104 citations · 673 across the 47 of their papers we have counts for
52 papers
Velocity Scaling in Flow Matching
Youssef Saied, François Fleuret
Scaling a learned flow-matching velocity field by a gain was recently shown to greatly improve generation quality. Prior work argued that velocity fields trained with…
Adversarial Images Hijack Web Agents from Visual Grounding to Browser Execution
Wanjing Han, Levi Taiji Li, Mu Zhang +2
Modern web agents built on large vision-language models process webpages, select relevant UI elements, and translate model outputs into browser actions. Existing visual red-teaming…
Model Casting and Low-Parameter Gating: Towards More Sparsely Activated FFNs
Maria Lomeli, Antoine Groudiev, Matthijs Douze +7
This paper introduces model casting, a mid-training recipe that drastically sparsifies the activations within the Feed-Forward Network (FFN) layer. With this strategy, at inference…
Let It Go or Learn to Self-Correct: Continuous Diffusion for Constrained Discrete Tasks
Mariia Drozdova, Stéphane Liem Nguyen, François Fleuret
Denoising Diffusion Probabilistic Models (DDPMs) generate samples by starting from noise and repeatedly denoising while keeping each update close to the current noisy state. This b…
MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities
Mingqiao Ye, Zhaochong An, Zhitong Gao +11
Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly…
End-to-End Optimal Detector Design with Mutual Information Surrogates
Kinga Anna Wozniak, Stephen Mulligan, Jan Kieseler +3
We introduce a novel approach for end-to-end black-box optimization of high energy physics (HEP) detectors using local deep learning (DL) surrogates. These surrogates approximate a…
LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging
Ke Wang, Nikolaos Dimitriadis, Alessandro Favero +3
Fine-tuning pre-trained models has become the standard approach to endow them with specialized knowledge, but it poses fundamental challenges. In particular, \textit{(i)} fine-tuni…
Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences
Nikolaos Dimitriadis, Pascal Frossard, Francois Fleuret
Multi-task trade-offs in machine learning can be addressed via Pareto Front Learning (PFL) methods that parameterize the Pareto Front (PF) with a single model. PFL permits to selec…
DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging
Matteo Pagliardini, Amirkeivan Mohtashami, Francois Fleuret +1
The transformer architecture by Vaswani et al. (2017) is now ubiquitous across application domains, from natural language processing to speech processing and image understanding. W…
DeepEMD: A Transformer-based Fast Estimation of the Earth Mover's Distance
Atul Kumar Sinha, Francois Fleuret
The Earth Mover's Distance (EMD) is the measure of choice between point clouds. However the computational cost to compute it makes it prohibitive as a training loss, and the standa…
PAUMER: Patch Pausing Transformer for Semantic Segmentation
Evann Courdier, Prabhu Teja Sivaprasad, François Fleuret
We study the problem of improving the efficiency of segmentation transformers by using disparate amounts of computation for different parts of the image. Our method, PAUMER, accomp…
SequeL: A Continual Learning Library in PyTorch and JAX
Nikolaos Dimitriadis, Francois Fleuret, Pascal Frossard
Continual Learning is an important and challenging problem in machine learning, where models must adapt to a continuous stream of new data without forgetting previously acquired kn…
ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields
Mohammad Mahdi Johari, Camilla Carta, François Fleuret
We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a seque…
Inference from Real-World Sparse Measurements
Arnaud Pannatier, Kyle Matoba, François Fleuret
Real-world problems often involve complex and unstructured sets of measurements, which occur when sensors are sparsely placed in either space or time. Being able to model this irre…
Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models
Nikolaos Dimitriadis, Pascal Frossard, François Fleuret
In Multi-Task Learning (MTL), tasks may compete and limit the performance achieved on each other, rather than guiding the optimization to a solution, superior to all its single-tas…
Transformers are Sample-Efficient World Models
Vincent Micheli, Eloi Alonso, François Fleuret
Deep reinforcement learning agents are notoriously sample inefficient, which considerably limits their application to real-world problems. Recently, many model-based methods have b…
Efficiently Training Low-Curvature Neural Networks
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju +1
The highly non-linear nature of deep neural networks causes them to be susceptible to adversarial examples and have unstable gradients which hinders interpretability. However, exis…
Flowification: Everything is a Normalizing Flow
Bálint Máté, Samuel Klein, Tobias Golling +1
The two key characteristics of a normalizing flow is that it is invertible (in particular, dimension preserving) and that it monitors the amount by which it changes the likelihood…
HyperMixer: An MLP-based Low Cost Alternative to Transformers
Florian Mai, Arnaud Pannatier, Fabio Fehr +4
Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input leng…
Borrowing from yourself: Faster future video segmentation with partial channel update
Evann Courdier, François Fleuret
Semantic segmentation is a well-addressed topic in the computer vision literature, but the design of fast and accurate video processing networks remains challenging. In addition, t…
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics
Atul Kumar Sinha, Daniele Paliotta, Bálint Máté +4
Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard Gea…
Agree to Disagree: Diversity through Disagreement for Better Transferability
Matteo Pagliardini, Martin Jaggi, François Fleuret +1
Gradient-based learning algorithms have an implicit simplicity bias which in effect can limit the diversity of predictors being sampled by the learning procedure. This behavior can…
Efficient Wind Speed Nowcasting with GPU-Accelerated Nearest Neighbors Algorithm
Arnaud Pannatier, Ricardo Picatoste, François Fleuret
This paper proposes a simple yet efficient high-altitude wind nowcasting pipeline. It processes efficiently a vast amount of live data recorded by airplanes over the whole airspace…
GeoNeRF: Generalizing NeRF with Geometry Priors
Mohammad Mahdi Johari, Yann Lepoittevin, François Fleuret
We present GeoNeRF, a generalizable photorealistic novel view synthesis method based on neural radiance fields. Our approach consists of two main stages: a geometry reasoner and a…