All Papers
34,598 papers found • Page 689 of 692
Conference
Weighted distance nearest neighbor condensing
Lee-Ad Gottlieb, Timor Sharabi, Roi Weiss
Weighted Ensemble Models Are Strong Continual Learners
Imad Eddine Marouf, Subhankar Roy, Enzo Tartaglione et al.
Weighted Envy-Freeness for Submodular Valuations
Luisa Montanari, Ulrike Schmidt-Kraepelin, Warut Suksompong et al.
Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation
Prantik Howlader, Hieu Le, Dimitris Samaras
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
Quang Truong, Peter Chin
Weisfeiler-Leman at the margin: When more expressivity matters
Billy Franks, Christopher Morris, Ameya Velingker et al.
Weisfeiler Leman for Euclidean Equivariant Machine Learning
Snir Hordan, Tal Amir, Nadav Dym
Well, Now We Know! Unveiling Sarcasm: Initiating and Exploring Multimodal Conversations with Reasoning
Gopendra Singh, Mauajama Firdaus, Dushyant Singh Chauhan et al.
WHAC: World-grounded Humans and Cameras
Wanqi Yin, Zhongang Cai, Chen Wei et al.
WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion
Soyong Shin, Juyong Kim, Eni Halilaj et al.
What Algorithms can Transformers Learn? A Study in Length Generalization
Hattie Zhou, Arwen Bradley, Etai Littwin et al.
What Are the Rules? Discovering Constraints from Data
Boris Wiegand, Dietrich Klakow, Jilles Vreeken
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks
Xingwu Chen, Difan Zou
"What Data Benefits My Classifier?" Enhancing Model Performance and Interpretability through Influence-Based Data Selection
Anshuman Chhabra, Peizhao Li, Prasant Mohapatra et al.
What Does a Query Answer Tell You? Informativeness of Query Answers for Knowledge Bases
Luca Andolfi, Gianluca Cima, Marco Console et al.
What does automatic differentiation compute for neural networks?
Sejun Park, Sanghyuk Chun, Wonyeol Lee
What does the Knowledge Neuron Thesis Have to do with Knowledge?
Jingcheng Niu, Andrew Liu, Zining Zhu et al.
What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation
Yihua Cheng, Yaning Zhu, Zongji Wang et al.
What Effects the Generalization in Visual Reinforcement Learning: Policy Consistency with Truncated Return Prediction
Shuo Wang, Zhihao Wu, X. Hu et al.
What How and When Should Object Detectors Update in Continually Changing Test Domains?
Jayeon Yoo, Dongkwan Lee, Inseop Chung et al.
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models
Letian Zhang, Xiaotong Zhai, Zhongkai Zhao et al.
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
Hongkang Li, Meng Wang, Tengfei Ma et al.
What is Dataset Distillation Learning?
William Yang, Ye Zhu, Zhiwei Deng et al.
What is the Long-Run Distribution of Stochastic Gradient Descent? A Large Deviations Analysis
Waïss Azizian, Franck Iutzeler, Jérôme Malick et al.
What Makes a Good Prune? Maximal Unstructured Pruning for Maximal Cosine Similarity
Gabryel Mason-Williams, Fredrik Dahlqvist
What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception
Wanfang Su, Lixing Chen, Yang Bai et al.
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
Wei Liu, Weihao Zeng, Keqing He et al.
What Makes Quantization for Large Language Model Hard? An Empirical Study from the Lens of Perturbation
Huankang Guan, Rynson W.H. Lau
What Matters to You? Towards Visual Representation Alignment for Robot Learning
Thomas Tian, Chenfeng Xu, Masayoshi Tomizuka et al.
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation
Aaditya Singh, Ted Moskovitz, Feilx Hill et al.
What's in a Prior? Learned Proximal Networks for Inverse Problems
Zhenghan Fang, Sam Buchanan, Jeremias Sulam
What's In My Big Data?
Yanai Elazar, Akshita Bhagia, Ian Magnusson et al.
What Sketch Explainability Really Means for Downstream Tasks?
Hmrishav Bandyopadhyay, Pinaki Nath Chowdhury, Ayan Kumar Bhunia et al.
What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions
raghav singhal, Mark Goldstein, Rajesh Ranganath
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
XiaoHui Zhang, Jiangyan Yi, Chenglong Wang et al.
What When and Where? Self-Supervised Spatio-Temporal Grounding in Untrimmed Multi-Action Videos from Narrated Instructions
Brian Chen, Nina Shvetsova, Andrew Rouditchenko et al.
What Will My Model Forget? Forecasting Forgotten Examples in Language Model Refinement
Xisen Jin, Xiang Ren
What Would Gauss Say About Representations? Probing Pretrained Image Models using Synthetic Gaussian Benchmarks
Ching-Yun (Irene) Ko, Pin-Yu Chen, Payel Das et al.
What You See is What You GAN: Rendering Every Pixel for High-Fidelity Geometry in 3D GANs
Alex Trevithick, Matthew Chan, Towaki Takikawa et al.
When and How Does In-Distribution Label Help Out-of-Distribution Detection?
Xuefeng Du, Yiyou Sun, Sharon Li
When and How do negative prompts take effect?
Yuanhao Ban, Ruochen Wang, Tianyi Zhou et al.
When Are Two Lists Better than One?: Benefits and Harms in Joint Decision-Making
Kate Donahue, Sreenivas Gollapudi, Kostas Kollias
When can transformers reason with abstract symbols?
Enric Boix-Adserà, Omid Saremi, Emmanuel Abbe et al.
When CEGAR Meets Regression: A Love Story in Optimal Classical Planning
Martín Pozo, Alvaro Torralba, Carlos Linares Lopez
When Do Program-of-Thought Works for Reasoning?
Zhen Bi, Ningyu Zhang, Yinuo Jiang et al.
When Do Prompting and Prefix-Tuning Work? A Theory of Capabilities and Limitations
Aleksandar Petrov, Philip Torr, Adel Bibi
When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
Zhening Li, Gabriel Poesia, Armando Solar-Lezama
When Do We Not Need Larger Vision Models?
Baifeng Shi, Ziyang Wu, Maolin Mao et al.
When Fast Fourier Transform Meets Transformer for Image Restoration
xingyu jiang, Xiuhui Zhang, Ning Gao et al.
When is Transfer Learning Possible?
My Phan, Kianté Brantley, Stephanie Milani et al.