Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

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Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

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Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

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How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Learning to Assemble with Alternative Plans

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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Random matrix methods for high-dimensional machine learning models

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

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Aggregating Spatial and Photometric Context for Photometric Stereo

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