100 citations · 438 across the 29 of their papers we have counts for
6 papers · 1 filter
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
Brandon Reagen, Udit Gupta, Robert Adolf +4
The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of t…
Dilated Convolutions for Modeling Long-Distance Genomic Dependencies
Ankit Gupta, Alexander M. Rush
We consider the task of detecting regulatory elements in the human genome directly from raw DNA. Past work has focused on small snippets of DNA, making it difficult to model long-d…
OpenNMT: Open-source Toolkit for Neural Machine Translation
Guillaume Klein, Yoon Kim, Yuntian Deng +3
We introduce an open-source toolkit for neural machine translation (NMT) to support research into model architectures, feature representations, and source modalities, while maintai…
Adapting Sequence Models for Sentence Correction
Allen Schmaltz, Yoon Kim, Alexander M. Rush +1
In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based…
Challenges in Data-to-Document Generation
Sam Wiseman, Stuart M. Shieber, Alexander M. Rush
Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records. In this work, we sugges…
Structured Attention Networks
Yoon Kim, Carl Denton, Luong Hoang +1
Attention networks have proven to be an effective approach for embedding categorical inference within a deep neural network. However, for many tasks we may want to model richer str…