Vincent Gripon

Orcid: 0000-0002-4353-4542

According to our database1, Vincent Gripon authored at least 142 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Local Mixup: Interpolation of closest input signals to prevent manifold intrusion.
Signal Process., 2024

Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning.
CoRR, 2024

LLM meets Vision-Language Models for Zero-Shot One-Class Classification.
CoRR, 2024

Few and Fewer: Learning Better from Few Examples Using Fewer Base Classes.
CoRR, 2024

A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models.
CoRR, 2024

Design Environment of Quantization-Aware Edge AI Hardware for Few-Shot Learning.
Proceedings of the 67th IEEE International Midwest Symposium on Circuits and Systems, 2024

BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2024

Unsupervised Adaptive Deep Learning Method for BCI Motor Imagery Decoding.
Proceedings of the 32nd European Signal Processing Conference, 2024

On Transfer in Classification: How Well do Subsets of Classes Generalize?
Proceedings of the 32nd European Signal Processing Conference, 2024

2023
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning.
CoRR, 2023

ThinResNet: A New Baseline for Structured Convolutional Networks Pruning.
CoRR, 2023

A Strong and Simple Deep Learning Baseline for BCI MI Decoding.
CoRR, 2023

Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach.
CoRR, 2023

Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities.
Proceedings of the IEEE International Conference on Acoustics, 2023

Entropy Based Feature Regularization to Improve Transferability of Deep Learning Models.
Proceedings of the IEEE International Conference on Acoustics, 2023

Active Learning for Efficient Few-Shot Classification.
Proceedings of the IEEE International Conference on Acoustics, 2023

A Statistical Model for Predicting Generalization in Few-Shot Classification.
Proceedings of the 31st European Signal Processing Conference, 2023

Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Rethinking Weight Decay for Efficient Neural Network Pruning.
J. Imaging, 2022

Easy - Ensemble Augmented-Shot-Y-Shaped Learning: State-of-the-Art Few-Shot Classification with Simple Components.
J. Imaging, 2022

A Statistical Model for Predicting Generalization in Few-Shot Classification.
CoRR, 2022

Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings.
CoRR, 2022

Preserving Fine-Grain Feature Information in Classification via Entropic Regularization.
CoRR, 2022

EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients.
CoRR, 2022

Preventing Manifold Intrusion with Locality: Local Mixup.
CoRR, 2022

Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning.
Algorithms, 2022

Inter-Operability of Compression Techniques for Efficient Deployment of CNNs on Microcontrollers.
Proceedings of the Advances in System-Integrated Intelligence, 2022

Energy Consumption Analysis of Pruned Semantic Segmentation Networks on an Embedded GPU.
Proceedings of the Advances in System-Integrated Intelligence, 2022

Leveraging Structured Pruning of Convolutional Neural Networks.
Proceedings of the IEEE Workshop on Signal Processing Systems, 2022

Pruning Graph Convolutional Networks to Select Meaningful Graph Frequencies for FMRI Decoding.
Proceedings of the 30th European Signal Processing Conference, 2022

A Local Mixup to Prevent Manifold Intrusion.
Proceedings of the 30th European Signal Processing Conference, 2022

2021
Quantization and Deployment of Deep Neural Networks on Microcontrollers.
Sensors, 2021

Improved Visual Localization via Graph Filtering.
J. Imaging, 2021

Predicting the Generalization Ability of a Few-Shot Classifier.
Inf., 2021

Graphs as Tools to Improve Deep Learning Methods.
CoRR, 2021

Graph-LDA: Graph Structure Priors to Improve the Accuracy in Few-Shot Classification.
CoRR, 2021

Representing Deep Neural Networks Latent Space Geometries with Graphs.
Algorithms, 2021

Using Deep Neural Networks to Predict and Improve the Performance of Polar Codes.
Proceedings of the 11th International Symposium on Topics in Coding, 2021

Improving Classification Accuracy With Graph Filtering.
Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

Towards an Intrinsic Definition of Robustness for a Classifier.
Proceedings of the IEEE International Conference on Acoustics, 2021

Leveraging the Feature Distribution in Transfer-Based Few-Shot Learning.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2021, 2021

Few-Shot Decoding of Brain Activation Maps.
Proceedings of the 29th European Signal Processing Conference, 2021

Inferring Graph Signal Translations as Invariant Transformations for Classification Tasks.
Proceedings of the 29th European Signal Processing Conference, 2021

Similarity between Base and Novel Classes: a Predictor of the Performance in Few-Shot Classification of Brain Activation Maps?
Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020
DecisiveNets: Training Deep Associative Memories to Solve Complex Machine Learning Problems.
CoRR, 2020

Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs.
CoRR, 2020

Continuous Pruning of Deep Convolutional Networks Using Selective Weight Decay.
CoRR, 2020

Few-shot Learning for Decoding Brain Signals.
CoRR, 2020

Some Remarks on Replicated Simulated Annealing.
CoRR, 2020

ThriftyNets : Convolutional Neural Networks with Tiny Parameter Budget.
CoRR, 2020

