48 citations · 58 across the 15 of their papers we have counts for
16 papers
Lookbehind-SAM: k steps back, 1 step forward
Gonçalo Mordido, Pranshu Malviya, Aristide Baratin +1
Sharpness-aware minimization (SAM) methods have gained increasing popularity by formulating the problem of minimizing both loss value and loss sharpness as a minimax objective. In…
Promoting Exploration in Memory-Augmented Adam using Critical Momenta
Pranshu Malviya, Gonçalo Mordido, Aristide Baratin +5
Adaptive gradient-based optimizers, notably Adam, have left their mark in training large-scale deep learning models, offering fast convergence and robustness to hyperparameter sett…
Should We Attend More or Less? Modulating Attention for Fairness
Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian +1
The advances in natural language processing (NLP) pose both opportunities and challenges. While recent progress enables the development of high-performing models for a variety of t…
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
Abdelrahman Zayed, Prasanna Parthasarathi, Goncalo Mordido +3
Data-driven predictive solutions predominant in commercial applications tend to suffer from biases and stereotypes, which raises equity concerns. Prediction models may discover, us…
SAMSON: Sharpness-Aware Minimization Scaled by Outlier Normalization for Improving DNN Generalization and Robustness
Gonçalo Mordido, Sébastien Henwood, Sarath Chandar +1
Energy-efficient deep neural network (DNN) accelerators are prone to non-idealities that degrade DNN performance at inference time. To mitigate such degradation, existing methods t…
Improving Meta-Learning Generalization with Activation-Based Early-Stopping
Simon Guiroy, Christopher Pal, Gonçalo Mordido +1
Meta-Learning algorithms for few-shot learning aim to train neural networks capable of generalizing to novel tasks using only a few examples. Early-stopping is critical for perform…
MemSE: Fast MSE Prediction for Noisy Memristor-Based DNN Accelerators
Jonathan Kern, Sébastien Henwood, Gonçalo Mordido +4
Memristors enable the computation of matrix-vector multiplications (MVM) in memory and, therefore, show great potential in highly increasing the energy efficiency of deep neural ne…
Compressing 1D Time-Channel Separable Convolutions using Sparse Random Ternary Matrices
Gonçalo Mordido, Matthijs Van Keirsbilck, Alexander Keller
We demonstrate that 1x1-convolutions in 1D time-channel separable convolutions may be replaced by constant, sparse random ternary matrices with weights in . Such layer…
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing
Gonçalo Mordido, Haojin Yang, Christoph Meinel
The analysis of the compression effects in generative adversarial networks (GANs) after training, i.e. without any fine-tuning, remains an unstudied, albeit important, topic with t…
Mark-Evaluate: Assessing Language Generation using Population Estimation Methods
Gonçalo Mordido, Christoph Meinel
We propose a family of metrics to assess language generation derived from population estimation methods widely used in ecology. More specifically, we use mark-recapture and maximum…
Improving the Evaluation of Generative Models with Fuzzy Logic
Julian Niedermeier, Gonçalo Mordido, Christoph Meinel
Objective and interpretable metrics to evaluate current artificial intelligent systems are of great importance, not only to analyze the current state of such systems but also to ob…
microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination
Gonçalo Mordido, Haojin Yang, Christoph Meinel
We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, calle…
Instant Quantization of Neural Networks using Monte Carlo Methods
Gonçalo Mordido, Matthijs Van Keirsbilck, Alexander Keller
Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption. We propose Monte Carlo methods to quan…
Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators
Gonçalo Mordido, Haojin Yang, Christoph Meinel
We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some p…
Automatic Organisation, Segmentation, and Filtering of User-Generated Audio Content
Gonçalo Mordido, João Magalhães, Sofia Cavaco
Using solely the information retrieved by audio fingerprinting techniques, we propose methods to treat a possibly large dataset of user-generated audio content, that (1) enable the…
Automatic Organisation and Quality Analysis of User-Generated Content with Audio Fingerprinting
Gonçalo Mordido, João Magalhães, Sofia Cavaco
The increase of the quantity of user-generated content experienced in social media has boosted the importance of analysing and organising the content by its quality. Here, we propo…