2 citations · 3 across the 35 of their papers we have counts for
40 papers
Decoupled Mode Connectivity for Base-to-Novel Generalization in Vision-Language Models
Imad Eddine Marouf, Khalid Oublal, Enzo Tartaglione +1
Prompt learning adapts vision-language models such as CLIP by optimizing a short learnable prompt. The cross-entropy objective pulls this prompt toward base-class specialization, w…
Exploiting Biases in Attention for Transformers
Charles Herr, Gabriele Spadaro, Enzo Tartaglione
Attention is known to be sparse in vision transformers. In particular, we analyze the unequal relevance of attention-matrix columns to the final computation and show that this phen…
Validity-Preserving Hierarchical RL for Joint Routing and Switch Placement in EDA
Dorian Gailhard, Ugo Lecerf, Enzo Tartaglione +2
Routing and switch placement are fundamental combinatorial optimization problems in chip design, requiring the joint optimization of routing topology and physical placement under s…
From Deep to Shallow: Unconstrained and Efficient Layer Merging Strategy
Petro Shulzhenko, Gabriele Spadaro, Enzo Tartaglione
Although Deep Neural Networks have become foundational in many areas of Machine Learning, high computational demands limit their application in resource-constrained environments. T…
CutClean: Neural Network Pruning for Privacy-Preserving Inference
Leonardo Magliolo, Vito Paolo Pastore, Giuseppe Valenzise +1
Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns. We show that this privacy leakage can occur even in the absence of repr…
Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models
Ivan Luiz De Moura Matos, Abdel Djalil Sad Saoud, Ekaterina Iakovleva +2
The issue of algorithmic biases in deep learning has led to the development of various debiasing techniques, many of which perform complex training procedures or dataset manipulati…
HFMCA: Orthonormal Feature Learning for EEG-based Brain Decoding
Yinghao Wang, Lintao Xu, Shujian Yu +2
Electroencephalography (EEG) analysis is critical for brain-computer interfaces and neuroscience, but the intrinsic noise and high dimensionality of EEG signals hinder effective fe…
Nix and Fix: Targeting 1000x Compression of 3D Gaussian Splatting with Diffusion Models
Cem Eteke, Enzo Tartaglione
3D Gaussian Splatting (3DGS) revolutionized novel view rendering. Instead of inferring from dense spatial points, as implicit representations do, 3DGS uses sparse Gaussians. This e…
RAVE: Rate-Adaptive Visual Encoding for 3D Gaussian Splatting
Hoang-Nhat Tran, Francesco Di Sario, Gabriele Spadaro +2
Recent advances in neural scene representations have transformed immersive multimedia, with 3D Gaussian Splatting (3DGS) enabling real-time photorealistic rendering. Despite its ef…
How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
Nathan Roos, Ekaterina Iakovleva, Ani Gjergji +2
Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks, yet their tendency to replicate and amplify dataset biases remains…
Memory Constrained Dynamic Subnetwork Update for Transfer Learning
Aël Quélennec, Pavlo Mozharovskyi, Van-Tam Nguyen +1
On-device neural network training faces critical memory constraints that limit the adaptation of pre-trained models to downstream tasks. We present MeDyate, a theoretically-grounde…
Study of Training Dynamics for Memory-Constrained Fine-Tuning
Aël Quélennec, Nour Hezbri, Pavlo Mozharovskyi +2
Memory-efficient training of deep neural networks has become increasingly important as models grow larger while deployment environments impose strict resource constraints. We propo…
Efficient Resource-Constrained Training of Transformers via Subspace Optimization
Le-Trung Nguyen, Enzo Tartaglione, Van-Tam Nguyen
As AI increasingly shapes daily life, energy consumption and data privacy have become pressing concerns. On-device learning trains models directly on edge devices, cutting energy c…
The silence of the weights: a structural pruning strategy for attention-based audio signal architectures with second order metrics
Andrea Diecidue, Carlo Alberto Barbano, Piero Fraternali +2
Transformer-based models have become the state of the art across multiple domains, from natural language processing to machine listening, thanks to the attention mechanisms. Howeve…
Neural Velocity for hyperparameter tuning
Gianluca Dalmasso, Andrea Bragagnolo, Enzo Tartaglione +2
Hyperparameter tuning, such as learning rate decay and defining a stopping criterion, often relies on monitoring the validation loss. This paper presents NeVe, a dynamic training a…
Feature-Aware (Hyper)graph Generation via Next-Scale Prediction
Dorian Gailhard, Enzo Tartaglione, Lirida Naviner +1
Graph generative models perform well on small-scale structured data but struggle to scale to large, complex structures. Hierarchical approaches improve scalability but often ignore…
Denoising Diffusion Probabilistic Model for Point Cloud Compression at Low Bit-Rates
Gabriele Spadaro, Alberto Presta, Jhony H. Giraldo +5
Efficient compression of low-bit-rate point clouds is critical for bandwidth-constrained applications. However, existing techniques mainly focus on high-fidelity reconstruction, re…
I-INR: Iterative Implicit Neural Representations
Ali Haider, Muhammad Salman Ali, Maryam Qamar +5
Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable functions parameterized by neural…
Lightweight Embedded FPGA Deployment of Learned Image Compression with Knowledge Distillation and Hybrid Quantization
Alaa Mazouz, Sumanta Chaudhuri, Marco Cagnanzzo +3
Learnable Image Compression (LIC) has shown the potential to outperform standardized video codecs in RD efficiency, prompting the research for hardware-friendly implementations. Mo…
Security and Real-time FPGA integration for Learned Image Compression
Alaa Mazouz, Carl De Sousa Tria, Sumanta Chaudhuri +4
Learnable Image Compression (LIC) has proven capable of outperforming standardized video codecs in compression efficiency. However, achieving both real-time and secure LIC operatio…
Compression in 3D Gaussian Splatting: A Survey of Methods, Trends, and Future Directions
Muhammad Salman Ali, Chaoning Zhang, Marco Cagnazzo +3
3D Gaussian Splatting (3DGS) has recently emerged as a pioneering approach in explicit scene rendering and computer graphics. Unlike traditional neural radiance field (NeRF) method…
Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing
Massimiliano Ciranni, Vito Paolo Pastore, Roberto Di Via +3
Deep learning model effectiveness in classification tasks is often challenged by the quality and quantity of training data whenever they are affected by strong spurious correlation…
Ask and Remember: A Questions-Only Replay Strategy for Continual Visual Question Answering
Imad Eddine Marouf, Enzo Tartaglione, Stephane Lathuiliere +1
Continual Learning in Visual Question Answering (VQACL) requires models to acquire new visual-linguistic skills (plasticity) while preserving previously learned knowledge (stabilit…
GoDe: Gaussians on Demand for Backbone-Agnostic Progressive Compression
Francesco Di Sario, Riccardo Renzulli, Marco Grangetto +2
Splat-based radiance fields have rapidly diversified in primitive type, kernel support, and appearance parameterization. This diversity makes compression difficult: most codecs are…