Predicting the Accuracy of a Few-Shot Classifier.
CoRR, 2020

BitPruning: Learning Bitlengths for Aggressive and Accurate Quantization.
CoRR, 2020

Exploiting Unsupervised Inputs for Accurate Few-Shot Classification.
CoRR, 2020

Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks.
Proceedings of the 18th IEEE International New Circuits and Systems Conference, 2020

Graph Topology Inference Benchmarks for Machine Learning.
Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

Attention Based Pruning for Shift Networks.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

GPU-Based Self-Organizing Maps for Post-labeled Few-Shot Unsupervised Learning.
Proceedings of the Neural Information Processing - 27th International Conference, 2020

Deep Geometric Knowledge Distillation with Graphs.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Efficient Representations for Graph and Neural Network Signals. (Représentations efficaces pour les signaux sur graphes et réseaux de neurones).
, 2020

2019
Budget Restricted Incremental Learning with Pre-Trained Convolutional Neural Networks and Binary Associative Memories.
J. Signal Process. Syst., 2019

Improved Visual Localization via Graph Smoothing.
CoRR, 2019

Comparing linear structure-based and data-driven latent spatial representations for sequence prediction.
CoRR, 2019

A Unified Deep Learning Formalism For Processing Graph Signals.
CoRR, 2019

Efficient Hardware Implementation of Incremental Learning and Inference on Chip.
Proceedings of the 17th IEEE International New Circuits and Systems Conference, 2019

Training Modern Deep Neural Networks for Memory-Fault Robustness.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2019

Transfer Learning with Sparse Associative Memories.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation, 2019

Structural Robustness for Deep Learning Architectures.
Proceedings of the IEEE Data Science Workshop, 2019

Introducing Graph Smoothness Loss for Training Deep Learning Architectures.
Proceedings of the IEEE Data Science Workshop, 2019

2018
Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals.
IEEE Trans. Signal Inf. Process. over Networks, 2018

Memory Vectors for Similarity Search in High-Dimensional Spaces.
IEEE Trans. Big Data, 2018

SimiNet: A Novel Method for Quantifying Brain Network Similarity.
IEEE Trans. Pattern Anal. Mach. Intell., 2018

Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks.
CoRR, 2018

Transfer Incremental Learning using Data Augmentation.
CoRR, 2018

Laplacian Power Networks: Bounding Indicator Function Smoothness for Adversarial Defense.
CoRR, 2018

An Inside Look at Deep Neural Networks Using Graph Signal Processing.
Proceedings of the 2018 Information Theory and Applications Workshop, 2018

Improving Accuracy of Nonparametric Transfer Learning Via Vector Segmentation.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Graph-Projected Signal Processing.
Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing, 2018

Matching Convolutional Neural Networks without Priors about Data.
Proceedings of the 2018 IEEE Data Science Workshop, 2018

2017
Convolutional neural networks on irregular domains through approximate translations on inferred graphs.
CoRR, 2017

Robust Associative Memories Naturally Occuring From Recurrent Hebbian Networks Under Noise.
CoRR, 2017

Translations on graphs with neighborhood preservation.
CoRR, 2017

NoC-MRAM architecture for memory-based computing: Database-search case study.
Proceedings of the 15th IEEE International New Circuits and Systems Conference, 2017

Learning local receptive fields and their weight sharing scheme on graphs.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

Evaluating graph signal processing for neuroimaging through classification and dimensionality reduction.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

Incremental learning on chip.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

Tropical graph signal processing.
Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016
Fault-Tolerant Associative Memories Based on c-Partite Graphs.
IEEE Trans. Signal Process., 2016

Storing Sequences in Binary Tournament-Based Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., 2016

Twin Neurons for Efficient Real-World Data Distribution in Networks of Neural Cliques: Applications in Power Management in Electronic Circuits.
IEEE Trans. Neural Networks Learn. Syst., 2016

Generalizing the Convolution Operator to Extend CNNs to Irregular Domains.
CoRR, 2016

Characterization and inference of weighted graph topologies from observations of diffused signals.
CoRR, 2016

Associative Memories to Accelerate Approximate Nearest Neighbor Search.
CoRR, 2016

A Biologically Inspired Framework for Visual Information Processing and an Application on Modeling Bottom-Up Visual Attention.
Cogn. Comput., 2016

Assembly output codes for learning neural networks.
Proceedings of the 9th International Symposium on Turbo Codes and Iterative Information Processing, 2016

Distributed coding and synaptic pruning.
Proceedings of the 9th International Symposium on Turbo Codes and Iterative Information Processing, 2016

A turbo-inspired iterative approach for correspondence problems of image features.
Proceedings of the 9th International Symposium on Turbo Codes and Iterative Information Processing, 2016

Nearest Neighbour Search using binary neural networks.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Massively parallel implementation of sparse message retrieval algorithms in Clustered Clique Networks.
Proceedings of the International Conference on High Performance Computing & Simulation, 2016

Towards a characterization of the uncertainty curve for graphs.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

