Learning Graphical Models
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting
Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric J. Horvitz, Stefano Ermon
A learned generative model often produces biased statistics relative to the underlying data distribution.
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/
Program Synthesis and Semantic Parsing with Learned Code Idioms
Eui Chul Shin, Miltiadis Allamanis, Marc Brockschmidt, Alex Polozov
Neural Information Processing Systems http://nips.cc/
Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks
Structured prediction energy networks (SPENs) (Belanger & McCallum, 2016) are a type of energy-based model (LeCun et al., 2006) in which inference is done by gradient descent.
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim
Neural Information Processing Systems http://nips.cc/