39 citations · 76 across the 4 of their papers we have counts for
6 papers · 1 filter
CEREAL: Few-Sample Clustering Evaluation
Nihal V. Nayak, Ethan R. Elenberg, Clemens Rosenbaum
Evaluating clustering quality with reliable evaluation metrics like normalized mutual information (NMI) requires labeled data that can be expensive to annotate. We focus on the und…
On the Role of Weight Sharing During Deep Option Learning
Matthew Riemer, Ignacio Cases, Clemens Rosenbaum +2
The options framework is a popular approach for building temporally extended actions in reinforcement learning. In particular, the option-critic architecture provides general purpo…
Routing Networks and the Challenges of Modular and Compositional Computation
Clemens Rosenbaum, Ignacio Cases, Matthew Riemer +1
Compositionality is a key strategy for addressing combinatorial complexity and the curse of dimensionality. Recent work has shown that compositional solutions can be learned and of…
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning
Clemens Rosenbaum, Tim Klinger, Matthew Riemer
Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of trans…
e-QRAQ: A Multi-turn Reasoning Dataset and Simulator with Explanations
Clemens Rosenbaum, Tian Gao, Tim Klinger
In this paper we present a new dataset and user simulator e-QRAQ (explainable Query, Reason, and Answer Question) which tests an Agent's ability to read an ambiguous text; ask ques…
Deep Reinforcement Learning With Macro-Actions
Ishan P. Durugkar, Clemens Rosenbaum, Stefan Dernbach +1
Deep reinforcement learning has been shown to be a powerful framework for learning policies from complex high-dimensional sensory inputs to actions in complex tasks, such as the At…