73 citations · 134 across the 5 of their papers we have counts for
5 papers
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients
Milad Alizadeh, Shyam A. Tailor, Luisa M Zintgraf +4
Pruning neural networks at initialization would enable us to find sparse models that retain the accuracy of the original network while consuming fewer computational resources for t…
COIN: COmpression with Implicit Neural representations
Emilien Dupont, Adam Goliński, Milad Alizadeh +2
We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the ima…
Single Shot Structured Pruning Before Training
Joost van Amersfoort, Milad Alizadeh, Sebastian Farquhar +2
We introduce a method to speed up training by 2x and inference by 3x in deep neural networks using structured pruning applied before training. Unlike previous works on pruning befo…
Gradient Regularization for Quantization Robustness
Milad Alizadeh, Arash Behboodi, Mart van Baalen +3
We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-trainin…
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
Angelos Filos, Sebastian Farquhar, Aidan N. Gomez +6
Evaluation of Bayesian deep learning (BDL) methods is challenging. We often seek to evaluate the methods' robustness and scalability, assessing whether new tools give `better' unce…