Papers
11,955 papers found
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI
Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
Distilling Knowledge from Large-Scale Image Models for Object Detection
Gang Li, Wenhai Wang, Xiang Li et al.
DistillSpec: Improving Speculative Decoding via Knowledge Distillation
Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat et al.
Distinguished In Uniform: Self-Attention Vs. Virtual Nodes
Eran Rosenbluth, Jan Tönshoff, Martin Ritzert et al.
Distributed Semantic Segmentation with Efficient Joint Source and Task Decoding
Danish Nazir, Timo Bartels, Jan Piewek et al.
Distributionally Robust Optimization with Bias and Variance Reduction
Ronak Mehta, Vincent Roulet, Krishna Pillutla et al.
Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF
Anand Siththaranjan, Cassidy Laidlaw, Dylan Hadfield-Menell
DittoGym: Learning to Control Soft Shape-Shifting Robots
Suning Huang, Boyuan Chen, Huazhe Xu et al.
Diverse Projection Ensembles for Distributional Reinforcement Learning
Moritz Akiya Zanger, Wendelin Boehmer, Matthijs T. J. Spaan
Divide and not forget: Ensemble of selectively trained experts in Continual Learning
Grzegorz Rypeść, Sebastian Cygert, Valeriya Khan et al.
Diving Segmentation Model into Pixels
Chen Gan, Zihao Yin, Kelei He et al.
DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model
Yinghao Xu, Hao Tan, Fujun Luan et al.
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes
Zhihan Zhou, Yanrong Ji, Weijian Li et al.
DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text
Xianjun Yang, Wei Cheng, Yue Wu et al.
Does CLIP’s generalization performance mainly stem from high train-test similarity?
Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak et al.
Does Progress On Object Recognition Benchmarks Improve Generalization on Crowdsourced, Global Data?
Megan Richards, Polina Kirichenko, Diane Bouchacourt et al.
Does Writing with Language Models Reduce Content Diversity?
Vishakh Padmakumar, He He
Do Generated Data Always Help Contrastive Learning?
Yifei Wang, Jizhe Zhang, Yisen Wang
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models
Yung-Sung Chuang, Yujia Xie, Hongyin Luo et al.
Domain-Agnostic Molecular Generation with Chemical Feedback
Yin Fang, Ningyu Zhang, Zhuo Chen et al.
Domain constraints improve risk prediction when outcome data is missing
Sidhika Balachandar, Nikhil Garg, Emma Pierson
Domain-Inspired Sharpness-Aware Minimization Under Domain Shifts
Ruipeng Zhang, Ziqing Fan, Jiangchao Yao et al.
Domain Randomization via Entropy Maximization
Gabriele Tiboni, Pascal Klink, Jan Peters et al.