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Francesco Faccio
Francesco Faccio
Senior Research Scientist, Google DeepMind
Dirección de correo verificada de google.com - Página principal
Título
Citado por
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Año
Policy optimization via importance sampling
AM Metelli, M Papini, F Faccio, M Restelli
NeurIPS 2018 (oral), 2018
1142018
Mindstorms in Natural Language-Based Societies of Mind
M Zhuge*, H Liu*, F Faccio*, DR Ashley*, R Csordás, A Gopalakrishnan, ...
NeurIPSW (Best Paper Award), 2023
712023
GPTSwarm: Language Agents as Optimizable Graphs
M Zhuge, W Wang, L Kirsch, F Faccio, D Khizbullin, J Schmidhuber
ICML 2024 (oral), 2024
38*2024
Parameter-based value functions
F Faccio, L Kirsch, J Schmidhuber
ICLR 2021, 2020
252020
Cross-attention makes inference cumbersome in text-to-image diffusion models
W Zhang, H Liu, J Xie, F Faccio, MZ Shou, J Schmidhuber
arXiv preprint arXiv:2404.02747, 2024
232024
Goal-conditioned generators of deep policies
F Faccio*, V Herrmann*, A Ramesh, L Kirsch, J Schmidhuber
AAAI 2023 (oral), 2023
142023
Neural differential equations for learning to program neural nets through continuous learning rules
K Irie, F Faccio, J Schmidhuber
NeurIPS 2022, 2022
142022
Upside-down reinforcement learning can diverge in stochastic environments with episodic resets
M Štrupl, F Faccio, DR Ashley, J Schmidhuber, RK Srivastava
arXiv preprint arXiv:2205.06595, 2022
112022
Bayesian brains and the Rényi divergence
N Sajid*, F Faccio*, L Da Costa, T Parr, J Schmidhuber, K Friston
Neural Computation 34 (4), 829-855, 2022
112022
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
F Faccio, A Ramesh, V Herrmann, J Harb, J Schmidhuber
Decision Awareness in Reinforcement Learning Workshop at ICML 2022, 2022
102022
Learning to identify critical states for reinforcement learning from videos
H Liu, M Zhuge, B Li, Y Wang, F Faccio, B Ghanem, J Schmidhuber
ICCV 2023, 2023
92023
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute
A Stanić, D Ashley, O Serikov, L Kirsch, F Faccio, J Schmidhuber, ...
arXiv preprint arXiv:2309.11197, 2023
82023
Learning useful representations of recurrent neural network weight matrices
V Herrmann, F Faccio, J Schmidhuber
ICML 2024 (oral), 2024
62024
Reward-Weighted Regression Converges to a Global Optimum
M Štrupl*, F Faccio*, DR Ashley, RK Srivastava, J Schmidhuber
AAAI 2022, 2022
42022
How to correctly do semantic backpropagation on language-based agentic systems
W Wang, HA Alyahya, DR Ashley, O Serikov, D Khizbullin, F Faccio, ...
arXiv preprint arXiv:2412.03624, 2024
22024
Highway reinforcement learning
Y Wang, M Strupl, F Faccio, Q Wu, H Liu, M Grudzień, X Tan, ...
arXiv preprint arXiv:2405.18289, 2024
22024
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
Y Wang, Q Wu, W Li, DR Ashley, F Faccio, C Huang, J Schmidhuber
arXiv preprint arXiv:2406.08404, 2024
12024
Highway Value Iteration Networks
Y Wang, W Li, F Faccio, Q Wu, J Schmidhuber
ICML 2024, 2024
12024
Faster Diffusion via Temporal Attention Decomposition
H Liu, W Zhang, J Xie, F Faccio, M Xu, T Xiang, MZ Shou, JM Perez-Rua, ...
arXiv e-prints, arXiv: 2404.02747, 2024
12024
Upside Down Reinforcement Learning with Policy Generators
J Di Ventura, DR Ashley, V Herrmann, F Faccio, J Schmidhuber
arXiv preprint arXiv:2501.16288, 2025
2025
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
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