2 citations · 3 across the 35 of their papers we have counts for
7 papers · 2 filters
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…
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…
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…