22 citations · 108 across the 43 of their papers we have counts for
7 papers · 1 filter
Auto-Compressing Networks
Vaggelis Dorovatas, Georgios Paraskevopoulos, Alexandros Potamianos
Deep neural networks with short residual connections have demonstrated remarkable success across domains, but increasing depth often introduces computational redundancy without cor…
Y-Drop: A Conductance based Dropout for fully connected layers
Efthymios Georgiou, Georgios Paraskevopoulos, Alexandros Potamianos
In this work, we introduce Y-Drop, a regularization method that biases the dropout algorithm towards dropping more important neurons with higher probability. The backbone of our ap…
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks
Nikolaos Antoniou, Efthymios Georgiou, Alexandros Potamianos
Designing powerful adversarial attacks is of paramount importance for the evaluation of -bounded adversarial defenses. Projected Gradient Descent (PGD) is one of the most e…
MMLatch: Bottom-up Top-down Fusion for Multimodal Sentiment Analysis
Georgios Paraskevopoulos, Efthymios Georgiou, Alexandros Potamianos
Current deep learning approaches for multimodal fusion rely on bottom-up fusion of high and mid-level latent modality representations (late/mid fusion) or low level sensory inputs…
Unsupervised low-rank representations for speech emotion recognition
Georgios Paraskevopoulos, Efthymios Tzinis, Nikolaos Ellinas +2
We examine the use of linear and non-linear dimensionality reduction algorithms for extracting low-rank feature representations for speech emotion recognition. Two feature sets are…
Attention-based Conditioning Methods for External Knowledge Integration
Katerina Margatina, Christos Baziotis, Alexandros Potamianos
In this paper, we present a novel approach for incorporating external knowledge in Recurrent Neural Networks (RNNs). We propose the integration of lexicon features into the self-at…
Pattern Search Multidimensional Scaling
Georgios Paraskevopoulos, Efthymios Tzinis, Emmanouil-Vasileios Vlatakis-Gkaragkounis +1
We present a novel view of nonlinear manifold learning using derivative-free optimization techniques. Specifically, we propose an extension of the classical multi-dimensional scali…