Search Results for author: Lukasz Kaiser

Found 18 papers, 14 papers with code

Training Verifiers to Solve Math Word Problems

2 code implementations27 Oct 2021 Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman

State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning.

Mathematical Reasoning

Rethinking Attention with Performers

10 code implementations ICLR 2021 Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller

We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness.

Image Generation

Parallel Scheduled Sampling

no code implementations11 Jun 2019 Daniel Duckworth, Arvind Neelakantan, Ben Goodrich, Lukasz Kaiser, Samy Bengio

Experimentally, we find the proposed technique leads to equivalent or better performance on image generation, summarization, dialog generation, and translation compared to teacher-forced training.

Image Generation

Sample Efficient Text Summarization Using a Single Pre-Trained Transformer

2 code implementations21 May 2019 Urvashi Khandelwal, Kevin Clark, Dan Jurafsky, Lukasz Kaiser

Language model (LM) pre-training has resulted in impressive performance and sample efficiency on a variety of language understanding tasks.

 Ranked #1 on Text Summarization on DUC 2004 Task 1 (ROUGE-2 metric)

Abstractive Text Summarization +3

Model-Based Reinforcement Learning for Atari

4 code implementations1 Mar 2019 Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Model-based Reinforcement Learning +1

Area Attention

1 code implementation ICLR 2019 Yang Li, Lukasz Kaiser, Samy Bengio, Si Si

We propose area attention: a way to attend to areas in the memory, where each area contains a group of items that are structurally adjacent, e. g., spatially for a 2D memory such as images, or temporally for a 1D memory such as natural language sentences.

Image Captioning Machine Translation +1

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.

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

493 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

Adding Gradient Noise Improves Learning for Very Deep Networks

5 code implementations21 Nov 2015 Arvind Neelakantan, Luke Vilnis, Quoc V. Le, Ilya Sutskever, Lukasz Kaiser, Karol Kurach, James Martens

This success is partially attributed to architectural innovations such as convolutional and long short-term memory networks.

Question Answering

Multi-task Sequence to Sequence Learning

no code implementations19 Nov 2015 Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, Lukasz Kaiser

This paper examines three multi-task learning (MTL) settings for sequence to sequence models: (a) the oneto-many setting - where the encoder is shared between several tasks such as machine translation and syntactic parsing, (b) the many-to-one setting - useful when only the decoder can be shared, as in the case of translation and image caption generation, and (c) the many-to-many setting - where multiple encoders and decoders are shared, which is the case with unsupervised objectives and translation.

Machine Translation Multi-Task Learning +1

Grammar as a Foreign Language

8 code implementations NeurIPS 2015 Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton

Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades.

Constituency Parsing

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