Summary of Satisficing Exploration For Deep Reinforcement Learning, by Dilip Arumugam et al.
Satisficing Exploration for Deep Reinforcement Learningby Dilip Arumugam, Saurabh Kumar, Ramki Gummadi, Benjamin Van RoyFirst…
Summary of This Probably Looks Exactly Like That: An Invertible Prototypical Network, by Zachariah Carmichael et al.
This Probably Looks Exactly Like That: An Invertible Prototypical Networkby Zachariah Carmichael, Timothy Redgrave, Daniel…
Summary of On the Calibration Of Epistemic Uncertainty: Principles, Paradoxes and Conflictual Loss, by Mohammed Fellaji et al.
On the Calibration of Epistemic Uncertainty: Principles, Paradoxes and Conflictual Lossby Mohammed Fellaji, Frédéric Pennerath,…
Summary of Variance Norms For Kernelized Anomaly Detection, by Thomas Cass et al.
Variance Norms for Kernelized Anomaly Detectionby Thomas Cass, Lukas Gonon, Nikita ZozoulenkoFirst submitted to arxiv…
Summary of Simplifying the Theory on Over-smoothing, by Andreas Roth
Simplifying the Theory on Over-Smoothingby Andreas RothFirst submitted to arxiv on: 16 Jul 2024CategoriesMain: Machine…
Summary of Learning Confidence Bounds For Classification with Imbalanced Data, by Matt Clifford et al.
Learning Confidence Bounds for Classification with Imbalanced Databy Matt Clifford, Jonathan Erskine, Alexander Hepburn, Raúl…
Summary of Deep Learning Without Global Optimization by Random Fourier Neural Networks, By Owen Davis et al.
Deep Learning without Global Optimization by Random Fourier Neural Networksby Owen Davis, Gianluca Geraci, Mohammad…
Summary of Combining Wasserstein-1 and Wasserstein-2 Proximals: Robust Manifold Learning Via Well-posed Generative Flows, by Hyemin Gu et al.
Combining Wasserstein-1 and Wasserstein-2 proximals: robust manifold learning via well-posed generative flowsby Hyemin Gu, Markos…
Summary of Graphfm: a Scalable Framework For Multi-graph Pretraining, by Divyansha Lachi et al.
GraphFM: A Scalable Framework for Multi-Graph Pretrainingby Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva DyerFirst…
Summary of Benchmarking the Attribution Quality Of Vision Models, by Robin Hesse et al.
Benchmarking the Attribution Quality of Vision Modelsby Robin Hesse, Simone Schaub-Meyer, Stefan RothFirst submitted to…