A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The article discusses the importance of unlearning in machine learning models, particularly in the context of data regulations like GDPR. Unlearning involves removing the influence of certain data from a…
Continue readingThe article presents Counterfactual Concept Bottleneck Models (CF-CBMs), a new class of models in deep learning. These models are designed to address three fundamental queries: predicting class labels, explaining task…
Continue readingThe article presents Symbolic Machine Learning Prover (SMLP), a tool and library for system exploration based on data samples. SMLP uses a grey-box approach, combining statistical methods with machine learning…
Continue readingThe paper proposes a new decision-making framework for partially observable systems in continuous time with discrete state and action spaces. The framework uses approximation methods for the filtering and control…
Continue readingThis article delves into the use of Large Language Models (LLMs) in the field of chemistry. It discusses the complexities and innovations at this interdisciplinary juncture. The paper examines how…
Continue readingThe paper reviews the development and techniques of few-shot learning on graphs, a field that has seen significant advancements in recent years. The authors categorize existing studies into three major…
Continue readingThe paper argues that satellite data could create a significant shift in machine learning, suggesting a need to rethink existing practices designed for traditional data modalities. As machine learning for…
Continue readingThis article presents research on how to improve the accuracy of predictions in Machine Learning when there is a covariate shift – a change in the distribution of data between…
Continue readingThe research paper presents a new methodology for causal inference that integrates large language models (LLM) into statistical causal discovery (SCD). This is achieved through statistical causal prompting (SCP) for…
Continue readingThe study proposes Kernel-Eigen Pair Sparse Variational Gaussian Processes (KEP-SVGP) for building uncertainty-aware self-attention in transformers. The asymmetry of attention kernels is addressed using Kernel SVD (KSVD), yielding reduced complexity….
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