Papers
Variational Feature Pyramid Networks
Panagiotis Dimitrakopoulos, Giorgos Sfikas, Christophoros Nikou
Variational Inference for Infinitely Deep Neural Networks
Achille Nazaret, David Blei
Variational Inference with Locally Enhanced Bounds for Hierarchical Models
Tomas Geffner, Justin Domke
Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics
Yichen Gu, David T Blaauw, Joshua Welch
Variational nearest neighbor Gaussian process
Luhuan Wu, Geoff Pleiss, John P Cunningham
Variational On-the-Fly Personalization
Jangho Kim, Jun-Tae Lee, Simyung Chang et al.
Variational Sparse Coding with Learned Thresholding
Kion Fallah, Christopher J Rozell
Variational Wasserstein gradient flow
Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei et al.
VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty
Randy Ardywibowo, Zepeng Huo, Zhangyang Wang et al.
VarScene: A Deep Generative Model for Realistic Scene Graph Synthesis
Tathagat Verma, Abir De, Yateesh Agrawal et al.
Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences
Aadirupa Saha, Pierre Gaillard
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching
Yecheng Ma, Andrew Shen, Dinesh Jayaraman et al.
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
Zhenheng Tang, Yonggang Zhang, Shaohuai Shi et al.
Visual Attention Emerges from Recurrent Sparse Reconstruction
Baifeng Shi, Yale Song, Neel Joshi et al.
ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder
Sangwon Kim, Jaeyeal Nam, Byoung Chul Ko
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix
Teng Wang, Wenhao Jiang, Zhichao Lu et al.
VLUE: A Multi-Task Multi-Dimension Benchmark for Evaluating Vision-Language Pre-training
Wangchunshu Zhou, Yan Zeng, Shizhe Diao et al.
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
Gregory Benton, Wesley Maddox, Andrew Gordon Wilson
Weisfeiler-Lehman Meets Gromov-Wasserstein
Samantha Chen, Sunhyuk Lim, Facundo Memoli et al.
Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy
Zhihan Liu, Miao Lu, Zhaoran Wang et al.
What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?
Tiffany J Vlaar, Jonathan Frankle
What Dense Graph Do You Need for Self-Attention?
Yuxin Wang, Chu-Tak Lee, Qipeng Guo et al.
What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?
Thomas Wang, Adam Roberts, Daniel Hesslow et al.
When and How Mixup Improves Calibration
Linjun Zhang, Zhun Deng, Kenji Kawaguchi et al.
When Are Linear Stochastic Bandits Attackable?
Huazheng Wang, Haifeng Xu, Hongning Wang