NEURIPS Poster Papers
4,493 papers found • Page 89 of 90
Watermarking Autoregressive Image Generation
Nikola Jovanović, Ismail Labiad, Tomas Soucek et al.
Wavy Transformer
Satoshi Noguchi, Yoshinobu Kawahara
Weak-shot Keypoint Estimation via Keyness and Correspondence Transfer
Junjie Chen, Zeyu Luo, Zezheng Liu et al.
Weak-to-Strong Generalization under Distribution Shifts
Myeongho Jeon, Jan Sobotka, Suhwan Choi et al.
WearVQA: A Visual Question Answering Benchmark for Wearables in Egocentric Authentic Real-world scenarios
Eun Chang, Zhuangqun Huang, Yiwei Liao et al.
WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization
Jiahao Wen, Hang Yu, Zhedong Zheng
Weaver: Shrinking the Generation-Verification Gap by Scaling Compute for Verification
Jon Saad-Falcon, Estefany Kelly Buchanan, Mayee Chen et al.
WebDancer: Towards Autonomous Information Seeking Agency
Jialong Wu, Baixuan Li, Runnan Fang et al.
Web-Scale Collection of Video Data for 4D Animal Reconstruction
Brian Nlong Zhao, Jiajun Wu, Shangzhe Wu
WebThinker: Empowering Large Reasoning Models with Deep Research Capability
Xiaoxi Li, Jiajie Jin, Guanting Dong et al.
We Should Chart an Atlas of All the World's Models
Eliahu Horwitz, Nitzan Kurer, Jonathan Kahana et al.
What Can RL Bring to VLA Generalization? An Empirical Study
Jijia Liu, Feng Gao, Bingwen Wei et al.
What Data Enables Optimal Decisions? An Exact Characterization for Linear Optimization
Omar Bennouna, Amine Bennouna, Saurabh Amin et al.
What Does It Take to Build a Performant Selective Classifier?
Stephan Rabanser, Nicolas Papernot
What Do Latent Action Models Actually Learn?
Chuheng Zhang, Tim Pearce, Pushi Zhang et al.
What Happens During the Loss Plateau? Understanding Abrupt Learning in Transformers
Pulkit Gopalani, Wei Hu
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
Sang Choe, Hwijeen Ahn, Juhan Bae et al.
WHAT MAKES MATH PROBLEMS HARD FOR REINFORCEMENT LEARNING: A CASE STUDY
Ali Shehper, Anibal Medina-Mardones, Lucas Fagan et al.
What Matters in Data for DPO?
Yu Pan, Zhongze Cai, Huaiyang Zhong et al.
What Really is a Member? Discrediting Membership Inference via Poisoning
Neal Mangaokar, Ashish Hooda, Zhuohang Li et al.
What’s in Common? Multimodal Models Hallucinate When Reasoning Across Scenes
Candace Ross, Florian Bordes, Adina Williams et al.
What's Producible May Not Be Reachable: Measuring the Steerability of Generative Models
Keyon Vafa, Sarah Bentley, Jon Kleinberg et al.
What We Miss Matters: Learning from the Overlooked in Point Cloud Transformers
Yi Wang, Jiaze Wang, Ziyu Guo et al.
When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery
Dominik Meier, Sujai Hiremath, PROMIT GHOSAL et al.
When and how can inexact generative models still sample from the data manifold?
Nisha Chandramoorthy, Adriaan de Clercq
When Are Concepts Erased From Diffusion Models?
Kevin Lu, Nicky Kriplani, Rohit Gandikota et al.
When Can Model-Free Reinforcement Learning be Enough for Thinking?
Josiah Hanna, Nicholas Corrado
When Causal Dynamics Matter: Adapting Causal Strategies through Meta-Aware Interventions
Moritz Willig, Tim Woydt, Devendra Singh Dhami et al.
When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective
Beatrix Nielsen, Emanuele Marconato, Andrea Dittadi et al.
When Does Curriculum Learning Help? A Theoretical Perspective
Raman Arora, Yunjuan Wang, Kaibo Zhang
When Do Transformers Outperform Feedforward and Recurrent Networks? A Statistical Perspective
Alireza Mousavi-Hosseini, Clayton Sanford, Denny Wu et al.
When Kernels Multiply, Clusters Unify: Fusing Embeddings with the Kronecker Product
Youqi WU, Jingwei Zhang, Farzan Farnia
When Lower-Order Terms Dominate: Adaptive Expert Algorithms for Heavy-Tailed Losses
Antoine Moulin, Emmanuel Esposito, Dirk van der Hoeven
When majority rules, minority loses: bias amplification of gradient descent
François Bachoc, Jerome Bolte, Ryan Boustany et al.
When Models Don’t Collapse: On the Consistency of Iterative MLE
Daniel Barzilai, Ohad Shamir
When No Paths Lead to Rome: Benchmarking Systematic Neural Relational Reasoning
Anirban Das, Muhammad Irtaza Khalid, Rafael Peñaloza et al.
When Semantics Mislead Vision: Mitigating Large Multimodal Models Hallucinations in Scene Text Spotting and Understanding
Yan Shu, Hangui Lin, Yexin Liu et al.
When Thinking Drifts: Evidential Grounding for Robust Video Reasoning
Romy Luo, Zihui (Sherry) Xue, Alex Dimakis et al.
Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models
Donghoon Ahn, Jiwon Kang, Sanghyun Lee et al.
Where Graph Meets Heterogeneity: Multi-View Collaborative Graph Experts
Zhihao Wu, Jinyu Cai, Yunhe Zhang et al.
Which Data Attributes Stimulate Math and Code Reasoning? An Investigation via Influence Functions
Siqi Kou, Qingyuan Tian, Hanwen Xu et al.
Whitened Score Diffusion: A Structured Prior for Imaging Inverse Problems
Jeffrey Alido, Tongyu Li, Yu Sun et al.
Whole-Body Conditioned Egocentric Video Prediction
Yutong Bai, Danny Tran, Amir Bar et al.
Who Reasons in the Large Language Models?
Jie Shao, Jianxin Wu
Whose Instructions Count? Resolving Preference Bias in Instruction Fine-Tuning
Jiayu Zhang, Changbang Li, Yinan Peng et al.
Who Speaks for the Trigger? Dynamic Expert Routing in Backdoored Mixture-of-Experts Transformers
Xin Zhao, Xiaojun Chen, Bingshan Liu et al.
Why 1 + 1 < 1 in Visual Token Pruning: Beyond Naive Integration via Multi-Objective Balanced Covering
Yangfu Li, Hongjian Zhan, Tianyi Chen et al.
Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations
Yiyou Sun, Yu Gai, Lijie Chen et al.
Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation
Sungmin Cha, Kyunghyun Cho
Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion
Alan Amin, Nate Gruver, Andrew Wilson