Compression of Deep Neural Networks on the Fly.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

A Neural Network Model for Solving the Feature Correspondence Problem.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2016, 2016

Neighborhood-preserving translations on graphs.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

Associative Memory based on clustered Neural Networks: Improved model and architecture for Oriented Edge Detection.
Proceedings of the 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP), 2016

2015
Algorithm and Architecture for a Low-Power Content-Addressable Memory Based on Sparse Clustered Networks.
IEEE Trans. Very Large Scale Integr. Syst., 2015

A novel algorithm for measuring graph similarity: Application to brain networks.
Proceedings of the 7th International IEEE/EMBS Conference on Neural Engineering, 2015

Algorithm and implementation of an associative memory for oriented edge detection using improved clustered neural networks.
Proceedings of the 2015 IEEE International Symposium on Circuits and Systems, 2015

A model of bottom-up visual attention using cortical magnification.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Restricted Clustered Neural Network for Storing Real Data.
Proceedings of the 25th edition on Great Lakes Symposium on VLSI, GLVLSI 2015, Pittsburgh, PA, USA, May 20, 2015

Toward an uncertainty principle for weighted graphs.
Proceedings of the 23rd European Signal Processing Conference, 2015

Graph reconstruction from the observation of diffused signals.
Proceedings of the 53rd Annual Allerton Conference on Communication, 2015

2014
Algorithm and Architecture of Fully-Parallel Associative Memories Based on Sparse Clustered Networks.
J. Signal Process. Syst., 2014

A Nonvolatile Associative Memory-Based Context-Driven Search Engine Using 90 nm CMOS/MTJ-Hybrid Logic-in-Memory Architecture.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2014

Combating Corrupt Messages in Sparse Clustered Associative Memories.
CoRR, 2014

Algorithm and architecture for a multiple-field context-driven search engine using fully-parallel clustered associative memories.
Proceedings of the 2014 IEEE Workshop on Signal Processing Systems, 2014

Information, noise, coding, modulation: What about the brain?
Proceedings of the 8th International Symposium on Turbo Codes and Iterative Information Processing, 2014

Towards a spectral characterization of signals supported on small-world networks.
Proceedings of the IEEE International Conference on Acoustics, 2014

Cluster-based associative memories built from unreliable storage.
Proceedings of the IEEE International Conference on Acoustics, 2014

A GPU-based associative memory using sparse Neural Networks.
Proceedings of the International Conference on High Performance Computing & Simulation, 2014

Sparse binary matrices as efficient associative memories.
Proceedings of the 52nd Annual Allerton Conference on Communication, 2014

Huffman Coding for Storing Non-Uniformly Distributed Messages in Networks of Neural Cliques.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013
A Massively Parallel Associative Memory Based on Sparse Neural Networks
CoRR, 2013

Bounds on associative memories
CoRR, 2013

Improving Sparse Associative Memories by Escaping from Bogus Fixed Points.
CoRR, 2013

Storing non-uniformly distributed messages in networks of neural cliques.
CoRR, 2013

A study of retrieval algorithms of sparse messages in networks of neural cliques.
CoRR, 2013

Maximum likelihood associative memories.
Proceedings of the 2013 IEEE Information Theory Workshop, 2013

Reconstructing a graph from path traces.
Proceedings of the 2013 IEEE International Symposium on Information Theory, 2013

Reduced-complexity binary-weight-coded associative memories.
Proceedings of the IEEE International Conference on Acoustics, 2013

A low-power Content-Addressable Memory based on clustered-sparse networks.
Proceedings of the 24th International Conference on Application-Specific Systems, 2013

Sparse structured associative memories as efficient set-membership data structures.
Proceedings of the 51st Annual Allerton Conference on Communication, 2013

2012
Learning sparse messages in networks of neural cliques
CoRR, 2012

Forwarding Without Repeating: Efficient Rumor Spreading in Bounded-Degree Graphs
CoRR, 2012

Compressing multisets using tries.
Proceedings of the 2012 IEEE Information Theory Workshop, 2012

Nearly-optimal associative memories based on distributed constant weight codes.
Proceedings of the 2012 Information Theory and Applications Workshop, 2012

Random clique codes.
Proceedings of the 7th International Symposium on Turbo Codes and Iterative Information Processing, 2012

Architecture and implementation of an associative memory using sparse clustered networks.
Proceedings of the 2012 IEEE International Symposium on Circuits and Systems, 2012

2011
Networks of Neural Cliques. (Réseaux de cliques neurales).
PhD thesis, 2011

Sparse Neural Networks With Large Learning Diversity.
IEEE Trans. Neural Networks, 2011

A simple and efficient way to store many messages using neural cliques.
Proceedings of the 2011 IEEE Symposium on Computational Intelligence, 2011

2009
Qualitative Concurrent Games with Imperfect Information
CoRR, 2009

Qualitative Concurrent Stochastic Games with Imperfect Information.
Proceedings of the Automata, Languages and Programming, 36th Internatilonal Colloquium, 2009


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