116 citations · 132 across the 17 of their papers we have counts for
5 papers · 2 filters
t-Exponential Memory Networks for Question-Answering Machines
Kyriakos Tolias, Sotirios Chatzis
Recent advances in deep learning have brought to the fore models that can make multiple computational steps in the service of completing a task; these are capable of describ- ing l…
Amortized Context Vector Inference for Sequence-to-Sequence Networks
Kyriacos Tolias, Ioannis Kourouklides, Sotirios Chatzis
Neural attention (NA) has become a key component of sequence-to-sequence models that yield state-of-the-art performance in as hard tasks as abstractive document summarization (ADS)…
Nonparametric Bayesian Deep Networks with Local Competition
Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis
The aim of this work is to enable inference of deep networks that retain high accuracy for the least possible model complexity, with the latter deduced from the data during inferen…
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity
Harris Partaourides, Sotirios P. Chatzis
Deep neural networks (DNNs) often require good regularizers to generalize well. Currently, state-of-the-art DNN regularization techniques consist in randomly dropping units and/or…
Deep learning with t-exponential Bayesian kitchen sinks
Harris Partaourides, Sotirios Chatzis
Bayesian learning has been recently considered as an effective means of accounting for uncertainty in trained deep network parameters. This is of crucial importance when dealing wi…