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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.
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.
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.
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning
Massive Open Online Courses (MOOCs) have become popular platforms for online learning.
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.
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.
