Papers
11,951 papers found
Transformer Embeddings of Irregularly Spaced Events and Their Participants
Hongyuan Mei, Chenghao Yang, Jason Eisner
Transformers Can Do Bayesian Inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango et al.
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models
Chaoyue Liu, Libin Zhu, Misha Belkin
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
Alexander Thebelt, Calvin Tsay, Robert Lee et al.
TRGP: Trust Region Gradient Projection for Continual Learning
Sen Lin, Li Yang, Deliang Fan et al.
Triangle and Four Cycle Counting with Predictions in Graph Streams
Justin Y Chen, Talya Eden, Piotr Indyk et al.
Trigger Hunting with a Topological Prior for Trojan Detection
Xiaoling Hu, Xiao Lin, Michael Cogswell et al.
Trivial or Impossible --- dichotomous data difficulty masks model differences (on ImageNet and beyond)
Kristof Meding, Luca M. Schulze Buschoff, Robert Geirhos et al.
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen et al.
Tuformer: Data-driven Design of Transformers for Improved Generalization or Efficiency
Xiaoyu Liu, Jiahao Su, Furong Huang
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
Minhao LIU, Ailing Zeng, Qiuxia LAI et al.
Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime
Naoki Nishikawa, Taiji Suzuki, Atsushi Nitanda et al.
Uncertainty Modeling for Out-of-Distribution Generalization
Xiaotong Li, Yongxing Dai, Yixiao Ge et al.
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Yongqiang Chen, Han Yang, Yonggang Zhang et al.
Understanding and Leveraging Overparameterization in Recursive Value Estimation
Chenjun Xiao, Bo Dai, Jincheng Mei et al.
Understanding and Preventing Capacity Loss in Reinforcement Learning
Clare Lyle, Mark Rowland, Will Dabney
Understanding approximate and unrolled dictionary learning for pattern recovery
Benoît Malézieux, Thomas Moreau, Matthieu Kowalski
Understanding Dimensional Collapse in Contrastive Self-supervised Learning
Li Jing, Pascal Vincent, Yann LeCun et al.
Understanding Domain Randomization for Sim-to-real Transfer
Xiaoyu Chen, Jiachen Hu, Chi Jin et al.
Understanding Intrinsic Robustness Using Label Uncertainty
Xiao Zhang, David Evans
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective
Qi Lyu, Xiao Fu, Weiran Wang et al.
Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain et al.
Understanding the Role of Self Attention for Efficient Speech Recognition
Kyuhong Shim, Jungwook Choi, Wonyong Sung
Understanding the Variance Collapse of SVGD in High Dimensions
Jimmy Ba, Murat A Erdogdu, Marzyeh Ghassemi et al.
Unified Visual Transformer Compression
Shixing Yu, Tianlong Chen, Jiayi Shen et al.