48 citations · 71 across the 4 of their papers we have counts for
15 papers
Rethinking the Role of Gradient-Based Attribution Methods for Model Interpretability
Suraj Srinivas, Francois Fleuret
Current methods for the interpretability of discriminative deep neural networks commonly rely on the model's input-gradients, i.e., the gradients of the output logits w.r.t. the in…
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels +2
The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration. The efficacy of optimizers is often studied under n…
Full-Gradient Representation for Neural Network Visualization
Suraj Srinivas, Francois Fleuret
We introduce a new tool for interpreting neural net responses, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity com…
Reducing Noise in GAN Training with Variance Reduced Extragradient
Tatjana Chavdarova, Gauthier Gidel, François Fleuret +1
We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimiz…
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching
Stepan Tulyakov, Anton Ivanov, Francois Fleuret
End-to-end deep-learning networks recently demonstrated extremely good perfor- mance for stereo matching. However, existing networks are difficult to use for practical applications…
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Angelos Katharopoulos, François Fleuret
Deep neural network training spends most of the computation on examples that are properly handled, and could be ignored. We propose to mitigate this phenomenon with a principled im…