no code implementations • 5 Oct 2023 • Fabio Ferreira, Ivo Rapant, Frank Hutter
Many Self-Supervised Learning (SSL) methods train their models to be invariant to different "views" of an image input for which a good data augmentation pipeline is crucial.
1 code implementation • 6 Jun 2023 • Sebastian Pineda Arango, Fabio Ferreira, Arlind Kadra, Frank Hutter, Josif Grabocka
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 dataset.
no code implementations • 16 Jul 2022 • Diane Wagner, Fabio Ferreira, Danny Stoll, Robin Tibor Schirrmeister, Samuel Müller, Frank Hutter
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.
1 code implementation • 16 Jun 2022 • Ekrem Öztürk, Fabio Ferreira, Hadi S. Jomaa, Lars Schmidt-Thieme, Josif Grabocka, Frank Hutter
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, particularly if D is small?
no code implementations • 15 Jun 2022 • Adrian El Baz, Ihsan Ullah, Edesio Alcobaça, André C. P. L. F. Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Chaoyu Guan, Isabelle Guyon, Timothy Hospedales, Shell Hu, Mike Huisman, Frank Hutter, Zhengying Liu, Felix Mohr, Ekrem Öztürk, Jan N. van Rijn, Haozhe Sun, Xin Wang, Wenwu Zhu
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 resources, restricting their success to domains where such resources are available.
1 code implementation • ICLR 2022 • Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger, Frank Hutter
In a one-to-one comparison, learning an SE proxy requires more interactions with the real environment than training agents only on the real environment.
no code implementations • 11 Jan 2022 • Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, Frank Hutter, Rongrong Ji, Julio C. S. Jacques Junior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sebastien Treguer, Jin Wang, Peng Wang, Chenglin Wu, Youcheng Xiong, Arbe r Zela, Yang Zhang
Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly.
1 code implementation • 24 Jan 2021 • Fabio Ferreira, Thomas Nierhoff, Frank Hutter
This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning.
no code implementations • 28 Sep 2020 • Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp, Frank Hutter
We present MDP Playground, an efficient benchmark for Reinforcement Learning (RL) algorithms with various dimensions of hardness that can be controlled independently to challenge algorithms in different ways and to obtain varying degrees of hardness in generated environments.
1 code implementation • 24 Oct 2019 • Lin Shao, Fabio Ferreira, Mikael Jorda, Varun Nambiar, Jianlan Luo, Eugen Solowjow, Juan Aparicio Ojea, Oussama Khatib, Jeannette Bohg
The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand.
no code implementations • 25 Sep 2019 • Fabio Ferreira, Luciana Lourdes Silva, Marco Tulio Valente
Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks.
1 code implementation • 17 Sep 2019 • Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda, Fabio Ferreira, André Biedenkapp, Jan Ole von Hartz, Frank Hutter
We define a parameterised collection of fast-to-run toy environments in OpenAI Gym by varying these dimensions and propose to use these to understand agents better.
1 code implementation • 9 Sep 2019 • Fabio Ferreira, Lin Shao, Tamim Asfour, Jeannette Bohg
The first, Graph Networks (GN) based approach, considers explicitly defined edge attributes and not only does it consistently underperform an auto-encoder baseline that we modified to predict future states, our results indicate how different edge attributes can significantly influence the predictions.
1 code implementation • 21 Jul 2019 • Jonas Rothfuss, Fabio Ferreira, Simon Boehm, Simon Walther, Maxim Ulrich, Tamim Asfour, Andreas Krause
To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.
1 code implementation • 3 Mar 2019 • Jonas Rothfuss, Fabio Ferreira, Simon Walther, Maxim Ulrich
Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $\mathbf{x}$ and a dependent variable $\mathbf{y}$ by modeling their conditional probability $p(\mathbf{y}|\mathbf{x})$.
2 code implementations • 2 Jul 2018 • Fabio Ferreira, Jonas Rothfuss, Eren Erdal Aksoy, You Zhou, Tamim Asfour
We release two artificial datasets, Simulated Flying Shapes and Simulated Planar Manipulator that allow to test the learning ability of video processing systems.
1 code implementation • 12 Jan 2018 • Jonas Rothfuss, Fabio Ferreira, Eren Erdal Aksoy, You Zhou, Tamim Asfour
We present a novel deep neural network architecture for representing robot experiences in an episodic-like memory which facilitates encoding, recalling, and predicting action experiences.