| 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 | 120 | 2020 |
| 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 | 35 | 2019 |
| 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 | 34 | 2021 |
| 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 | 29 | 2019 |
| 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 | 20 | 2022 |
| 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 | 15 | 2022 |
| 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 | 5 | 2019 |
| 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 | 1 | 2024 |
| Forward-looking P M Ulrich, S Walther, J Rothfuss, F Ferreira | 1 | 2019 |
| 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 |
Frank HutterELLIS Institute Tübingen & Professor, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
