research
∙
10/16/2022
Model Criticism for Long-Form Text Generation
Language models have demonstrated the ability to generate highly fluent ...
share
research
∙
10/11/2022
Markup-to-Image Diffusion Models with Scheduled Sampling
Building on recent advances in image generation, we present a fully data...
research
∙
07/02/2022
Interference Constrained Beam Alignment for Time-Varying Channels via Kernelized Bandits
To fully utilize the abundant spectrum resources in millimeter wave (mmW...
research
∙
05/24/2022
Semi-Parametric Deep Neural Networks in Linear Time and Memory
Recent advances in deep learning have been driven by large-scale paramet...
research
∙
01/08/2022
Low-Rank Constraints for Fast Inference in Structured Models
Structured distributions, i.e. distributions over combinatorial spaces, ...
research
∙
09/14/2021
Rationales for Sequential Predictions
Sequence models are a critical component of modern NLP systems, but thei...
research
∙
07/06/2021
Weighted Gaussian Process Bandits for Non-stationary Environments
In this paper, we consider the Gaussian process (GP) bandit optimization...
research
∙
01/09/2021
Incentive Design and Profit Sharing in Multi-modal Transportation Network
We consider the situation where multiple transportation service provider...
research
∙
06/01/2020
Cascaded Text Generation with Markov Transformers
The two dominant approaches to neural text generation are fully autoregr...
research
∙
04/22/2020
Residual Energy-Based Models for Text Generation
Text generation is ubiquitous in many NLP tasks, from summarization, to ...
research
∙
09/29/2019
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Conventional hardware-friendly quantization methods, such as fixed-point...
research
∙
09/03/2019
Neural Linguistic Steganography
Whereas traditional cryptography encrypts a secret message into an unint...
research
∙
06/07/2019
Real or Fake? Learning to Discriminate Machine from Human Generated Text
Recent advances in generative modeling of text have demonstrated remarka...
research
∙
11/16/2018
Statistical Impact of New York Health Legislation
As the US Government plays an increasing role in health care, it becomes...
research
∙
09/11/2018
Visual Attention Model for Cross-sectional Stock Return Prediction and End-to-End Multimodal Market Representation Learning
Technical and fundamental analysis are traditional tools used to analyze...
research
∙
08/31/2018
Bottom-Up Abstractive Summarization
Neural network-based methods for abstractive summarization produce outpu...
research
∙
07/10/2018
Latent Alignment and Variational Attention
Neural attention has become central to many state-of-the-art models in n...
research
∙
05/28/2018
OpenNMT: Neural Machine Translation Toolkit
OpenNMT is an open-source toolkit for neural machine translation (NMT). ...
research
∙
01/10/2017
OpenNMT: Open-Source Toolkit for Neural Machine Translation
We describe an open-source toolkit for neural machine translation (NMT)....
research
∙
09/26/2016
Dropout with Expectation-linear Regularization
Dropout, a simple and effective way to train deep neural networks, has l...
research
∙
09/16/2016
Image-to-Markup Generation with Coarse-to-Fine Attention
We present a neural encoder-decoder model to convert images into present...
research
∙
07/18/2016
Neural Machine Translation with Recurrent Attention Modeling
Knowing which words have been attended to in previous time steps while g...
research
∙
06/29/2016
Learning Concept Taxonomies from Multi-modal Data
We study the problem of automatically building hypernym taxonomies from ...
research
∙
12/23/2015
Latent Variable Modeling with Diversity-Inducing Mutual Angular Regularization
Latent Variable Models (LVMs) are a large family of machine learning mod...
research
∙
10/21/2015
