A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The authors discuss the need for explainable AI systems, focusing on counterfactual explanations that are often used post-decision. They argue that these explanations often lack plausibility, hence limiting their value….
Continue readingThe research focuses on understanding human emotions, particularly fear, in virtual reality (VR) environments. This understanding is crucial in the development of immersive games, scene development, and next-generation human-computer interaction…
Continue readingThe article presents a new framework for Interactive Machine Learning (IML), aiming to incorporate human expertise into machine learning algorithms. The framework is grounded in Bayesian Optimization (BO), thus called…
Continue readingThe paper presents a new approach to crowdsourcing, leveraging self-supervised learning and a novel aggregation scheme. Instead of averaging estimates from crowdworkers, it adapts weights based on their previous estimates….
Continue readingThe article studies the impact of overparameterization on the complexity and sensitivity of the function represented by neural networks. It discusses the ‘double-descent’ phenomenon, where highly overparameterized models escape overfitting…
Continue readingThe article ‘Prompt Smells: An Omen for Undesirable Generative AI Outputs’ discusses the recent trends in Generative Artificial Intelligence (GenAI) and its application in various fields. It sheds light on…
Continue readingThe paper presents an in-depth analysis of the joint effect of task similarity and overparameterization on catastrophic forgetting in continual learning. The researchers focus on two-task continual linear regression, where…
Continue readingThe authors respond to Makelov et al.’s recent paper that reviews subspace interchange intervention methods like Distributed Alignment Search (DAS) and claims these could cause interpretability illusions. They argue that…
Continue readingThe article introduces a new framework for feature selection in machine learning models. The approach uses a feature masking method, allowing the same model to be used during feature selection,…
Continue readingThis article presents a new method for multiview clustering (MVC) via contrastive learning, called CCEC. MVC is a process that groups data samples into clusters by synthesizing information across multiple…
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