Search Results for author: Aidan N. Gomez

Found 17 papers, 12 papers with code

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning

2 code implementations NeurIPS 2021 Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Tom Rainforth, Yarin Gal

We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input.

3D Part Segmentation

Robustness to Pruning Predicts Generalization in Deep Neural Networks

no code implementations10 Mar 2021 Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, Yarin Gal

Existing generalization measures that aim to capture a model's simplicity based on parameter counts or norms fail to explain generalization in overparameterized deep neural networks.

Interlocking Backpropagation: Improving depthwise model-parallelism

1 code implementation8 Oct 2020 Aidan N. Gomez, Oscar Key, Kuba Perlin, Stephen Gou, Nick Frosst, Jeff Dean, Yarin Gal

Motivated by poor resource utilisation, we introduce a class of intermediary strategies between local and global learning referred to as interlocking backpropagation.

Image Classification

Improving compute efficacy frontiers with SliceOut

no code implementations21 Jul 2020 Pascal Notin, Aidan N. Gomez, Joanna Yoo, Yarin Gal

Pushing forward the compute efficacy frontier in deep learning is critical for tasks that require frequent model re-training or workloads that entail training a large number of models.

Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers

no code implementations8 Jun 2020 Tim Z. Xiao, Aidan N. Gomez, Yarin Gal

We detect out-of-training-distribution sentences in Neural Machine Translation using the Bayesian Deep Learning equivalent of Transformer models.

Machine Translation Translation

A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks

1 code implementation22 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.

Out-of-Distribution Detection

Learning Sparse Networks Using Targeted Dropout

2 code implementations31 May 2019 Aidan N. Gomez, Ivan Zhang, Siddhartha Rao Kamalakara, Divyam Madaan, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton

Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights.

Network Pruning Neural Network Compression

Targeted Dropout

1 code implementation NIPS Workshop CDNNRIA 2018 Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton

Neural networks are extremely flexible models due to their large number of parameters, which is beneficial for learning, but also highly redundant.

Tensor2Tensor for Neural Machine Translation

15 code implementations WS 2018 Ashish Vaswani, Samy Bengio, Eugene Brevdo, Francois Chollet, Aidan N. Gomez, Stephan Gouws, Llion Jones, Łukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, Jakob Uszkoreit

Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.

Machine Translation Translation

Unsupervised Cipher Cracking Using Discrete GANs

1 code implementation ICLR 2018 Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser

This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext.

The Reversible Residual Network: Backpropagation Without Storing Activations

8 code implementations NeurIPS 2017 Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse

Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider.

General Classification Image Classification

One Model To Learn Them All

1 code implementation16 Jun 2017 Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer, Ashish Vaswani, Niki Parmar, Llion Jones, Jakob Uszkoreit

We present a single model that yields good results on a number of problems spanning multiple domains.

Image Captioning Image Classification +2

Attention Is All You Need

511 code implementations NeurIPS 2017 Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.

Ranked #2 on Multimodal Machine Translation on Multi30K (BLUE (DE-EN) metric)

Abstractive Text Summarization Constituency Parsing +2

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