

引用次数: 0

Few-Shot Class-Incremental Learning with Non-IID Decentralized Data 利用非 IID 分散数据进行少镜头分类增量学习

arXiv - CS - Machine Learning Pub Date : 2024-09-18 DOI: arxiv-2409.11657

Cuiwei Liu, Siang Xu, Huaijun Qiu, Jing Zhang, Zhi Liu, Liang Zhao

{"title":"Few-Shot Class-Incremental Learning with Non-IID Decentralized Data","authors":"Cuiwei Liu, Siang Xu, Huaijun Qiu, Jing Zhang, Zhi Liu, Liang Zhao","doi":"arxiv-2409.11657","DOIUrl":"https://doi.org/arxiv-2409.11657","url":null,"abstract":"Few-shot class-incremental learning is crucial for developing scalable and\u0000adaptive intelligent systems, as it enables models to acquire new classes with\u0000minimal annotated data while safeguarding the previously accumulated knowledge.\u0000Nonetheless, existing methods deal with continuous data streams in a\u0000centralized manner, limiting their applicability in scenarios that prioritize\u0000data privacy and security. To this end, this paper introduces federated\u0000few-shot class-incremental learning, a decentralized machine learning paradigm\u0000tailored to progressively learn new classes from scarce data distributed across\u0000multiple clients. In this learning paradigm, clients locally update their\u0000models with new classes while preserving data privacy, and then transmit the\u0000model updates to a central server where they are aggregated globally. However,\u0000this paradigm faces several issues, such as difficulties in few-shot learning,\u0000catastrophic forgetting, and data heterogeneity. To address these challenges,\u0000we present a synthetic data-driven framework that leverages replay buffer data\u0000to maintain existing knowledge and facilitate the acquisition of new knowledge.\u0000Within this framework, a noise-aware generative replay module is developed to\u0000fine-tune local models with a balance of new and replay data, while generating\u0000synthetic data of new classes to further expand the replay buffer for future\u0000tasks. Furthermore, a class-specific weighted aggregation strategy is designed\u0000to tackle data heterogeneity by adaptively aggregating class-specific\u0000parameters based on local models performance on synthetic data. This enables\u0000effective global model optimization without direct access to client data.\u0000Comprehensive experiments across three widely-used datasets underscore the\u0000effectiveness and preeminence of the introduced framework.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261956","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