1 code implementation • ICLR 2022 • Milad Alizadeh, Shyam A. Tailor, Luisa M Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal
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 training and inference.
1 code implementation • 30 Jan 2022 • Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Goliński, Yee Whye Teh, Arnaud Doucet
Neural compression algorithms are typically based on autoencoders that require specialized encoder and decoder architectures for different data modalities.
1 code implementation • ICLR Workshop Neural_Compression 2021 • Emilien Dupont, Adam Goliński, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet
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 image.
no code implementations • 1 Jul 2020 • Joost van Amersfoort, Milad Alizadeh, Sebastian Farquhar, Nicholas Lane, Yarin Gal
We introduce a method to speed up training by 2x and inference by 3x in deep neural networks using structured pruning applied before training.
no code implementations • ICLR 2020 • Milad Alizadeh, Arash Behboodi, Mart van Baalen, Christos Louizos, Tijmen Blankevoort, Max Welling
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-training quantization.
1 code implementation • 22 Dec 2019 • Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G. J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal
From our comparison we conclude that some current techniques which solve benchmarks such as UCI `overfit' their uncertainty to the dataset---when evaluated on our benchmark these underperform in comparison to simpler baselines.
1 code implementation • ICLR 2019 • Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane, Yarin Gal
In this work, we empirically identify and study the effectiveness of the various ad-hoc techniques commonly used in the literature, providing best-practices for efficient training of binary models.
no code implementations • ICLR 2018 • Vincent W.-S. Tseng, Sourav Bhattachary, Javier Fernández Marqués, Milad Alizadeh, Catherine Tong, Nicholas Donald Lane
In this work we present BinaryFlex, a neural network architecture that learns weighting coefficients of predefined orthogonal binary basis instead of the conventional approach of learning directly the convolutional filters.