Markov Models
Co-Generation with GANs using AIS based HMC
Tiantian Fang, Alexander Schwing
Therefore, in this paper, we study the occurring challenges for co-generation with GANs. To address those challenges we develop an annealed importance sampling based Hamiltonian Monte Carlo co-generation algorithm.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Maxime Gasse, Didier Chetelat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
Neural Information Processing Systems http://nips.cc/
Re-examination of the Role of Latent Variables in Sequence Modeling
Guokun Lai, Zihang Dai, Yiming Yang, Shinjae Yoo
Neural Information Processing Systems http://nips.cc/
A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning
Francisco Garcia, Philip S. Thomas
Neural Information Processing Systems http://nips.cc/
Learning low-dimensional state embeddings and metastable clusters from time series data
Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang
Markov state trajectories, where the transition kernel has a small intrinsic rank. In the spirit of diffusion map, we propose an efficient method for learning a low-dimensional state embedding and capturing the process's dynamics.
Machine Teaching of Active Sequential Learners
Tomi Peltola, Mustafa Mert Çelikok, Pedram Daee, Samuel Kaski
Neural Information Processing Systems http://nips.cc/
Expressive power of tensor-network factorizations for probabilistic modeling
Ivan Glasser, Ryan Sweke, Nicola Pancotti, Jens Eisert, Ignacio Cirac
Many problems in diverse areas of computer science and physics involve constructing efficient representations of high-dimensional functions. Neural networks are a particular example of such representations that have enjoyed great empirical success, and much effort has been dedicated to understanding their expressive power - i.e. the set of functions that they can efficiently represent. Analogously, tensor networks are a class of powerful representations of high-dimensional arrays (tensors), for which a variety of algorithms and methods have been developed.
Variational Temporal Abstraction
Taesup Kim, Sungjin Ahn, Yoshua Bengio
We introduce a variational approach to learning and inference of temporally hierarchical structure and representation for sequential data.
Decoding Communications with Partial Information
Machine language acquisition is often presented as a problem of imitation learning: there exists a community of language users from which a learner observes speech acts and attempts to decode the mappings between utterances and situations. However, an interesting consideration that is typically unaddressed is partial observability, i.e. the learner is assumed to see all relevant information. This paper explores relaxing this assumption, thereby posing a more challenging setting where such information needs to be inferred from knowledge of the environment, the actions taken, and messages sent. We see several motivating examples of this problem, demonstrate how they can be solved in a toy setting, and formally explore challenges that arise in more general settings. A learning-based algorithm is then presented to perform the decoding of private information to facilitate language acquisition.