Search Results for author: Byungsoo Jeon

Found 6 papers, 2 papers with code

Collage: Seamless Integration of Deep Learning Backends with Automatic Placement

1 code implementation1 Nov 2021 Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia

Given the fast-evolving nature of the DL ecosystem, this manual approach often slows down continuous innovations across different layers; it prevents hardware vendors from the fast deployment of their cutting-edge libraries, DL framework developers must repeatedly adjust their hand-coded rules to accommodate new versions of libraries, and machine learning practitioners need to wait for the integration of new technologies and often encounter unsatisfactory performance.

FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning

no code implementations1 Jan 2021 Bharathan Balaji, Petros Christodoulou, Xiaoyu Lu, Byungsoo Jeon, Jordan Bell-Masterson

We propose a simple class of deep reinforcement learning (RL) methods, called FactoredRL, that can leverage factored environment structures to improve the sample efficiency of existing model-based and model-free RL algorithms.

reinforcement-learning Reinforcement Learning (RL)

Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos

no code implementations5 Feb 2020 Byungsoo Jeon, Namyong Park

This paper addresses a key challenge in MOOC dropout prediction, namely to build meaningful representations from clickstream data.

Time-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States

no code implementations1 May 2019 Byungsoo Jeon, Eyal Shafran, Luke Breitfeller, Jason Levin, Carolyn P. Rose

This paper addresses a key challenge in Educational Data Mining, namely to model student behavioral trajectories in order to provide a means for identifying students most at-risk, with the goal of providing supportive interventions.

Time Series Analysis

Attentive Interaction Model: Modeling Changes in View in Argumentation

1 code implementation NAACL 2018 Yohan Jo, Shivani Poddar, Byungsoo Jeon, Qinlan Shen, Carolyn P. Rose, Graham Neubig

We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument, with the goal of predicting if the argument successfully changes the OH's view.

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