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Automatic Diagnosis of Alzheimer's Disease Using Neural Network Language Models
Publications associées (45)
Predicting optical transmission through complex scattering media from reflection patterns with deep neural networks
Demetri Psaltis, Eirini Kakkava
Deep neural networks (DNNs) are used to reconstruct transmission speckle intensity patterns from the respective reflection speckle intensity patterns generated by illuminated parafilm layers. The dependence of the reconstruction accuracy on the thickness o ...
2021Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)
Clément Hongler, Franck Raymond Gabriel, Arthur Jacot
The Neural Tangent Kernel is a new way to understand the gradient descent in deep neural networks, connecting them with kernel methods. In this talk, I'll introduce this formalism and give a number of results on the Neural Tangent Kernel and explain how th ...
ASSOC COMPUTING MACHINERY2021Supplementary Material - AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks
Sabine Süsstrunk, Majed El Helou, Frederike Dümbgen
In this supplementary material, we present the details of the neural network architecture and training settings used in all our experiments. This holds for all experiments presented in the main paper as well as in this supplementary material. We also show ...
2020The Unstoppable Rise of Computational Linguistics in Deep Learning
In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of v ...
Association for Computational Linguistics2020Capsule networks as recurrent models of grouping and segmentation
Michael Herzog, Adrien Christophe Doerig, Bilge Sayim, Mauro Manassi, Lynn Schmittwilken
Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we pre ...
2020Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks
We experimentally solve the problem of maximizing capacity under a total supply power constraint in a massively parallel submarine cable context, i.e., for a spatially uncoupled system in which fiber Kerr nonlinearity is not a dominant limitation. By using ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020Exact Preimages of Neural Network Aircraft Collision Avoidance Systems
François Fleuret, Kyle Michael Matoba
A common pattern of progress in engineering has seen deep neural networks displacing human-designed logic. There are many advantages to this approach, divorcing decisionmaking from human oversight and intuition has costs as well. One is that deep neural ne ...
2020Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks
Demetri Psaltis, Navid Borhani, Eirini Kakkava, Babak Rahmani, Ugur Tegin
Spatiotemporal nonlinear interactions in multimode fibers are of interest for beam shaping and frequency conversion by exploiting the nonlinear interaction of different pump modes from quasi-continuous wave to ultrashort pulses centered around visible to i ...
2020The committee machine: computational to statistical gaps in learning a two-layers neural network
Nicolas Macris, Florent Gérard Krzakala, Lenka Zdeborová, Jean François Emmanuel Barbier
Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student scenario in multi-layer neural networks. In this paper, we provide a rigor ...
IOP PUBLISHING LTD2019A geometry-inspired decision-based attack
Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Yujia Liu
Deep neural networks have recently achieved tremen-dous success in image classification. Recent studies havehowever shown that they are easily misled into incorrectclassification decisions by adversarial examples. Adver-saries can even craft attacks by que ...
2019