Papers
11,015 papers found
Unsupervised Model Selection for Variational Disentangled Representation Learning
Sunny Duan, Loic Matthey, Andre Saraiva et al.
V4D: 4D Convolutional Neural Networks for Video-level Representation Learning
Shiwen Zhang, Sheng Guo, Weilin Huang et al.
Variance Reduction With Sparse Gradients
Melih Elibol, Lihua Lei, Michael I. Jordan
Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities
Baichuan Yuan, Xiaowei Wang, Jianxin Ma et al.
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
Hao Zhang, Bo Chen, Long Tian et al.
Variational Recurrent Models for Solving Partially Observable Control Tasks
Dongqi Han, Kenji Doya, Jun Tani
Variational Template Machine for Data-to-Text Generation
Rong Ye, Wenxian Shi, Hao Zhou et al.
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl et al.
Vid2Game: Controllable Characters Extracted from Real-World Videos
Oran Gafni, Lior Wolf, Yaniv Taigman
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan et al.
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Weijie Su, Xizhou Zhu, Yue Cao et al.
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
H. Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg et al.
vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
Alexei Baevski, Steffen Schneider, Michael Auli
Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search
Anji Liu, Jianshu Chen, Mingze Yu et al.
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Allan Zhou, Eric Jang, Daniel Kappler et al.
Weakly Supervised Clustering by Exploiting Unique Class Count
Mustafa Umit Oner, Hwee Kuan Lee, Wing-Kin Sung
Weakly Supervised Disentanglement with Guarantees
Rui Shu, Yining Chen, Abhishek Kumar et al.
What Can Neural Networks Reason About?
Keyulu Xu, Jingling Li, Mozhi Zhang et al.
What graph neural networks cannot learn: depth vs width
Andreas Loukas
White Noise Analysis of Neural Networks
Ali Borji, Sikun Lin
Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity
Jingzhao Zhang, Tianxing He, Suvrit Sra et al.
Why Not to Use Zero Imputation? Correcting Sparsity Bias in Training Neural Networks
Joonyoung Yi, Juhyuk Lee, Kwang Joon Kim et al.
word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement
Aliakbar Panahi, Seyran Saeedi, Tom Arodz
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings
Daniel Ruffinelli, Samuel Broscheit, Rainer Gemulla
You Only Train Once: Loss-Conditional Training of Deep Networks
Alexey Dosovitskiy, Josip Djolonga