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02/20/2023
Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation
Skip connections and normalisation layers form two standard architectura...
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05/31/2022
Pre-training via Denoising for Molecular Property Prediction
Many important problems involving molecular property prediction from 3D ...
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03/15/2022
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers
Training very deep neural networks is still an extremely challenging tas...
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04/13/2021
On the validity of kernel approximations for orthogonally-initialized neural networks
In this note we extend kernel function approximation results for neural ...
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07/09/2019
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Increasing the batch size is a popular way to speed up neural network tr...
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07/04/2019
Adversarial Robustness through Local Linearization
Adversarial training is an effective methodology for training deep neura...
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05/27/2019
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks
Natural gradient descent has proven effective at mitigating the effects ...
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05/13/2019
Differentiable Game Mechanics
Deep learning is built on the foundational guarantee that gradient desce...
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02/06/2019
On the Variance of Unbiased Online Recurrent Optimization
The recently proposed Unbiased Online Recurrent Optimization algorithm (...
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02/15/2018
The Mechanics of n-Player Differentiable Games
The cornerstone underpinning deep learning is the guarantee that gradien...
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02/03/2016
A Kronecker-factored approximate Fisher matrix for convolution layers
Second-order optimization methods such as natural gradient descent have ...
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11/21/2015
Adding Gradient Noise Improves Learning for Very Deep Networks
Deep feedforward and recurrent networks have achieved impressive results...
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03/19/2015
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
We propose an efficient method for approximating natural gradient descen...
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12/03/2014
New insights and perspectives on the natural gradient method
Natural gradient descent is an optimization method traditionally motivat...
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11/27/2014
On the Expressive Efficiency of Sum Product Networks
Sum Product Networks (SPNs) are a recently developed class of deep gener...
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06/27/2012
