ICLR Papers
6,124 papers found • Page 19 of 123
DisPose: Disentangling Pose Guidance for Controllable Human Image Animation
Hongxiang Li, Yaowei Li, Yuhang Yang et al.
Dissecting Adversarial Robustness of Multimodal LM Agents
Chen Wu, Rishi Shah, Jing Yu Koh et al.
Distance-Based Tree-Sliced Wasserstein Distance
Viet-Hoang Tran, Minh-Khoi Nguyen-Nhat, Trang Pham et al.
Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching
Enshu Liu, Xuefei Ning, Yu Wang et al.
DistillHGNN: A Knowledge Distillation Approach for High-Speed Hypergraph Neural Networks
Saman Forouzandeh, Parham Moradi Dowlatabadi, Mahdi Jalili
Distilling Dataset into Neural Field
Donghyeok Shin, HeeSun Bae, Gyuwon Sim et al.
Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning
Jaehyeon Son, Soochan Lee, Gunhee Kim
Distilling Structural Representations into Protein Sequence Models
Jeffrey Ouyang-Zhang, Chengyue Gong, Yue Zhao et al.
Dist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint
Guangkun Nie, Gongzheng Tang, Shenda Hong
Distributed Speculative Inference (DSI): Speculation Parallelism for Provably Faster Lossless Language Model Inference
Nadav Timor, Jonathan Mamou, Daniel Korat et al.
Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers
Lei Chen, Joan Bruna, Alberto Bietti
Distribution Backtracking Builds A Faster Convergence Trajectory for Diffusion Distillation
Shengyuan Zhang, Ling Yang, Zejian Li et al.
Distribution-Free Data Uncertainty for Neural Network Regression
Domokos M. Kelen, Ádám Jung, Péter Kersch et al.
Distribution-Specific Agnostic Conditional Classification With Halfspaces
Jizhou Huang, Brendan Juba
DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agent
Taiyi Wang, Zhihao Wu, Jianheng Liu et al.
DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors
Keon Lee, Dong Won Kim, Jaehyeon Kim et al.
Divergence-enhanced Knowledge-guided Context Optimization for Visual-Language Prompt Tuning
Yilun Li, Miaomiao Cheng, Xu Han et al.
Divergence of Neural Tangent Kernel in Classification Problems
Zixiong Yu, Songtao Tian, Guhan Chen
Divergence-Regularized Discounted Aggregation: Equilibrium Finding in Multiplayer Partially Observable Stochastic Games
Runyu Lu, Yuanheng Zhu, Dongbin Zhao
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
Hanlin Yang, Jian Yao, Weiming Liu et al.
Diverse Preference Learning for Capabilities and Alignment
Stewart Slocum, Asher Parker-Sartori, Dylan Hadfield-Menell
Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents
Kexun Zhang, Weiran Yao, Zuxin Liu et al.
Diversity-Rewarded CFG Distillation
Geoffrey Cideron, Andrea Agostinelli, Johan Ferret et al.
Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning
Hyun Ryu, Gyeongman Kim, Hyemin S. Lee et al.
DLEFT-MKC: Dynamic Late Fusion Multiple Kernel Clustering with Robust Tensor Learning via Min-Max Optimization
Yi Zhang, Siwei Wang, Jiyuan Liu et al.
Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models
Francisco Eiras, Aleksandar Petrov, Philip Torr et al.
Do as We Do, Not as You Think: the Conformity of Large Language Models
Zhiyuan Weng, Guikun Chen, Wenguan Wang
Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives
Qinsi Wang, Jinghan Ke, Masayoshi Tomizuka et al.
DocMIA: Document-Level Membership Inference Attacks against DocVQA Models
Khanh Nguyen, Raouf Kerkouche, Mario Fritz et al.
Do Contemporary Causal Inference Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark
Haining Yu, Yizhou Sun
DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models
Zeping Min, Xinshang Wang
Do Deep Neural Network Solutions Form a Star Domain?
Ankit Sonthalia, Alexander Rubinstein, Ehsan Abbasnejad et al.
Do Egocentric Video-Language Models Truly Understand Hand-Object Interactions?
BOSHEN XU, Ziheng Wang, Yang Du et al.
Does Editing Provide Evidence for Localization?
Zihao Wang, Victor Veitch
Does Refusal Training in LLMs Generalize to the Past Tense?
Maksym Andriushchenko, Nicolas Flammarion
Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts?
Sravanti Addepalli, Yerram Varun, Arun Suggala et al.
Does SGD really happen in tiny subspaces?
Minhak Song, Kwangjun Ahn, Chulhee Yun
Does Spatial Cognition Emerge in Frontier Models?
Santhosh Kumar Ramakrishnan, Erik Wijmans, Philipp Krähenbühl et al.
Does Training with Synthetic Data Truly Protect Privacy?
Yunpeng Zhao, Jie Zhang
DoF: A Diffusion Factorization Framework for Offline Multi-Agent Reinforcement Learning
Chao Li, Ziwei Deng, Chenxing Lin et al.
Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
Javier Ferrando, Oscar Obeso, Senthooran Rajamanoharan et al.
Do Large Language Models Truly Understand Geometric Structures?
Xiaofeng Wang, Yiming Wang, Wenhong Zhu et al.
Do LLM Agents Have Regret? A Case Study in Online Learning and Games
Chanwoo Park, Xiangyu Liu, Asuman Ozdaglar et al.
Do LLMs estimate uncertainty well in instruction-following?
Juyeon Heo, Miao Xiong, Christina Heinze-Deml et al.
Do LLMs have Consistent Values?
Naama Rozen, Liat Bezalel, Gal Elidan et al.
Do LLMs ``know'' internally when they follow instructions?
Juyeon Heo, Christina Heinze-Deml, Oussama Elachqar et al.
Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs
Siyan Zhao, Mingyi Hong, Yang Liu et al.
Domain Guidance: A Simple Transfer Approach for a Pre-trained Diffusion Model
Jincheng Zhong, XiangCheng Zhang, Jianmin Wang et al.
Do Mice Grok? Glimpses of Hidden Progress in Sensory Cortex
Tanishq Kumar, Blake Bordelon, Cengiz Pehlevan et al.
Do not write that jailbreak paper
Javier Rando