115 citations · 164 across the 16 of their papers we have counts for
18 papers
Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations
Wistan Marchadour, Pedro Soto Vega, Franck Vermet +1
The wide use of Convolutional Neural Networks (CNN) in numerous domains and real-world classification applications is justified by their high precision and automation speed, helpin…
On associative neural networks for sparse patterns with huge capacities
Matthias Löwe, Franck Vermet
Generalized Hopfield models with higher-order or exponential interaction terms are known to have substantially larger storage capacities than the classical quadratic model. On the…
Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning
Raphael Lafargue, Luke Smith, Franck Vermet +4
The predominant method for computing confidence intervals (CI) in few-shot learning (FSL) is based on sampling the tasks with replacement, i.e.\ allowing the same samples to appear…
Geometry-preserving Lie Group Integrators For Differential Equations On The Manifold Of Symmetric Positive Definite Matrices
Lucas Drumetz, Alexandre Reiffers-Masson, Naoufal El Bekri +1
In many applications, one encounters signals that lie on manifolds rather than a Euclidean space. In particular, covariance matrices are examples of ubiquitous mathematical objects…
Evaluation of importance estimators in deep learning classifiers for Computed Tomography
Lennart Brocki, Wistan Marchadour, Jonas Maison +5
Deep learning has shown superb performance in detecting objects and classifying images, ensuring a great promise for analyzing medical imaging. Translating the success of deep lear…
Model Transparency and Interpretability : Survey and Application to the Insurance Industry
Dimitri Delcaillau, Antoine Ly, Alize Papp +1
The use of models, even if efficient, must be accompanied by an understanding at all levels of the process that transforms data (upstream and downstream). Thus, needs increase to d…
Collaborative Insurance Sustainability and Network Structure
Arthur Charpentier, Lariosse Kouakou, Matthias Löwe +2
The peer-to-peer (P2P) economy has been growing with the advent of the Internet, with well known brands such as Uber or Airbnb being examples thereof. In the insurance sector the a…
Some Remarks on Replicated Simulated Annealing
Vincent Gripon, Matthias Löwe, Franck Vermet
Recently authors have introduced the idea of training discrete weights neural networks using a mix between classical simulated annealing and a replica ansatz known from the statist…
Towards an Intrinsic Definition of Robustness for a Classifier
Théo Giraudon, Vincent Gripon, Matthias Löwe +1
The robustness of classifiers has become a question of paramount importance in the past few years. Indeed, it has been shown that state-of-the-art deep learning architectures can e…
Multi-group Binary Choice with Social Interaction and a Random Communication Structure -- a Random Graph Approach
Matthias Löwe, Kristina Schubert, Franck Vermet
We construct and analyze a random graph model for discrete choice with social interaction and several groups of equal size. We concentrate on the case of two groups of equal sizes…
Improving Accuracy of Nonparametric Transfer Learning via Vector Segmentation
Vincent Gripon, Ghouthi B. Hacene, Matthias Löwe +1
Transfer learning using deep neural networks as feature extractors has become increasingly popular over the past few years. It allows to obtain state-of-the-art accuracy on dataset…
On a model of associative memory with huge storage capacity
Mete Demircigil, Judith Heusel, Matthias Löwe +2
In [7] Krotov and Hopfield suggest a generalized version of the well-known Hopfield model of associative memory. In their version they consider a polynomial interaction function an…
Associative Memories to Accelerate Approximate Nearest Neighbor Search
Vincent Gripon, Matthias Löwe, Franck Vermet
Nearest neighbor search is a very active field in machine learning for it appears in many application cases, including classification and object retrieval. In its canonical version…
A Comparative Study of Sparse Associative Memories
Vincent Gripon, Judith Heusel, Matthias Löwe +1
We study various models of associative memories with sparse information, i.e. a pattern to be stored is a random string of s and s with about s, only. We compare…
On the capacity of a new model of associative memory based on neural cliques
Judith Heusel, Matthias Löwe, Franck Vermet
Based on recent work by Gripon and Berrou, we introduce a new model of an associative memory. We show that this model has an efficiency bounded away from 0 and is therefore signifi…
Large deviation upper bounds for sums of positively associated indicators
Matthias Löwe, Franck Vermet
We give exponential upper bounds for , in particular , where is a sum of indicator random variables that are positively associated. These bounds allow, in p…
Capacity of an associative memory model on random graph architectures
Matthias Löwe, Franck Vermet
We analyze the storage capacity of the Hopfield models on classes of random graphs. While such a setup has been analyzed for the case that the underlying random graph model is an E…
Mixing times for the Swapping Algorithm on the Blume-Emery-Griffiths Model
M. Ebbers, H. Knöpfel, M. Löwe +1
We analyze the so called Swapping Algorithm, a parallel version of the well-known Metropolis-Hastings algorithm, on the mean-field version of the Blume-Emery-Griffiths model in sta…