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Francesco Faccio
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2020 – today
- 2024
- [c9]Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber:
Learning Useful Representations of Recurrent Neural Network Weight Matrices. ICML 2024 - [c8]Yuhui Wang, Weida Li, Francesco Faccio, Qingyuan Wu, Jürgen Schmidhuber:
Highway Value Iteration Networks. ICML 2024 - [c7]Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber:
GPTSwarm: Language Agents as Optimizable Graphs. ICML 2024 - [i18]Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber:
Language Agents as Optimizable Graphs. CoRR abs/2402.16823 (2024) - [i17]Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber:
Learning Useful Representations of Recurrent Neural Network Weight Matrices. CoRR abs/2403.11998 (2024) - [i16]Wentian Zhang, Haozhe Liu, Jinheng Xie, Francesco Faccio, Mike Zheng Shou, Jürgen Schmidhuber:
Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models. CoRR abs/2404.02747 (2024) - [i15]Mohannad Alhakami, Dylan R. Ashley, Joel Dunham, Francesco Faccio, Eric Feron, Jürgen Schmidhuber:
Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms. CoRR abs/2404.08093 (2024) - [i14]Yuhui Wang, Miroslav Strupl, Francesco Faccio, Qingyuan Wu, Haozhe Liu, Michal Grudzien, Xiaoyang Tan, Jürgen Schmidhuber:
Highway Reinforcement Learning. CoRR abs/2405.18289 (2024) - [i13]Yuhui Wang, Weida Li, Francesco Faccio, Qingyuan Wu, Jürgen Schmidhuber:
Highway Value Iteration Networks. CoRR abs/2406.03485 (2024) - [i12]Yuhui Wang, Qingyuan Wu, Weida Li, Dylan R. Ashley, Francesco Faccio, Chao Huang, Jürgen Schmidhuber:
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning. CoRR abs/2406.08404 (2024) - 2023
- [c6]Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
Goal-Conditioned Generators of Deep Policies. AAAI 2023: 7503-7511 - [c5]Haozhe Liu, Mingchen Zhuge, Bing Li, Yuhui Wang, Francesco Faccio, Bernard Ghanem, Jürgen Schmidhuber:
Learning to Identify Critical States for Reinforcement Learning from Videos. ICCV 2023: 1955-1965 - [d1]Andrea Loreggia, Francesco Faccio, Diego Mussolin, Maurizio Zambonin:
Chiller simulated and real-world data. IEEE DataPort, 2023 - [i11]Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, Louis Kirsch, Bing Li, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piekos, Aditya A. Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanic, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber:
Mindstorms in Natural Language-Based Societies of Mind. CoRR abs/2305.17066 (2023) - [i10]Haozhe Liu, Mingchen Zhuge, Bing Li, Yuhui Wang, Francesco Faccio, Bernard Ghanem, Jürgen Schmidhuber:
Learning to Identify Critical States for Reinforcement Learning from Videos. CoRR abs/2308.07795 (2023) - [i9]Aleksandar Stanic, Dylan R. Ashley, Oleg Serikov, Louis Kirsch, Francesco Faccio, Jürgen Schmidhuber, Thomas Hofmann, Imanol Schlag:
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute. CoRR abs/2309.11197 (2023) - 2022
- [j2]Andrea Loreggia, Francesco Faccio, Diego Mussolin, Maurizio Zambonin:
Simulating Floating Head Pressure Control With Artificial Intelligence. IEEE Access 10: 125963-125971 (2022) - [j1]Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl J. Friston:
Bayesian Brains and the Rényi Divergence. Neural Comput. 34(4): 829-855 (2022) - [c4]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Reward-Weighted Regression Converges to a Global Optimum. AAAI 2022: 8361-8369 - [c3]Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber:
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules. NeurIPS 2022 - [i8]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets. CoRR abs/2205.06595 (2022) - [i7]Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber:
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules. CoRR abs/2206.01649 (2022) - [i6]Francesco Faccio, Aditya A. Ramesh, Vincent Herrmann, Jean Harb, Jürgen Schmidhuber:
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States. CoRR abs/2207.01566 (2022) - [i5]Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
Goal-Conditioned Generators of Deep Policies. CoRR abs/2207.01570 (2022) - 2021
- [c2]Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber:
Parameter-Based Value Functions. ICLR 2021 - [i4]Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl J. Friston:
Bayesian brains and the Rényi divergence. CoRR abs/2107.05438 (2021) - [i3]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Reward-Weighted Regression Converges to a Global Optimum. CoRR abs/2107.09088 (2021) - 2020
- [i2]Francesco Faccio, Jürgen Schmidhuber:
Parameter-based Value Functions. CoRR abs/2006.09226 (2020)
2010 – 2019
- 2018
- [c1]Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli:
Policy Optimization via Importance Sampling. NeurIPS 2018: 5447-5459 - [i1]Alberto Maria Metelli, Matteo Papini, Francesco Faccio, Marcello Restelli:
Policy Optimization via Importance Sampling. CoRR abs/1809.06098 (2018)
Coauthor Index
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last updated on 2024-11-15 19:32 CET by the dblp team
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