2 citations · 3 across the 35 of their papers we have counts for
9 papers · 1 filter
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…