Deep Learning theory Accepted Papers
#ICML2018

An explicit expression for the global minimizer network
Deep Neural Networks Learn Non-Smooth Functions Effectively
Understanding Deep Neural Networks with Renyi’s α-entropy Functional
Depth Efficiency of Deep Mixture Models and Sum-Product Networks using Tensor Analysis
Universal approximations of invariant maps by neural networks
Information based regularization for deep learning
Loss-Calibrated Approximate Inference in Bayesian Neural Networks
A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs
Difficulties in Optimising Feedforward Neural Networks
Homotopic deep recurrent neural networks for approximating meta-heuristics
On the Analysis of Trajectories of Gradient Descent in the Optimization of Deep Neural Networks