Papers
11,955 papers found
Turning large language models into cognitive models
Marcel Binz, Eric Schulz
TUVF: Learning Generalizable Texture UV Radiance Fields
An-Chieh Cheng, Xueting Li, Sifei Liu et al.
Two-stage LLM Fine-tuning with Less Specialization and More Generalization
Yihan Wang, Si Si, Daliang Li et al.
Two-timescale Extragradient for Finding Local Minimax Points
Jiseok Chae, Kyuwon Kim, Donghwan Kim
UC-NERF: Neural Radiance Field for Under-Calibrated Multi-View Cameras in Autonomous Driving
Kai Cheng, Xiaoxiao Long, Wei Yin et al.
UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Framework
Tarun Kalluri, Sreyas Ravichandran, Manmohan Chandraker
UMERegRobust – Universal Manifold Embedding Compatible Features for Robust Point Cloud Registration
Yuval Haitman, Amit Efraim, Joseph M Francos
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
Luca Eyring, Dominik Klein, Théo Uscidda et al.
Unbiased Watermark for Large Language Models
Zhengmian Hu, Lichang Chen, Xidong Wu et al.
Uncertainty-aware Constraint Inference in Inverse Constrained Reinforcement Learning
Sheng Xu, Guiliang Liu
Uncertainty-aware Graph-based Hyperspectral Image Classification
Linlin Yu, Yifei Lou, Feng Chen
Uncertainty Quantification via Stable Distribution Propagation
Felix Petersen, Aashwin Ananda Mishra, Hilde Kuehne et al.
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning
James Chapman, Lennie Wells, Ana Lawry Aguila
Understanding Addition in Transformers
Philip Quirke, Fazl Barez
Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks
Hao Chen, Jindong Wang, Ankit Shah et al.
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
Runtian Zhai, Bingbin Liu, Andrej Risteski et al.
Understanding Catastrophic Forgetting in Language Models via Implicit Inference
Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan
Understanding Certified Training with Interval Bound Propagation
Yuhao Mao, Mark Niklas Mueller, Marc Fischer et al.
Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory
Wei Huang, Ye Shi, Zhongyi Cai et al.
Understanding Domain Generalization: A Noise Robustness Perspective
Rui Qiao, Bryan Kian Hsiang Low
Understanding Expressivity of GNN in Rule Learning
Haiquan Qiu, Yongqi Zhang, Yong Li et al.
Understanding In-Context Learning from Repetitions
Jianhao Yan, Jin Xu, Chiyu Song et al.
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions
Satwik Bhattamishra, Arkil Patel, Phil Blunsom et al.
Understanding prompt engineering may not require rethinking generalization
Victor Akinwande, Yiding Jiang, Dylan Sam et al.
Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation
Noel Loo, Ramin Hasani, Mathias Lechner et al.