Enzo Tartaglione

Latest news

[Oct 24] Paper accepted for publication at WACV 2025 - ELMGS: Enhancing memory and computation scaLability through coMpression for 3D Gaussian Splatting

[Oct 24] Paper accepted for publication at WACV 2025 - Efficient Progressive Image Compression with Variance-aware Masking

[Sep 24] Paper accepted for publication at NeurIPS 2024 - Activation Map Compression through Tensor Decomposition for Deep Learning

[Sep 24] I gave a Nectar track talk at ECML-PKDD 2024 for Mining bias-target Alignment from Voronoi Cells . Slides are available here

[Sep 24] I have been awarded the HDR!

[Aug 24] Paper accepted for publication at WACV 2025 - WiGNet: Windowed Vision Graph Neural Network

[Aug 24] Paper accepted for publication at CADL 2024 (ECCV workshop) - Memory-Optimized Once-For-All network

[Aug 24] Paper accepted for publication at ICPR 2024 - WaterMAS: Sharpness-Aware Maximization for Neural Network Watermarking

[Jul 24] Paper accepted for publication at BMVC 2024 - Trimming the Fat: Efficient Compression of 3D Gaussian Splats through Pruning

[Jul 24] Paper accepted for publication at ECCV 2024 - Debiasing surgeon: fantastic weights and how to find them

[Jul 24] Paper accepted for publication at ECCV 2024 - Boost Your NeRF: A Model-Agnostic Mixture of Experts Framework for High Quality and Efficient Rendering

[Jul 24] Paper accepted for publication at ECCV 2024 - Weighted Ensemble Models Are Strong Continual Learners

[Jul 24] I have been appointed the Associate Professor affiliation at Institut Polytechnique de Paris!

[Jun 24] The ANR project "BANERA: Bias-Aware NEural aRchitecture seArch" has been accepted for funding.
Soon a PhD and a PostDoc to be recruited!

[Jun 24] Paper accepted for publication at ICIP 2024 - GABIC: Graph-based Attention Block for Image Compression

[May 24] Paper accepted for publication at ECML-PKDD 2024 - The Simpler The Better: An Entropy-Based Importance Metric To Reduce Neural Networks’ Depth

Paper accepted for publication at DCC 2024 as oral - Domain Adaptation for learned image compression with supervised Adapters

Paper accepted for publication at AAAI 2024 - DSD$^2$: can we Dodge Sparse Double Descent and compress the neural network worry-free?

Paper accepted for publication at AAAI 2024 - Find the lady: permutation and re-synchronization of deep neural networks

Paper accepted for publication at WACVW 2024 - Towards On-device Learning on Edge Devices: Ways to Select Neurons to Update under a Budget Constraint

Paper accepted for publication at WACV 2024 - Mini but Mighty: Finetuning ViTs with Mini Adapters

Stages application for February 2025 are now open!

Mission

In a world where deep learning is becoming more and more state-of-the-art, where the race to the computational capabilities determines the new technologies, it is crucial to open the black box deep learning is. Many good-willing researchers are already moving important steps in such direction, despite a wide multitiude and hetereogeneity of scientific backgrounds. This is good, this is progress!
We target it in the long term developing techniques which simplify these models. Some are easier to prune than others: why? How is the information being processed inside a deep model, from a macroscopic perspective? These are few of the questions to be answered to move in the right direction!