37 citations · 73 across the 7 of their papers we have counts for
7 papers
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network
Yao Feng, Yuhong Jiang, Hang Su +2
Model-based reinforcement learning usually suffers from a high sample complexity in training the world model, especially for the environments with complex dynamics. To make the tra…
Pre-Trained Models: Past, Present and Future
Xu Han, Zhengyan Zhang, Ning Ding +21
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to sophis…
Max-Margin Deep Generative Models for (Semi-)Supervised Learning
Chongxuan Li, Jun Zhu, Bo Zhang
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. How…
Kernel Bayesian Inference with Posterior Regularization
Yang Song, Jun Zhu, Yong Ren
We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution. This equ…
Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields
Ni Lao, Jun Zhu
Structure learning of Conditional Random Fields (CRFs) can be cast into an L1-regularized optimization problem. To avoid optimizing over a fully linked model, gain-based or gradien…
Dropout Training for Support Vector Machines
Ning Chen, Jun Zhu, Jianfei Chen +1
Dropout and other feature noising schemes have shown promising results in controlling over-fitting by artificially corrupting the training data. Though extensive theoretical and em…