research
∙
02/21/2022
Transformer Quality in Linear Time
We revisit the design choices in Transformers, and propose methods to ad...
share
research
∙
11/19/2021
Combined Scaling for Zero-shot Transfer Learning
We present a combined scaling method called BASIC that achieves 85.7 zer...
research
∙
09/17/2021
Primer: Searching for Efficient Transformers for Language Modeling
Large Transformer models have been central to recent advances in natural...
research
∙
08/24/2021
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
With recent progress in joint modeling of visual and textual representat...
research
∙
07/12/2021
Combiner: Full Attention Transformer with Sparse Computation Cost
Transformers provide a class of expressive architectures that are extrem...
research
∙
06/09/2021
CoAtNet: Marrying Convolution and Attention for All Data Sizes
Transformers have attracted increasing interests in computer vision, but...
research
∙
05/17/2021
Pay Attention to MLPs
Transformers have become one of the most important architectural innovat...
research
∙
09/18/2020
Unsupervised Parallel Corpus Mining on Web Data
With a large amount of parallel data, neural machine translation systems...
research
∙
06/05/2020
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
With the success of language pretraining, it is highly desirable to deve...
research
∙
03/23/2020
Meta Pseudo Labels
Many training algorithms of a deep neural network can be interpreted as ...
research
∙
10/18/2019
A Mutual Information Maximization Perspective of Language Representation Learning
We show state-of-the-art word representation learning methods maximize a...
research
∙
06/19/2019
XLNet: Generalized Autoregressive Pretraining for Language Understanding
With the capability of modeling bidirectional contexts, denoising autoen...
research
∙
04/29/2019
Unsupervised Data Augmentation
Despite its success, deep learning still needs large labeled datasets to...
research
∙
02/04/2019
Re-examination of the Role of Latent Variables in Sequence Modeling
With latent variables, stochastic recurrent models have achieved state-o...
research
∙
01/09/2019
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Transformer networks have a potential of learning longer-term dependency...
research
∙
11/24/2018
Characterizing and Avoiding Negative Transfer
When labeled data is scarce for a specific target task, transfer learnin...
research
∙
09/25/2018
Fast and Simple Mixture of Softmaxes with BPE and Hybrid-LightRNN for Language Generation
Mixture of Softmaxes (MoS) has been shown to be effective at addressing ...
research
∙
08/22/2018
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation
In this work, we examine methods for data augmentation for text-based ta...
research
∙
04/29/2018
From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction
In this work, we study the credit assignment problem in reward augmented...
research
∙
11/10/2017
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
We formulate language modeling as a matrix factorization problem, and sh...
research
∙
11/09/2017
Large-scale Cloze Test Dataset Designed by Teachers
Cloze test is widely adopted in language exams to evaluate students' lan...
research
∙
05/31/2017
Controllable Invariance through Adversarial Feature Learning
Learning meaningful representations that maintain the content necessary ...
research
∙
05/27/2017
Good Semi-supervised Learning that Requires a Bad GAN
Semi-supervised learning methods based on generative adversarial network...
research
∙
04/19/2017
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Knowledge bases are important resources for a variety of natural languag...
research
∙
06/07/2016
