22k citations · 25.2k across the 19 of their papers we have counts for
23 papers
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot +10
A classical problem in computer vision is to infer a 3D scene representation from few images that can be used to render novel views at interactive rates. Previous work focuses on r…
MLP-Mixer: An all-MLP Architecture for Vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov +9
Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this…
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai +3
Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and seman…
Differentiable Patch Selection for Image Recognition
Jean-Baptiste Cordonnier, Aravindh Mahendran, Alexey Dosovitskiy +3
Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand…
Towards End-to-End In-Image Neural Machine Translation
Elman Mansimov, Mitchell Stern, Mia Chen +3
In this paper, we offer a preliminary investigation into the task of in-image machine translation: transforming an image containing text in one language into an image containing th…
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov +9
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention…
Object-Centric Learning with Slot Attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner +5
Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learn…
An Empirical Study of Generation Order for Machine Translation
William Chan, Mitchell Stern, Jamie Kiros +1
In this work, we present an empirical study of generation order for machine translation. Building on recent advances in insertion-based modeling, we first introduce a soft order-re…
KERMIT: Generative Insertion-Based Modeling for Sequences
William Chan, Nikita Kitaev, Kelvin Guu +2
We present KERMIT, a simple insertion-based approach to generative modeling for sequences and sequence pairs. KERMIT models the joint distribution and its decompositions (i.e., mar…
Scaling Autoregressive Video Models
Dirk Weissenborn, Oscar Täckström, Jakob Uszkoreit
Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-t…
Insertion Transformer: Flexible Sequence Generation via Insertion Operations
Mitchell Stern, William Chan, Jamie Kiros +1
We present the Insertion Transformer, an iterative, partially autoregressive model for sequence generation based on insertion operations. Unlike typical autoregressive models which…
Blockwise Parallel Decoding for Deep Autoregressive Models
Mitchell Stern, Noam Shazeer, Jakob Uszkoreit
Deep autoregressive sequence-to-sequence models have demonstrated impressive performance across a wide variety of tasks in recent years. While common architecture classes such as r…
Music Transformer
Cheng-Zhi Anna Huang, Ashish Vaswani, Jakob Uszkoreit +7
Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such…
Universal Transformers
Mostafa Dehghani, Stephan Gouws, Oriol Vinyals +2
Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. How…
Self-Attention with Relative Position Representations
Peter Shaw, Jakob Uszkoreit, Ashish Vaswani
Relying entirely on an attention mechanism, the Transformer introduced by Vaswani et al. (2017) achieves state-of-the-art results for machine translation. In contrast to recurrent…
Tensor2Tensor for Neural Machine Translation
Ashish Vaswani, Samy Bengio, Eugene Brevdo +10
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…
Fast Decoding in Sequence Models using Discrete Latent Variables
Łukasz Kaiser, Aurko Roy, Ashish Vaswani +4
Autoregressive sequence models based on deep neural networks, such as RNNs, Wavenet and the Transformer attain state-of-the-art results on many tasks. However, they are difficult t…
Image Transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit +4
Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of mod…
One Model To Learn Them All
Lukasz Kaiser, Aidan N. Gomez, Noam Shazeer +4
Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involv…
Attention Is All You Need
Ashish Vaswani, Noam Shazeer, Niki Parmar +5
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also conne…
Neural Paraphrase Identification of Questions with Noisy Pretraining
Gaurav Singh Tomar, Thyago Duque, Oscar Täckström +2
We present a solution to the problem of paraphrase identification of questions. We focus on a recent dataset of question pairs annotated with binary paraphrase labels and show that…
Hierarchical Question Answering for Long Documents
Eunsol Choi, Daniel Hewlett, Alexandre Lacoste +3
We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most…
A Decomposable Attention Model for Natural Language Inference
Ankur P. Parikh, Oscar Täckström, Dipanjan Das +1
We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus ma…