DeeSCo: Deep heterogeneous ensemble with Stochastic Combinatory loss for gaze estimation. - Edouard Yvinec; Arnaud Dapogny; Kévin Bailly (FG, 2020)

RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks. - Edouard Yvinec; Arnaud Dapogny; Matthieu Cord; Kevin Bailly (NeurIPS, 2021)

RED : Looking for Redundancies for Data-Free Structured Compression of Deep Neural Networks. - Edouard Yvinec; Arnaud Dapogny; Matthieu Cord; Kevin Bailly (CoRR, 2021)

To Fold or Not to Fold: a Necessary and Sufficient Condition on Batch-Normalization Layers Folding. - Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (IJCAI, 2022)

SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance. - Edouard Yvinec; Arnaud Dapogny; Matthieu Cord; Kevin Bailly (NeurIPS, 2022)

Multi-label Transformer for Action Unit Detection. - Gauthier Tallec; Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (CoRR, 2022)

RULe: Relocalization-Uniformization-Landmark Estimation Network for Real-Time Face Alignment in Degraded Conditions. - Arnaud Dapogny; Gauthier Tallec; Jules Bonnard; Edouard Yvinec; Kévin Bailly (FG, 2023)

Adversarial Deep Multi-Task Learning Using Semantically Orthogonal Spaces and Application to Facial Attributes Prediction. - Arnaud Dapogny; Gauthier Tallec; Jules Bonnard; Edouard Yvinec; Kévin Bailly (FG, 2023)

Fighting Over-Fitting with Quantization for Learning Deep Neural Networks on Noisy Labels. - Gauthier Tallec; Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (ICIP, 2023)

Designing Strong Baselines for Ternary Neural Network Quantization through Support and Mass Equalization. - Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (ICIP, 2023)

PowerQuant: Automorphism Search for Non-Uniform Quantization. - Edouard Yvinec; Arnaud Dapogny; Matthieu Cord; Kevin Bailly (ICLR, 2023)

REx: Data-Free Residual Quantization Error Expansion. - Edouard Yvinec; Arnaud Dapogny; Matthieu Cord; Kevin Bailly (NeurIPS, 2023)

SPIQ: Data-Free Per-Channel Static Input Quantization. - Edouard Yvinec; Arnaud Dapogny; Matthieu Cord; Kevin Bailly (WACV, 2023)

SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference. - Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (CoRR, 2023)

NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search. - Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (CoRR, 2023)

Gradient-Based Post-Training Quantization: Challenging the Status Quo. - Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (CoRR, 2023)

Archtree: on-the-fly tree-structured exploration for latency-aware pruning of deep neural networks. - Rémi Ouazan Reboul; Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (CoRR, 2023)

PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions. - Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (CoRR, 2023)

Efficient Neural Networks: Post Training Pruning and Quantization. (Accélération des réseaux de neurones). - Edouard Yvinec (2023)

RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging. - Edouard Yvinec; Arnaud Dapogny; Matthieu Cord; Kevin Bailly (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings. - Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (ICLR, 2024)

SPOT: Text Source Prediction from Originality Score Thresholding. - Edouard Yvinec; Gabriel Kasser (CoRR, 2024)

PIPE: Parallelized inference through ensembling of residual quantization expansions. - Edouard Yvinec; Arnaud Dapogny; Kevin Bailly (Pattern Recognit., 2024)

Gemma 3 Technical Report. - Aishwarya Kamath; Johan Ferret; Shreya Pathak; Nino Vieillard; Ramona Merhej; Sarah Perrin; Tatiana Matejovicova; Alexandre Ramé; Morgane Rivière; Louis Rouillard; Thomas Mesnard; Geoffrey Cideron; et al. (CoRR, 2025)

MedGemma Technical Report. - Andrew Sellergren; Sahar Kazemzadeh; Tiam Jaroensri; Atilla P. Kiraly; Madeleine Traverse; Timo Kohlberger; Shawn Xu; Fayaz Jamil; Cían Hughes; Charles Lau; Justin Chen; Fereshteh Mahvar; et al. (CoRR, 2025)