8 citations · 14 across the 13 of their papers we have counts for
13 papers · 1 filter
Lévy Attention: Single-Pass Predictive Uncertainty for Continuous-Time Attention
Sotirios P. Chatzis, Loukas Papadoulas
Deep models for irregularly-sampled time series answer queries at arbitrary continuous timestamps, yet report nothing about how far each answer should be trusted. We show the atten…
Transformers with Stochastic Competition for Tabular Data Modelling
Andreas Voskou, Charalambos Christoforou, Sotirios Chatzis
Despite the prevalence and significance of tabular data across numerous industries and fields, it has been relatively underexplored in the realm of deep learning. Even today, neura…
Continual Deep Learning on the Edge via Stochastic Local Competition among Subnetworks
Theodoros Christophides, Kyriakos Tolias, Sotirios Chatzis
Continual learning on edge devices poses unique challenges due to stringent resource constraints. This paper introduces a novel method that leverages stochastic competition princip…
Competing Mutual Information Constraints with Stochastic Competition-based Activations for Learning Diversified Representations
Konstantinos P. Panousis, Anastasios Antoniadis, Sotirios Chatzis
This work aims to address the long-established problem of learning diversified representations. To this end, we combine information-theoretic arguments with stochastic competition-…
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning
Konstantinos P. Panousis, Sotirios Chatzis, Antonios Alexos +1
This work addresses adversarial robustness in deep learning by considering deep networks with stochastic local winner-takes-all (LWTA) activations. This type of network units resul…
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning
Antonios Alexos, Konstantinos P. Panousis, Sotirios Chatzis
This work attempts to address adversarial robustness of deep networks by means of novel learning arguments. Specifically, inspired from results in neuroscience, we propose a local…