Search Results for author:
Found 6 papers, 2 papers with code
Collage: Seamless Integration of Deep Learning Backends with Automatic Placement
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.
Attentive Interaction Model: Modeling Changes in View in Argumentation
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.
Time-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States
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.
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning
Massive Open Online Courses (MOOCs) have become popular platforms for online learning.
Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos
This paper addresses a key challenge in MOOC dropout prediction, namely to build meaningful representations from clickstream data.
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning
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.
