114 citations · 144 across the 12 of their papers we have counts for
9 papers · 1 filter
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
Sebastian Pineda Arango, Fabio Ferreira, Arlind Kadra +2
With the ever-increasing number of pretrained models, machine learning practitioners are continuously faced with which pretrained model to use, and how to finetune it for a new dat…
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning
Diane Wagner, Fabio Ferreira, Danny Stoll +3
Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks. However…
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Adrian El Baz, Ihsan Ullah, Edesio Alcobaça +17
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resourc…
Zero-Shot AutoML with Pretrained Models
Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa +3
Given a new dataset D and a low compute budget, how should we choose a pre-trained model to fine-tune to D, and set the fine-tuning hyperparameters without risking overfitting, par…
Learning Synthetic Environments and Reward Networks for Reinforcement Learning
Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger +1
We introduce Synthetic Environments (SEs) and Reward Networks (RNs), represented by neural networks, as proxy environment models for training Reinforcement Learning (RL) agents. We…
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Zhengying Liu, Adrien Pavao, Zhen Xu +22
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) th…
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies
Fabio Ferreira, Thomas Nierhoff, Frank Hutter
This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more e…
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases
Fabio Ferreira, Lin Shao, Tamim Asfour +1
Endowing robots with human-like physical reasoning abilities remains challenging. We argue that existing methods often disregard spatio-temporal relations and by using Graph Neural…
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…