Summary of Learning Mixtures Of Unknown Causal Interventions, by Abhinav Kumar et al.
Learning Mixtures of Unknown Causal Interventionsby Abhinav Kumar, Kirankumar Shiragur, Caroline UhlerFirst submitted to arxiv…
Summary of Inclusive Kl Minimization: a Wasserstein-fisher-rao Gradient Flow Perspective, by Jia-jie Zhu
Inclusive KL Minimization: A Wasserstein-Fisher-Rao Gradient Flow Perspectiveby Jia-Jie ZhuFirst submitted to arxiv on: 31…
Summary of Understanding the Limits Of Vision Language Models Through the Lens Of the Binding Problem, by Declan Campbell et al.
Understanding the Limits of Vision Language Models Through the Lens of the Binding Problemby Declan…
Summary of Minimum Empirical Divergence For Sub-gaussian Linear Bandits, by Kapilan Balagopalan and Kwang-sung Jun
Minimum Empirical Divergence for Sub-Gaussian Linear Banditsby Kapilan Balagopalan, Kwang-Sung JunFirst submitted to arxiv on:…
Summary of Residual Transformer Alignment with Spectral Decomposition, by Lorenzo Basile et al.
ResiDual Transformer Alignment with Spectral Decompositionby Lorenzo Basile, Valentino Maiorca, Luca Bortolussi, Emanuele Rodolà, Francesco…
Summary of Deep Learning Through a Telescoping Lens: a Simple Model Provides Empirical Insights on Grokking, Gradient Boosting & Beyond, by Alan Jeffares et al.
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient…
Summary of Io Transformer: Evaluating Swinv2-based Reward Models For Computer Vision, by Maxwell Meyer and Jack Spruyt
IO Transformer: Evaluating SwinV2-Based Reward Models for Computer Visionby Maxwell Meyer, Jack SpruytFirst submitted to…
Summary of Enhancing Diversity in Bayesian Deep Learning Via Hyperspherical Energy Minimization Of Cka, by David Smerkous et al.
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKAby David Smerkous, Qinxun…
Summary of Space For Improvement: Navigating the Design Space For Federated Learning in Satellite Constellations, by Grace Kim et al.
Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellationsby Grace Kim,…
Summary of Quantifying Calibration Error in Modern Neural Networks Through Evidence Based Theory, by Koffi Ismael Ouattara
Quantifying calibration error in modern neural networks through evidence based theoryby Koffi Ismael OuattaraFirst submitted…