The study presents Simulated Overparametrization (SOP), a novel paradigm that combines the efficiency of compact models with the advanced learning proficiencies of overparameterized models. SOP trains a larger model in…
Continue readingThe article presents Neural Implicit Topology Optimization (NITO), a deep learning approach for topology optimization in engineering design. NITO offers a resolution-free and domain-agnostic solution, outperforming state-of-the-art models in structural…
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 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 readingThe paper introduces a novel approach to Intrusion Detection Systems (IDS), called X-CBA. This approach leverages the advantages of Graph Neural Networks (GNNs) to effectively process network traffic data while…
Continue readingThis review paper provides an overview of deep learning approaches for trajectory data. The authors identify eight specific mobility use cases, analyzing the deep learning models and training data used….
Continue readingThe paper proposes using techniques from control theory to update Deep Neural Networks (DNN) parameters online. It formulates the fully-connected feedforward DNN as a continuous-time dynamical system. The authors present…
Continue readingThe paper discusses the role of Bayesian Deep Learning (BDL) in the current deep learning research landscape. It points out that while most research focuses on predictive accuracy in supervised…
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