A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The article introduces a novel approach to Kriging, a method of estimating attributes of unseen locations based on neighboring data. The proposed method, called Contrastive- Prototypical self-supervised learning for Kriging…
Continue readingThe article presents LLpowershap, a novel feature selection method that uses loss-based Shapley values to identify informative features and reduce noise. The method also demonstrates higher or equal predictive performance…
Continue readingThe study tackles the issues in Automated Model Evaluation (AutoEval), such as overconfidence and high computational cost. It introduces a novel measure, Meta-Distribution Energy (MDE), to enhance the AutoEval framework’s…
Continue readingThe article presents a comprehensive benchmarking of different selective classification frameworks that are based on deep neural networks. Selective classification allows a model to abstain from making a prediction when…
Continue readingThe article ‘When Redundancy Matters: Machine Teaching of Representations’ discusses the idea of teaching representations in machine teaching, rather than concepts. It emphasizes the role of redundancy in representation, which…
Continue readingFalcon is a novel framework that aims to address the issue of bias in machine learning models. The framework employs a data-centric approach, focusing on the curation and labeling of…
Continue readingThe paper proposes a new method for algorithm-selection using short probing trajectories. This approach is applied by running a solver on an instance for a brief period. The method provides…
Continue readingThe paper presents DeepRicci, a self-supervised model for graph structure-feature co-refinement. It aims to solve the over-squashing issue that is common in Graph Neural Networks (GNNs). This is achieved by…
Continue readingThe research paper introduces MORPH, a pseudo-label-based concept drift adaptation method designed for neural networks to mitigate the challenges in malware detection. Concept drift refers to the shift in the…
Continue readingThe article discusses the use of Physics-Informed Neural Networks (PINNs) for solving partial differential equations (PDEs). However, PINNs can face difficulties when dealing with equations with rapidly changing solutions. To…
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