104 citations · 673 across the 47 of their papers we have counts for
10 papers · 1 filter
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…