Zobrazeno 1 - 10
of 26 227
pro vyhledávání: '"A, Abate"'
Autor:
Pan, Yi, Jiang, Hanqi, Chen, Junhao, Li, Yiwei, Zhao, Huaqin, Zhou, Yifan, Shu, Peng, Wu, Zihao, Liu, Zhengliang, Zhu, Dajiang, Li, Xiang, Abate, Yohannes, Liu, Tianming
Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial intelligence, predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware. Significant advancements have been mad
Externí odkaz:
http://arxiv.org/abs/2410.09674
A crucial capability of Machine Learning models in real-world applications is the ability to continuously learn new tasks. This adaptability allows them to respond to potentially inevitable shifts in the data-generating distribution over time. Howeve
Externí odkaz:
http://arxiv.org/abs/2410.07812
Autor:
Mathiesen, Frederik Baymler, Romao, Licio, Calvert, Simeon C., Laurenti, Luca, Abate, Alessandro
In this paper, we present a novel data-driven approach to quantify safety for non-linear, discrete-time stochastic systems with unknown noise distribution. We define safety as the probability that the system remains in a given region of the state spa
Externí odkaz:
http://arxiv.org/abs/2410.06662
Linear temporal logic (LTL) has recently been adopted as a powerful formalism for specifying complex, temporally extended tasks in reinforcement learning (RL). However, learning policies that efficiently satisfy arbitrary specifications not observed
Externí odkaz:
http://arxiv.org/abs/2410.04631
Autor:
Abate, Nicolás, Torroba, Gonzalo
Information-theoretic methods have led to significant advances in nonperturbative quantum field theory in flat space. In this work, we show that these ideas can be generalized to field theories in a fixed de Sitter space. Focusing on 1+1-dimensional
Externí odkaz:
http://arxiv.org/abs/2409.18186
To evaluate the safety and usefulness of deployment protocols for untrusted AIs, AI Control uses a red-teaming exercise played between a protocol designer and an adversary. This paper introduces AI-Control Games, a formal decision-making model of the
Externí odkaz:
http://arxiv.org/abs/2409.07985
Autor:
Abate, Carlo, Bianchi, Filippo Maria
We propose a novel approach to compute the MAXCUT in attributed graphs, i.e., graphs with features associated with nodes and edges. Our approach is robust to the underlying graph topology and is fully differentiable, making it possible to find soluti
Externí odkaz:
http://arxiv.org/abs/2409.05100
Autor:
Voria, Gianmario, Sellitto, Giulia, Ferrara, Carmine, Abate, Francesco, De Lucia, Andrea, Ferrucci, Filomena, Catolino, Gemma, Palomba, Fabio
Machine learning's widespread adoption in decision-making processes raises concerns about fairness, particularly regarding the treatment of sensitive features and potential discrimination against minorities. The software engineering community has res
Externí odkaz:
http://arxiv.org/abs/2408.16683
Autor:
Benjamin, Patrick, Abate, Alessandro
Recent works have provided algorithms by which decentralised agents, which may be connected via a communication network, can learn equilibria in Mean-Field Games from a single, non-episodic run of the empirical system. However, these algorithms are g
Externí odkaz:
http://arxiv.org/abs/2408.11607
We present a data-driven approach for producing policies that are provably robust across unknown stochastic environments. Existing approaches can learn models of a single environment as an interval Markov decision processes (IMDP) and produce a robus
Externí odkaz:
http://arxiv.org/abs/2408.03093