
NIPS 2015 Awards - NeurIPS
The workshop chairs would like to thank the award selection committee: Ahmad Abdulkader, George Dahl, Hugo Larochelle , Honglak Lee , Matthias Bethge , Koray kavukcuoglu, Samy Bengio , Khalid …
Awesome-Computer-Vision-Paper-List/NeurIPS/nips2015.md at main ... - GitHub
This repository contains all the papers accepted in top conference of computer vision, with convenience to search related papers. - Awesome-Computer-Vision-Paper-List/NeurIPS/nips2015.md at main · …
NeurIPS 2015 Accepted Paper List - Paper Copilot
How to use the paper list below: - Overview: This table presents papers from the NIPS conference, year 2015. - Filtering: By default, the table loads the first 100 records. You can use the filter box under …
NIPS 2015 List of Accepted Papers - NeurIPS
NIPS 2015 Accepted Papers Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing Nihar Shah*, UC Berkeley; Dengyong Zhou, MSR Learning with Symmetric Label Noise: The …
Announcing the NeurIPS 2025 Best Paper Awards
Nov 26, 2025 · The Best Paper Award Committee members were nominated by the Program Chairs and the Database and Benchmark track chairs, who selected leading researchers across machine …
NeurIPS - List of Proceedings
Datasets and Benchmarks In 2021, NeurIPS introduced a new track, Datasets and Benchmarks. The first year of that track, 2021, has its own proceedings, accessible by the link below. From 2022 on, …
Best Paper Awards in Computer Science (since 1996)
"Distinguished paper award" and "outstanding paper award" are included but not "best student paper" (e.g. NIPS) or "best 10-year old paper" (e.g. POPL). The list of papers for years 2017 and 2018 were …
NeurIPS Paper List - Paper Copilot
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Best Paper Awards in Computer Science - Jeff Huang
Dec 13, 2023 · This is a collection of best paper awards from conferences in each computer science subfield, starting from 1996. Originally, the broadest representative conference for each subfield …
NeurIPS 2025 Awards
In this paper, we study building blocks for self-supervised RL that unlock substantial improvements in scalability, with network depth serving as a critical factor.