100 citations · 438 across the 31 of their papers we have counts for
15 papers · 1 filter
A Hierarchy of Graph Neural Networks Based on Learnable Local Features
Michael Lingzhi Li, Meng Dong, Jiawei Zhou +1
Graph neural networks (GNNs) are a powerful tool to learn representations on graphs by iteratively aggregating features from node neighbourhoods. Many variant models have been prop…
Encoder-Agnostic Adaptation for Conditional Language Generation
Zachary M. Ziegler, Luke Melas-Kyriazi, Sebastian Gehrmann +1
Large pretrained language models have changed the way researchers approach discriminative natural language understanding tasks, leading to the dominance of approaches that adapt a…
Neural Linguistic Steganography
Zachary M. Ziegler, Yuntian Deng, Alexander M. Rush
Whereas traditional cryptography encrypts a secret message into an unintelligible form, steganography conceals that communication is taking place by encoding a secret message into…
Commonsense Knowledge Mining from Pretrained Models
Joshua Feldman, Joe Davison, Alexander M. Rush
Inferring commonsense knowledge is a key challenge in natural language processing, but due to the sparsity of training data, previous work has shown that supervised methods for com…
AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference
Thierry Tambe, En-Yu Yang, Zishen Wan +5
Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequate…
MASR: A Modular Accelerator for Sparse RNNs
Udit Gupta, Brandon Reagen, Lillian Pentecost +5
Recurrent neural networks (RNNs) are becoming the de facto solution for speech recognition. RNNs exploit long-term temporal relationships in data by applying repeated, learned tran…