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Found 6 papers, 0 papers with code
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?
Deep neural networks (DNNs) are easily fooled by adversarial perturbations that are imperceptible to humans.
On visual self-supervision and its effect on model robustness
We identify primary ways in which self-supervision can be added to adversarial training, and observe that using a self-supervised loss to optimize both network parameters and find adversarial examples leads to the strongest improvement in model robustness, as this can be viewed as a form of ensemble adversarial training.
Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons
The brain does not work solely in a feed-forward fashion, but rather all of the neurons are in competition with each other; neurons are integrating information in a bottom up and top down fashion and incorporating expectation and feedback in the modeling process.
Does Phase Matter For Monaural Source Separation?
The "cocktail party" problem of fully separating multiple sources from a single channel audio waveform remains unsolved.
Image Compression: Sparse Coding vs. Bottleneck Autoencoders
Bottleneck autoencoders have been actively researched as a solution to image compression tasks.
Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs
We describe a computationally efficient, stochastic graph-regularization technique that can be utilized for the semi-supervised training of deep neural networks in a parallel or distributed setting.
