18 citations · 21 across the 4 of their papers we have counts for
5 papers · 1 filter
Parameter-Efficient Transfer Learning with Diff Pruning
Demi Guo, Alexander M. Rush, Yoon Kim
While task-specific finetuning of pretrained networks has led to significant empirical advances in NLP, the large size of networks makes finetuning difficult to deploy in multi-tas…
Sequence-Level Mixed Sample Data Augmentation
Demi Guo, Yoon Kim, Alexander M. Rush
Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. This work proposes a simple data augmentation approach t…
MicroNet for Efficient Language Modeling
Zhongxia Yan, Hanrui Wang, Demi Guo +1
It is important to design compact language models for efficient deployment. We improve upon recent advances in both the language modeling domain and the model-compression domain to…
Analyzing machine-learned representations: A natural language case study
Ishita Dasgupta, Demi Guo, Samuel J. Gershman +1
As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises of how the representations and decision rules they le…
Evaluating Compositionality in Sentence Embeddings
Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller +2
An important challenge for human-like AI is compositional semantics. Recent research has attempted to address this by using deep neural networks to learn vector space embeddings of…