Search Results for author: Fabio Ferreira

Found 17 papers, 11 papers with code

Beyond Random Augmentations: Pretraining with Hard Views

no code implementations5 Oct 2023 Fabio Ferreira, Ivo Rapant, Jörg K. H. Franke, Frank Hutter

To achieve this invariance, conventional approaches make use of random sampling operations within the image augmentation pipeline.

Contrastive Learning Image Augmentation +2

Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How

1 code implementation6 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.

Hyperparameter Optimization Image Classification

On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning

no code implementations16 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.

Bayesian Optimization Data Augmentation +2

Zero-Shot AutoML with Pretrained Models

1 code implementation16 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?

AutoML Meta-Learning

Learning Synthetic Environments and Reward Networks for Reinforcement Learning

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.

reinforcement-learning Reinforcement Learning (RL)

MDP Playground: Controlling Orthogonal Dimensions of Hardness in Toy Environments

no code implementations28 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.

OpenAI Gym Reinforcement Learning (RL)

UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands

1 code implementation24 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.

Object valid

Software Engineering Meets Deep Learning: A Mapping Study

no code implementations25 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.

MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning

1 code implementation17 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.

OpenAI Gym reinforcement-learning +1

Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases

1 code implementation9 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.

Inductive Bias

Noise Regularization for Conditional Density Estimation

1 code implementation21 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.

Density Estimation

Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks

1 code implementation3 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 $p(\mathbf{y}|\mathbf{x})$.

Density Estimation

Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets

2 code implementations2 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.

Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution

1 code implementation12 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.


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