Learning Graphical Models
Deep Generative Markov State Models
Hao Wu, Andreas Mardt, Luca Pasquali, Frank Noe
We propose a deep generative Markov State Model (DeepGenMSM) learningframework for inference of metastable dynamical systems and prediction of tra-jectories.
rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions
Mathieu Fehr, Olivier Buffet, Vincent Thomas, Jilles Dibangoye
Neural Information Processing Systems http://nips.cc/
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan
Neural Information Processing Systems http://nips.cc/
Towards Hardware-AwareTractable LearningofProbabilisticModels
Smart portable applications increasingly rely on edge computing due to privacy andlatencyconcerns.
Constraint-based Causal Structure Learning with Consistent Separating Sets
This paper concerns, more specifically, the inconsistency of separating sets used to remove dispensable edges, iteratively, based on conditional independence tests.
Deep Generative Models with Learnable Knowledge Constraints
Zhiting Hu, Zichao Yang, Russ R. Salakhutdinov, LIANHUI Qin, Xiaodan Liang, Haoye Dong, Eric P. Xing
Neural Information Processing Systems http://nips.cc/
Predictive Attractor Models
The task of sequential memory is considered challenging for models operating under biological constraints (i.e., local synaptic computations) for many reasons, including catastrophic forgetting,