research
∙
06/14/2023
A^2CiD^2: Accelerating Asynchronous Communication in Decentralized Deep Learning
Distributed training of Deep Learning models has been critical to many r...
share
research
∙
06/12/2023
Can Forward Gradient Match Backpropagation?
Forward Gradients - the idea of using directional derivatives in forward...
research
∙
06/06/2023
Guiding The Last Layer in Federated Learning with Pre-Trained Models
Federated Learning (FL) is an emerging paradigm that allows a model to b...
research
∙
07/26/2022
DADAO: Decoupled Accelerated Decentralized Asynchronous Optimization for Time-Varying Gossips
DADAO is a novel decentralized asynchronous stochastic algorithm to mini...
research
∙
07/18/2022
Why do tree-based models still outperform deep learning on tabular data?
While deep learning has enabled tremendous progress on text and image da...
research
∙
07/06/2022
On Non-Linear operators for Geometric Deep Learning
This work studies operators mapping vector and scalar fields defined ove...
research
∙
01/28/2022
Gradient Masked Averaging for Federated Learning
Federated learning is an emerging paradigm that permits a large number o...
research
∙
07/27/2021
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment
Sarcopenia is a medical condition characterized by a reduction in muscle...
research
∙
06/11/2021
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
A commonly cited inefficiency of neural network training using back-prop...
research
∙
01/19/2021
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
A recent line of work showed that various forms of convolutional kernel ...
research
∙
06/10/2020
Interferometric Graph Transform: a Deep Unsupervised Graph Representation
We propose the Interferometric Graph Transform (IGT), which is a new cla...
research
∙
01/23/2019
Decoupled Greedy Learning of CNNs
A commonly cited inefficiency of neural network training by back-propaga...
research
∙
12/29/2018
Greedy Layerwise Learning Can Scale to ImageNet
Shallow supervised 1-hidden layer neural networks have a number of favor...
research
∙
12/28/2018
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation s...
research
∙
09/27/2018
Compressing the Input for CNNs with the First-Order Scattering Transform
We study the first-order scattering transform as a candidate for reducin...
research
∙
09/17/2018
Scattering Networks for Hybrid Representation Learning
Scattering networks are a class of designed Convolutional Neural Network...
research
∙
06/01/2018
Nonlinear Acceleration of CNNs
The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleratio...
research
∙
05/24/2018
Nonlinear Acceleration of Deep Neural Networks
Regularized nonlinear acceleration (RNA) is a generic extrapolation sche...
research
∙
02/20/2018
i-RevNet: Deep Invertible Networks
It is widely believed that the success of deep convolutional networks is...
research
∙
03/27/2017
Scaling the Scattering Transform: Deep Hybrid Networks
We use the scattering network as a generic and fixed ini-tialization of ...
research
∙
03/12/2017
Multiscale Hierarchical Convolutional Networks
Deep neural network algorithms are difficult to analyze because they lac...
research
∙
03/06/2017
Building a Regular Decision Boundary with Deep Networks
In this work, we build a generic architecture of Convolutional Neural Ne...
research
∙
12/30/2014
Deep Roto-Translation Scattering for Object Classification
Dictionary learning algorithms or supervised deep convolution networks h...
research
∙
12/20/2013
