NeurIPS

Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons

Luke Taylor, Andrew J King, Nicol S Harper

Comments 15 pages, 5 figures

Journal ref Thirty-seventh Conference on Neural Information Processing Systems. 2023

Self-Distilled Representation Learning for Time Series

Felix Pieper, Konstantin Ditschuneit, Martin Genzel, Alexandra Lindt, Johannes Otterbach

Comments Presented at the NeurIPS 2023 Workshop: Self-Supervised Learning - Theory and Practice

Large Learning Rates Improve Generalization: But How Large Are We Talking About?

Ekaterina Lobacheva, Eduard Pockonechnyy, Maxim Kodryan, Dmitry Vetrov

Comments Published in Mathematics of Modern Machine Learning Workshop at NeurIPS 2023. First two authors contributed equally

Assessing AI Impact Assessments: A Classroom Study

Nari Johnson, Hoda Heidari

Comments 9 pages, 4 figures, to appear in the NeurIPS 2023 Regulatable ML Workshop

Exponentially Convergent Algorithms for Supervised Matrix Factorization

Joowon Lee, Hanbaek Lyu, Weixin Yao

Comments 33 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:2206.06774

Journal ref Neural Information Processing Systems 2023

Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

Haonan Yuan, Qingyun Sun, Xingcheng Fu, Ziwei Zhang, Cheng Ji, Hao Peng, Jianxin Li

Comments Accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

Auxiliary Losses for Learning Generalizable Concept-based Models

Ivaxi Sheth, Samira Ebrahimi Kahou

Comments Neurips 2023

On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation

Duy Minh Ho Nguyen, Tan Ngoc Pham, Nghiem Tuong Diep, Nghi Quoc Phan, Quang Pham, Vinh Tong, Binh T. Nguyen, Ngan Hoang Le, Nhat Ho, Pengtao Xie, Daniel Sonntag, Mathias Niepert

Comments Advances in Neural Information Processing Systems (NeurIPS) 2023, Workshop on robustness of zero/few-shot learning in foundation models

Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels

Shu-Lin Xu, Yifan Sun, Faen Zhang, Anqi Xu, Xiu-Shen Wei, Yi Yang

Comments Accepted by NeurIPS 2023

LenSiam: Self-Supervised Learning on Strong Gravitational Lens Images

Po-Wen Chang, Kuan-Wei Huang, Joshua Fagin, James Hung-Hsu Chan, Joshua Yao-Yu Lin

Comments 5 pages, 2 figures. Accepted by NeurIPS 2023 AI for Science Workshop

EDMSound: Spectrogram Based Diffusion Models for Efficient and High-Quality Audio Synthesis

Ge Zhu, Yutong Wen, Marc-André Carbonneau, Zhiyao Duan

Comments Accepted at NeurIPS Workshop: Machine Learning for Audio (Camera Ready)

Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks

Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein

Comments Accepted to NeurIPS 2023

Generating Medical Prescriptions with Conditional Transformer

Samuel Belkadi, Nicolo Micheletti, Lifeng Han, Warren Del-Pinto, Goran Nenadic

Comments Accepted to: Workshop on Synthetic Data Generation with Generative AI (SyntheticData4ML Workshop) at NeurIPS 2023

Selectivity Drives Productivity: Efficient Dataset Pruning for Enhanced Transfer Learning

Yihua Zhang, Yimeng Zhang, Aochuan Chen, Jinghan Jia, Jiancheng Liu, Gaowen Liu, Mingyi Hong, Shiyu Chang, Sijia Liu

Comments Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)

Model-adapted Fourier sampling for generative compressed sensing

Aaron Berk, Simone Brugiapaglia, Yaniv Plan, Matthew Scott, Xia Sheng, Ozgur Yilmaz

Comments 12 pages, 4 figures. Submitted to the NeurIPS 2023 Workshop on Deep Learning and Inverse Problems. This revision features additional attribution of work, aknowledgmenents, and a correction in definition 1.1

Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks

Luca Scimeca, Alexander Rubinstein, Armand Mihai Nicolicioiu, Damien Teney, Yoshua Bengio

Comments Accepted at Neural Information Processing Systems(NeurIPS) 2023 - Workshop on Diffusion Models

RegBN: Batch Normalization of Multimodal Data with Regularization

Morteza Ghahremani, Christian Wachinger

Journal ref Conference on Neural Information Processing Systems (NeurIPS 2023)

Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets

Anirudhan Badrinath, Yannis Flet-Berliac, Allen Nie, Emma Brunskill

Comments Accepted to the Conference on Neural Information Processing Systems 2023 (NeurIPS 2023)

LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep, Tan N. Pham, Tri Cao, Binh T. Nguyen, Paul Swoboda, Nhat Ho, Shadi Albarqouni, Pengtao Xie, Daniel Sonntag, Mathias Niepert

Comments Accepted at NeurIPS 2023

StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models

Yinghao Aaron Li, Cong Han, Vinay S. Raghavan, Gavin Mischler, Nima Mesgarani

Comments NeurIPS 2023

Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models

Shuo Chen, Jindong Gu, Zhen Han, Yunpu Ma, Philip Torr, Volker Tresp

Comments Accepted at NeurIPS 2023 Datasets and Benchmarks Track; 9 pages, 5 figures

Landmark Attention: Random-Access Infinite Context Length for Transformers

Amirkeivan Mohtashami, Martin Jaggi

Comments Published as a conference paper at NeurIPS 2023 - 37th Conference on Neural Information Processing Systems

PDP: Parameter-free Differentiable Pruning is All You Need

Minsik Cho, Saurabh Adya, Devang Naik

Journal ref NeurIPS 2023

Balanced Training for Sparse GANs

Yite Wang, Jing Wu, Naira Hovakimyan, Ruoyu Sun

Comments Accepted at NeurIPS 2023 (https://neurips.cc/virtual/2023/poster/70078). Our code will be released at https://github.com/YiteWang/ADAPT

Data Selection for Language Models via Importance Resampling

Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang

Comments NeurIPS 2023

Learning Universal Policies via Text-Guided Video Generation

Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, Pieter Abbeel

Comments NeurIPS 2023, Project Website: https://universal-policy.github.io/

PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits

Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun

Comments 10 pages, 1 figures, published in the 2022 Conference on Neural Information Processing Systems

Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks

Benjamin Feuer, Chinmay Hegde, Niv Cohen

Comments 2nd Table Representation Learning Workshop: 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

Combining astrophysical datasets with CRUMB

Fiona A. M. Porter, Anna M. M. Scaife

Comments Accepted in Machine Learning and the Physical Sciences Workshop at NeurIPS 2023; 6 pages, 1 figure, 1 table

Nonparametric Teaching for Multiple Learners

Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor Tsang, James Kwok

Comments NeurIPS 2023 (31 pages, 20 figures)