37 citations · 127 across the 31 of their papers we have counts for
5 papers · 1 filter
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks
Ileana Rugina, Rumen Dangovski, Li Jing +2
Attention mechanisms play a crucial role in the neural revolution of Natural Language Processing (NLP). With the growth of attention-based models, several pruning techniques have b…
Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in NLP Applications
Matthew Khoury, Rumen Dangovski, Longwu Ou +3
Deep neural networks have become the standard approach to building reliable Natural Language Processing (NLP) applications, ranging from Neural Machine Translation (NMT) to dialogu…
Contextualizing Enhances Gradient Based Meta Learning
Evan Vogelbaum, Rumen Dangovski, Li Jing +1
Meta learning methods have found success when applied to few shot classification problems, in which they quickly adapt to a small number of labeled examples. Prototypical represent…
OccamNet: A Fast Neural Model for Symbolic Regression at Scale
Owen Dugan, Rumen Dangovski, Allan Costa +4
Neural networks' expressiveness comes at the cost of complex, black-box models that often extrapolate poorly beyond the domain of the training dataset, conflicting with the goal of…
On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning
Guillem Ramírez, Rumen Dangovski, Preslav Nakov +1
The emergence of unsupervised word embeddings, pre-trained on very large monolingual text corpora, is at the core of the ongoing neural revolution in Natural Language Processing (N…