no code implementations • EMNLP (sdp) 2020 • Rohan Bhambhoria, Luna Feng, Dawn Sepehr, John Chen, Conner Cowling, Sedef Kocak, Elham Dolatabadi
Automatically generating question answer (QA) pairs from the rapidly growing coronavirus-related literature is of great value to the medical community.
no code implementations • 7 Sep 2023 • John Chen, Chen Dun, Anastasios Kyrillidis
Advances in Semi-Supervised Learning (SSL) have almost entirely closed the gap between SSL and Supervised Learning at a fraction of the number of labels.
no code implementations • 16 Aug 2023 • John Chen, Uri Wilensky
Building on Papert (1980)'s idea of children talking to computers, we propose ChatLogo, a hybrid natural-programming language interface for agent-based modeling and programming.
no code implementations • 29 Sep 2021 • Gaurav Gupta, Benjamin Coleman, John Chen, Anshumali Shrivastava
To this end, we propose STORM, an online sketching-based method for empirical risk minimization.
1 code implementation • 9 Jul 2021 • John Chen, Cameron Wolfe, Anastasios Kyrillidis
Deep learning practitioners often operate on a computational and monetary budget.
no code implementations • 2 Jul 2021 • John Chen, Qihan Wang, Anastasios Kyrillidis
In this work, we explore the connection between the double descent phenomena and the number of samples in the deep neural network setting.
no code implementations • 16 Jan 2021 • Hillary Ngai, Yoona Park, John Chen, Mahboobeh Parsapoor
In response to the Kaggle's COVID-19 Open Research Dataset (CORD-19) challenge, we have proposed three transformer-based question-answering systems using BERT, ALBERT, and T5 models.
no code implementations • 25 Nov 2020 • John Chen, Samarth Sinha, Anastasios Kyrillidis
On its own, improvements with StackMix hold across different number of labeled samples on CIFAR-100, maintaining approximately a 2\% gap in test accuracy -- down to using only 5\% of the whole dataset -- and is effective in the semi-supervised setting with a 2\% improvement with the standard benchmark $\Pi$-model.
1 code implementation • EMNLP (ClinicalNLP) 2020 • John Chen, Ian Berlot-Attwell, Safwan Hossain, Xindi Wang, Frank Rudzicz
Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as freetext.
no code implementations • 25 Jun 2020 • Benjamin Coleman, Gaurav Gupta, John Chen, Anshumali Shrivastava
To this end, we propose STORM, an online sketch for empirical risk minimization.
1 code implementation • ICML 2020 • John Chen, Vatsal Shah, Anastasios Kyrillidis
We introduce Negative Sampling in Semi-Supervised Learning (NS3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL).
2 code implementations • 11 Oct 2019 • John Chen, Cameron Wolfe, Zhao Li, Anastasios Kyrillidis
Momentum is a widely used technique for gradient-based optimizers in deep learning.
no code implementations • 25 Sep 2019 • John Chen, Anastasios Kyrillidis
Momentum is a simple and popular technique in deep learning for gradient-based optimizers.
1 code implementation • 23 Aug 2019 • John Chen, Ben Coleman, Anshumali Shrivastava
We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses.
Data Structures and Algorithms
no code implementations • NAACL 2019 • Yue Chen, John Chen
For the experiments we perform a comparison between using pseudo-data and real world data.
no code implementations • 11 May 2018 • Nicholas Ruiz, Srinivas Bangalore, John Chen
With the resurgence of chat-based dialog systems in consumer and enterprise applications, there has been much success in developing data-driven and rule-based natural language models to understand human intent.
no code implementations • RANLP 2017 • John Chen, Srinivas Bangalore
With the increasing number of communication platforms that offer variety of ways of connecting two interlocutors, there is a resurgence of chat-based dialog systems.