Summary of Indexed Minimum Empirical Divergence-based Algorithms For Linear Bandits, by Jie Bian and Vincent Y. F. Tan
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Banditsby Jie Bian, Vincent Y. F. TanFirst submitted…
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Banditsby Jie Bian, Vincent Y. F. TanFirst submitted…
AGS-GNN: Attribute-guided Sampling for Graph Neural Networksby Siddhartha Shankar Das, S M Ferdous, Mahantesh M…
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognitionby Zijin Gu, Tatiana…
Fast inference with Kronecker-sparse matricesby Antoine Gonon, Léon Zheng, Pascal Carrivain, Quoc-Tung LeFirst submitted to…
What Variables Affect Out-of-Distribution Generalization in Pretrained Models?by Md Yousuf Harun, Kyungbok Lee, Jhair Gallardo,…
OAC: Output-adaptive Calibration for Accurate Post-training Quantizationby Ali Edalati, Alireza Ghaffari, Masoud Asgharian, Lu Hou,…
AdjointDEIS: Efficient Gradients for Diffusion Modelsby Zander W. Blasingame, Chen LiuFirst submitted to arxiv on:…
Amortized nonmyopic active search via deep imitation learningby Quan Nguyen, Anindya Sarkar, Roman GarnettFirst submitted…
CEEBERT: Cross-Domain Inference in Early Exit BERTby Divya Jyoti Bajpai, Manjesh Kumar HanawalFirst submitted to…
Credal Wrapper of Model Averaging for Uncertainty Estimation on Out-Of-Distribution Detectionby Kaizheng Wang, Fabio Cuzzolin,…