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01/16/2023
Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach
The field of visual few-shot classification aims at transferring the sta...
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12/13/2022
A Statistical Model for Predicting Generalization in Few-Shot Classification
The estimation of the generalization error of classifiers often relies o...
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09/23/2022
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings
We consider a novel formulation of the problem of Active Few-Shot Classi...
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09/18/2022
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
Transductive Few-Shot learning has gained increased attention nowadays c...
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08/07/2022
Preserving Fine-Grain Feature Information in Classification via Entropic Regularization
Labeling a classification dataset implies to define classes and associat...
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06/13/2022
Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU
Deep neural networks are the state of the art in many computer vision ta...
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06/13/2022
Leveraging Structured Pruning of Convolutional Neural Networks
Structured pruning is a popular method to reduce the cost of convolution...
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03/09/2022
Pruning Graph Convolutional Networks to select meaningful graph frequencies for fMRI decoding
Graph Signal Processing is a promising framework to manipulate brain sig...
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01/24/2022
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Few-shot learning aims at leveraging knowledge learned by one or more de...
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01/12/2022
Preventing Manifold Intrusion with Locality: Local Mixup
Mixup is a data-dependent regularization technique that consists in line...
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10/18/2021
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
Few-shot classification is a challenging problem due to the uncertainty ...
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10/08/2021
Graphs as Tools to Improve Deep Learning Methods
In recent years, deep neural networks (DNNs) have known an important ris...
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08/23/2021
Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification
It is very common to face classification problems where the number of av...
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05/27/2021
Quantization and Deployment of Deep Neural Networks on Microcontrollers
Embedding Artificial Intelligence onto low-power devices is a challengin...
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05/11/2021
Using Deep Neural Networks to Predict and Improve the Performance of Polar Codes
Polar codes can theoretically achieve very competitive Frame Error Rates...
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02/18/2021
Inferring Graph Signal Translations as Invariant Transformations for Classification Tasks
The field of Graph Signal Processing (GSP) has proposed tools to general...
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01/12/2021
Improving Classification Accuracy with Graph Filtering
In machine learning, classifiers are typically susceptible to noise in t...
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12/02/2020
DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems
Learning deep representations to solve complex machine learning tasks ha...
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11/25/2020
Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs
Measuring the generalization performance of a Deep Neural Network (DNN) ...
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11/20/2020
Continuous Pruning of Deep Convolutional Networks Using Selective Weight Decay
During the last decade, deep convolutional networks have become the refe...
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11/14/2020
Representing Deep Neural Networks Latent Space Geometries with Graphs
Deep Learning (DL) has attracted a lot of attention for its ability to r...
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10/23/2020
Few-shot Learning for Decoding Brain Signals
Few-shot learning consists in addressing data-thrifty (inductive few-sho...
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09/30/2020
Some Remarks on Replicated Simulated Annealing
Recently authors have introduced the idea of training discrete weights n...
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09/04/2020
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning
Few-shot classification is a challenge in machine learning where the goa...
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07/20/2020
ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget
Typical deep convolutional architectures present an increasing number of...
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07/16/2020
Graph topology inference benchmarks for machine learning
Graphs are nowadays ubiquitous in the fields of signal processing and ma...
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07/08/2020
Predicting the Accuracy of a Few-Shot Classifier
In the context of few-shot learning, one cannot measure the generalizati...
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06/09/2020
Towards an Intrinsic Definition of Robustness for a Classifier
The robustness of classifiers has become a question of paramount importa...
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06/06/2020
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
Few-shot classification is a challenging problem due to the uncertainty ...
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02/08/2020
BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization
Neural networks have demonstrably achieved state-of-the art accuracy usi...
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01/27/2020
Exploiting Unsupervised Inputs for Accurate Few-Shot Classification
In few-shot classification, the aim is to learn models able to discrimin...
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11/23/2019
Training Modern Deep Neural Networks for Memory-Fault Robustness
Because deep neural networks (DNNs) rely on a large number of parameters...
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11/18/2019
Efficient Hardware Implementation of Incremental Learning and Inference on Chip
In this paper, we tackle the problem of incrementally learning a classif...
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11/08/2019
Deep geometric knowledge distillation with graphs
In most cases deep learning architectures are trained disregarding the a...
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11/07/2019
Improved Visual Localization via Graph Smoothing
Vision based localization is the problem of inferring the pose of the ca...
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09/11/2019
Structural Robustness for Deep Learning Architectures
Deep Networks have been shown to provide state-of-the-art performance in...
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08/19/2019
Comparing linear structure-based and data-driven latent spatial representations for sequence prediction
Predicting the future of Graph-supported Time Series (GTS) is a key chal...
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05/29/2019
Attention Based Pruning for Shift Networks
In many application domains such as computer vision, Convolutional Layer...
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05/01/2019
A Unified Deep Learning Formalism For Processing Graph Signals
Convolutional Neural Networks are very efficient at processing signals d...
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05/01/2019
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
We introduce a novel loss function for training deep learning architectu...
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04/04/2019
Transfer Learning with Sparse Associative Memories
In this paper, we introduce a novel layer designed to be used as the out...
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12/29/2018
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous co...
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10/04/2018
Transfer Incremental Learning using Data Augmentation
Deep learning-based methods have reached state of the art performances, ...
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05/24/2018
Laplacian Power Networks: Bounding Indicator Function Smoothness for Adversarial Defense
Deep Neural Networks often suffer from lack of robustness to adversarial...
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02/27/2018
Matching Convolutional Neural Networks without Priors about Data
We propose an extension of Convolutional Neural Networks (CNNs) to graph...
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10/27/2017
Convolutional neural networks on irregular domains through approximate translations on inferred graphs
We propose a generalization of convolutional neural networks (CNNs) to i...
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10/24/2017
Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation
Transfer learning using deep neural networks as feature extractors has b...
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09/25/2017
Robust Associative Memories Naturally Occuring From Recurrent Hebbian Networks Under Noise
The brain is a noisy system subject to energy constraints. These facts a...
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09/12/2017
Translations on graphs with neighborhood preservation
In the field of graph signal processing, defining translation operators ...
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06/08/2017
