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Fabio Ferreira
Fabio Ferreira
Sonstige NamenFábio Ferreira
PhD student, University Freiburg
Bestätigte E-Mail-Adresse bei cs.uni-freiburg.de
Titel
Zitiert von
Zitiert von
Jahr
Unigrasp: Learning a unified model to grasp with multifingered robotic hands
L Shao, F Ferreira, M Jorda, V Nambiar, J Luo, E Solowjow, JA Ojea, ...
IEEE Robotics and Automation Letters (RAL) 5 (2), 2286-2293, 2020
1202020
Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks
F Ferreira, J Rothfuss, S Walther, M Ulrich
arXiv preprint arXiv:1903.00954, 2019
92*2019
Deep episodic memory: Encoding, recalling, and predicting episodic experiences for robot action execution
F Ferreira, J Rothfuss, EE Aksoy, Y Zhou, T Asfour
IEEE Robotics and Automation Letters (RAL) 3 (4), 4007-4014, 2018
47*2018
Noise regularization for conditional density estimation
J Rothfuss, F Ferreira, S Boehm, S Walther, M Ulrich, T Asfour, A Krause
arXiv preprint arXiv:1907.08982, 2019
352019
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Z Liu, A Pavao, Z Xu, S Escalera, F Ferreira, I Guyon, S Hong, F Hutter, ...
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 43 (9 …, 2021
342021
Visuo-haptic grasping of unknown objects based on gaussian process implicit surfaces and deep learning
S Ottenhaus, D Renninghoff, R Grimm, F Ferreira, T Asfour
2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids …, 2019
292019
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning
D Wagner, F Ferreira, D Stoll, RT Schirrmeister, S Müller, F Hutter
International Conference on Machine Learning (ICML) 2022 Pre-Training Workshop, 2022
202022
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
A El Baz, I Ullah, E Alcobaça, AC Carvalho, H Chen, F Ferreira, H Gouk, ...
NeurIPS 2021 Competitions and Demonstrations Track, 80-96, 2022
152022
Zero-Shot AutoML with Pretrained Models
F Ferreira, E Öztürk, HS Jomaa, L Schmidt-Thieme, J Grabocka, F Hutter
International Conference on Machine Learning (ICML) 2022, 2022
15*2022
Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How
SP Arango, F Ferreira, A Kadra, F Hutter, J Grabocka
International Conference on Learning Representations (ICLR) 2024 (oral …, 2023
12*2023
MDP Playground: Controlling Dimensions of Hardness in Reinforcement Learning
R Rajan, JLB Diaz, S Guttikonda, F Ferreira, A Biedenkapp, F Hutter
arXiv preprint arXiv:1909.07750, 2020
11*2020
Learning Synthetic Environments and Reward Networks for Reinforcement Learning
F Ferreira, T Nierhoff, A Saelinger, F Hutter
International Conference on Learning Representations (ICLR) 2022, https …, 2022
10*2022
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases
F Ferreira, L Shao, T Asfour, J Bohg
Conference on Neural Information Processing Systems (NeurIPS 2019) 33 (Graph …, 2019
52019
Quick-Tune-Tool: A Practical Tool and its User Guide for Automatically Finetuning Pretrained Models
I Rapant, L Purucker, F Ferreira, SP Arango, A Kadra, J Grabocka, ...
AutoML Conference 2024 (Workshop Track), 2024
12024
Forward-looking P
M Ulrich, S Walther, J Rothfuss, F Ferreira
12019
Beyond Random Augmentations: Pretraining with Hard Views
F Ferreira, I Rapant, J Franke K. H., F Hutter
International Conference on Learning Representations (ICLR) 2025, 2025
2025
Transfer Learning for Finetuning Large Language Models
T Strangmann, L Purucker, JKH Franke, I Rapant, F Ferreira, F Hutter
NeurIPS 2024 Workshop on Adaptive Foundation Models, 2024
2024
One-shot World Models Using a Transformer Trained on a Synthetic Prior
F Ferreira, M Schlageter, R Rajan, A Biedenkapp, F Hutter
NeurIPS 2024 Workshop on Open-World Agents, 2024
2024
Device and method to improve reinforcement learning with synthetic environment
T Nierhoff, F Ferreira, F Hutter
US Patent App. 17/644,179, 2022
2022
Method and device for creating a system for the automated creation of machine learning systems
M Lindauer, A Zela, DO Stoll, F Ferreira, F Hutter, T Nierhoff
US Patent App. 17/366,639, 2022
2022
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