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Self-Supervised Learning

Bolei Zhou – From Network Dissection to Policy Dissection: Emergent Concepts in Deep Representations

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Self-Supervised Learning

Xiao Fu – Multiview and Self-Supervised Representation Learning: Nonlinear Mixture Identification

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Self-Supervised Learning

Frederick Klauschen – AI in cancer research and diagnostics – IPAM at UCLA

10 months ago

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Self-Supervised Learning

Osbert Bastani – Interpretable Machine Learning via Program Synthesis – IPAM at UCLA

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Self-Supervised Learning

Klaus Robert Muller – AI for the Sciences: Recent Developments for XAI – IPAM at UCLA

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Self-Supervised Learning

Jaesik Choi – Explainable AI to Analyze Internal Decision Mechanism of Deep Neural Networks

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Self-Supervised Learning

Bin Yu – Interpreting Deep Neural Networks towards Trustworthiness – IPAM at UCLA

10 months ago

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Self-Supervised Learning

Evrim Acar – Constrained Multimodal Data Mining using Coupled Matrix and Tensor Factorizations

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Self-Supervised Learning

Wojciech Samek – Concept-Level Explainable AI – IPAM at UCLA

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Self-Supervised Learning

Cecelia Clementi – Designing molecular models with machine learning and experimental data

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Category - AI Science

Computer Vision Deep Learning Markov Models Neural Network Quantum Computing in AI Reinforcement Learning Self-Supervised Learning Support Vector Machines (SVM)Transformer Architecture

Self-Supervised Learning

Petros Koumoutsakos – Alloys: Artificial Intelligence and Scientific Computing for Fluid Mechanics

Self-Supervised Learning

Gregoire Montavon – Towards Higher-Order and Disentangled Explainable AI – IPAM at UCLA

Self-Supervised Learning

Michele Ceriotti – Physical insights from atomic-scale machine learning – IPAM at UCLA

Self-Supervised Learning

Pamela Douglas – Exploring XAI Techniques for Neuroimaging – IPAM at UCLA

Self-Supervised Learning

Oliver Eberle – Explainable structured machine learning in similarity, graph and transformer models

Self-Supervised Learning

André Freitas – Building explanation machines for science: a neuro-symbolic perspective

Self-Supervised Learning

Vince Calhoun – Maximizing Information in neuroimaging: approaches for analysis and visualization

Self-Supervised Learning

Anders Søgaard – What’s Opaque to Whom – and Why? – IPAM at UCLA

Self-Supervised Learning

Lisa Nickerson – Addressing Confounds in Neuroimaging Machine Learning Predictions – IPAM at UCLA

Self-Supervised Learning

Nikolaus Kriegskorte – Controversial stimuli: experiments to adjudicate computational hypotheses

Self-Supervised Learning

Matteo Valleriani – XAI Historian – IPAM at UCLA

AI Science

Using AI to accelerate scientific discovery