9.4k citations · 16.5k across the 107 of their papers we have counts for
15 papers · 1 filter
Meta-Learning of Neural Architectures for Few-Shot Learning
Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen +1
The recent progress in neural architecture search (NAS) has allowed scaling the automated design of neural architectures to real-world domains, such as object detection and semanti…
OpenML-Python: an extensible Python API for OpenML
Matthias Feurer, Jan N. van Rijn, Arlind Kadra +6
OpenML is an online platform for open science collaboration in machine learning, used to share datasets and results of machine learning experiments. In this paper we introduce Open…
Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
Matilde Gargiani, Aaron Klein, Stefan Falkner +1
We propose probabilistic models that can extrapolate learning curves of iterative machine learning algorithms, such as stochastic gradient descent for training deep networks, based…
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control
Jörg K. H. Franke, Gregor Köhler, Noor Awad +1
Current Deep Reinforcement Learning algorithms still heavily rely on handcrafted neural network architectures. We propose a novel approach to automatically find strong topologies f…
Understanding and Robustifying Differentiable Architecture Search
Arber Zela, Thomas Elsken, Tonmoy Saikia +3
Differentiable Architecture Search (DARTS) has attracted a lot of attention due to its simplicity and small search costs achieved by a continuous relaxation and an approximation of…
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda +4
We present MDP Playground, a testbed for Reinforcement Learning (RL) agents with dimensions of hardness that can be controlled independently to challenge agents in different ways a…