Search Results for author: Ashish Vaswani

Found 34 papers, 16 papers with code

The Efficiency Misnomer

no code implementations ICLR 2022 Mostafa Dehghani, Anurag Arnab, Lucas Beyer, Ashish Vaswani, Yi Tay

We further present suggestions to improve reporting of efficiency metrics.

Scale Efficiently: Insights from Pretraining and Finetuning Transformers

no code implementations ICLR 2022 Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler

The key findings of this paper are as follows: (1) we show that aside from only the model size, model shape matters for downstream fine-tuning, (2) scaling protocols operate differently at different compute regions, (3) widely adopted T5-base and T5-large sizes are Pareto-inefficient.

Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

2 code implementations22 Sep 2021 Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler

The key findings of this paper are as follows: (1) we show that aside from only the model size, model shape matters for downstream fine-tuning, (2) scaling protocols operate differently at different compute regions, (3) widely adopted T5-base and T5-large sizes are Pareto-inefficient.

Simple and Efficient ways to Improve REALM

no code implementations EMNLP (MRQA) 2021 Vidhisha Balachandran, Ashish Vaswani, Yulia Tsvetkov, Niki Parmar

Dense retrieval has been shown to be effective for retrieving relevant documents for Open Domain QA, surpassing popular sparse retrieval methods like BM25.

Scaling Local Self-Attention for Parameter Efficient Visual Backbones

7 code implementations CVPR 2021 Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, Jonathon Shlens

Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50.

Image Classification Instance Segmentation +4

Bottleneck Transformers for Visual Recognition

12 code implementations CVPR 2021 Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani

Finally, we present a simple adaptation of the BoTNet design for image classification, resulting in models that achieve a strong performance of 84. 7% top-1 accuracy on the ImageNet benchmark while being up to 1. 64x faster in compute time than the popular EfficientNet models on TPU-v3 hardware.

Image Classification Instance Segmentation +2

Efficient Content-Based Sparse Attention with Routing Transformers

2 code implementations12 Mar 2020 Aurko Roy, Mohammad Saffar, Ashish Vaswani, David Grangier

This work builds upon two lines of research: it combines the modeling flexibility of prior work on content-based sparse attention with the efficiency gains from approaches based on local, temporal sparse attention.

Image Generation Language Modelling

Stand-Alone Self-Attention in Vision Models

7 code implementations NeurIPS 2019 Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens

The natural question that arises is whether attention can be a stand-alone primitive for vision models instead of serving as just an augmentation on top of convolutions.

object-detection Object Detection

Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation

no code implementations ACL 2019 Vihan Jain, Gabriel Magalhaes, Alexander Ku, Ashish Vaswani, Eugene Ie, Jason Baldridge

We also show that the existing paths in the dataset are not ideal for evaluating instruction following because they are direct-to-goal shortest paths.

Vision and Language Navigation

Towards a better understanding of Vector Quantized Autoencoders

no code implementations ICLR 2019 Aurko Roy, Ashish Vaswani, Niki Parmar, Arvind Neelakantan

Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.

Knowledge Distillation Machine Translation +1

Music Transformer

10 code implementations ICLR 2019 Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit, Noam Shazeer, Ian Simon, Curtis Hawthorne, Andrew M. Dai, Matthew D. Hoffman, Monica Dinculescu, Douglas Eck

This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length.

Music Modeling

Theory and Experiments on Vector Quantized Autoencoders

2 code implementations28 May 2018 Aurko Roy, Ashish Vaswani, Arvind Neelakantan, Niki Parmar

Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks.

Image Generation Knowledge Distillation +2

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

Fast Decoding in Sequence Models using Discrete Latent Variables

no code implementations ICML 2018 Łukasz Kaiser, Aurko Roy, Ashish Vaswani, Niki Parmar, Samy Bengio, Jakob Uszkoreit, Noam Shazeer

Finally, we evaluate our model end-to-end on the task of neural machine translation, where it is an order of magnitude faster at decoding than comparable autoregressive models.

Machine Translation Translation

Self-Attention with Relative Position Representations

9 code implementations NAACL 2018 Peter Shaw, Jakob Uszkoreit, Ashish Vaswani

On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1. 3 BLEU and 0. 3 BLEU over absolute position representations, respectively.

Machine Translation Translation

Image Transformer

no code implementations15 Feb 2018 Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, Dustin Tran

Image generation has been successfully cast as an autoregressive sequence generation or transformation problem.

Ranked #6 on Image Generation on ImageNet 32x32 (bpd metric)

Image Generation Image Super-Resolution

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 +3

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

Unsupervised Neural Hidden Markov Models

2 code implementations WS 2016 Ke Tran, Yonatan Bisk, Ashish Vaswani, Daniel Marcu, Kevin Knight

In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model.

TAG

Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States

1 code implementation TACL 2016 Ashish Vaswani, Kenji Sagae

Transition-based approaches based on local classification are attractive for dependency parsing due to their simplicity and speed, despite producing results slightly below the state-of-the-art.

Feature Engineering General Classification +3

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