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Found 20 papers, 5 papers with code
A New Dataset for End-to-End Sign Language Translation: The Greek Elementary School Dataset
A characteristic example is Phoenix2014T benchmark dataset, which only covers weather forecasts in German Sign Language.
Macroeconomic forecasting and sovereign risk assessment using deep learning techniques
In this study, we propose a novel approach of nowcasting and forecasting the macroeconomic status of a country using deep learning techniques.
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
This work addresses meta-learning (ML) by considering deep networks with stochastic local winner-takes-all (LWTA) activations.
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling
However, a come back of Bayesian methods is taking place that sheds new light on the design of deep neural networks, which also establish firm links with Bayesian models and inspire new paths for unsupervised learning, such as Bayesian tensor decomposition.
Competing Mutual Information Constraints with Stochastic Competition-based Activations for Learning Diversified Representations
To this end, we combine information-theoretic arguments with stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA) units.
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
This work explores the potency of stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA), against powerful (gradient-based) white-box and black-box adversarial attacks; we especially focus on Adversarial Training settings.
Ranked #2 on
Adversarial Robustness
on CIFAR-10
Dialog speech sentiment classification for imbalanced datasets
Speech is the most common way humans express their feelings, and sentiment analysis is the use of tools such as natural language processing and computational algorithms to identify the polarity of these feelings.
Stochastic Transformer Networks with Linear Competing Units: Application to end-to-end SL Translation
In this paper, we attenuate this need, by introducing an end-to-end SLT model that does not entail explicit use of glosses; the model only needs text groundtruth.
Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation
Memory-efficient continuous Sign Language Translation is a significant challenge for the development of assisted technologies with real-time applicability for the deaf.
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning
The main operating principle of the introduced units lies on stochastic arguments, as the network performs posterior sampling over competing units to select the winner.
A Deep Learning Approach for Dynamic Balance Sheet Stress Testing
Current stress testing methodologies attempt to simulate the risks underlying a financial institution's balance sheet by using several satellite models.
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning
This work attempts to address adversarial robustness of deep networks by means of novel learning arguments.
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition
Hidden Markov Models (HMMs) comprise a powerful generative approach for modeling sequential data and time-series in general.
A Self-Attentive Emotion Recognition Network
Modern deep learning approaches have achieved groundbreaking performance in modeling and classifying sequential data.
Quantum Statistics-Inspired Neural Attention
Specifically, our work broadens the notion of NA, by attempting to account for the case that the NA model becomes inherently incapable of discerning between individual source elements; this is assumed to be the case due to higher-order temporal dynamics.
t-Exponential Memory Networks for Question-Answering Machines
Our work addresses learning subtler and more complex underlying temporal dynamics in language modeling tasks that deal with sparse sequential data.
Amortized Context Vector Inference for Sequence-to-Sequence Networks
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) and video captioning (VC).
Nonparametric Bayesian Deep Networks with Local Competition
To this end, we revisit deep networks that comprise competing linear units, as opposed to nonlinear units that do not entail any form of (local) competition.
Deep learning with t-exponential Bayesian kitchen sinks
We effect model training by means of approximate inference based on a t-divergence measure; this generalizes the Kullback-Leibler divergence in the context of the t-exponential family of distributions.
Recurrent Latent Variable Networks for Session-Based Recommendation
In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation.
