activity
20182022
most citedPointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

95 citations · 246 across the 10 of their papers we have counts for

collaborators

11 papers

cs.CV2022

Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic Images in Facial Capture Pipelines

Winnie Lin, Yilin Zhu, Demi Guo +1

We propose an end-to-end pipeline for both building and tracking 3D facial models from personalized in-the-wild (cellphone, webcam, youtube clips, etc.) video data. First, we prese…

cs.CL2020★ 23 cited

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…

cs.CL2020★ 3 cited

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…

cs.CV2020★ 95 cited

PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding

Saining Xie, Jiatao Gu, Demi Guo +3

Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (eg., ImageNet) can help boost performan…

cs.CL2020

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…

cs.CL2019

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…

cs.AI2019★ 10 cited

Why Build an Assistant in Minecraft?

Arthur Szlam, Jonathan Gray, Kavya Srinet +11

In this document we describe a rationale for a research program aimed at building an open "assistant" in the game Minecraft, in order to make progress on the problems of natural la…

cs.AI2019★ 18 cited

CraftAssist: A Framework for Dialogue-enabled Interactive Agents

Jonathan Gray, Kavya Srinet, Yacine Jernite +6

This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions. The p…

cs.DB2018

The Internals of the Data Calculator

Stratos Idreos, Kostas Zoumpatianos, Brian Hentschel +2

Data structures are critical in any data-driven scenario, but they are notoriously hard to design due to a massive design space and the dependence of performance on workload and ha…

stat.ML2018★ 79 cited

Latent Alignment and Variational Attention

Yuntian Deng, Yoon Kim, Justin Chiu +2

Neural attention has become central to many state-of-the-art models in natural language processing and related domains. Attention networks are an easy-to-train and effective method…

cs.CL2018★ 18 cited

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…