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01/24/2023
Macroeconomic forecasting and sovereign risk assessment using deep learning techniques
In this study, we propose a novel approach of nowcasting and forecasting...
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08/02/2022
Stochastic Deep Networks with Linear Competing Units for Model-Agnostic Meta-Learning
This work addresses meta-learning (ML) by considering deep networks with...
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05/28/2022
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling
Sparse modeling for signal processing and machine learning has been at t...
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01/10/2022
Competing Mutual Information Constraints with Stochastic Competition-based Activations for Learning Diversified Representations
This work aims to address the long-established problem of learning diver...
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12/05/2021
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
This work explores the potency of stochastic competition-based activatio...
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09/15/2021
Dialog speech sentiment classification for imbalanced datasets
Speech is the most common way humans express their feelings, and sentime...
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02/11/2021
Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation
Memory-efficient continuous Sign Language Translation is a significant c...
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01/04/2021
Local Competition and Stochasticity for Adversarial Robustness in Deep Learning
This work addresses adversarial robustness in deep learning by consideri...
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06/18/2020
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning
This work attempts to address adversarial robustness of deep networks by...
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02/13/2020
Variational Conditional-Dependence Hidden Markov Models for Human Action Recognition
Hidden Markov Models (HMMs) are a powerful generative approach for model...
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04/24/2019
A Self-Attentive Emotion Recognition Network
Modern deep learning approaches have achieved groundbreaking performance...
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09/17/2018
Quantum Statistics-Inspired Neural Attention
Sequence-to-sequence (encoder-decoder) models with attention constitute ...
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09/04/2018
t-Exponential Memory Networks for Question-Answering Machines
Recent advances in deep learning have brought to the fore models that ca...
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05/23/2018
Amortized Context Vector Inference for Sequence-to-Sequence Networks
Neural attention (NA) is an effective mechanism for inferring complex st...
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05/19/2018
Nonparametric Bayesian Deep Networks with Local Competition
Local competition among neighboring neurons is a common procedure taking...
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02/10/2018
Deep learning with t-exponential Bayesian kitchen sinks
Bayesian learning has been recently considered as an effective means of ...
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06/13/2017