Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Capobianco Roberto"'
Non-markovian Reinforcement Learning (RL) tasks are very hard to solve, because agents must consider the entire history of state-action pairs to act rationally in the environment. Most works use symbolic formalisms (as Linear Temporal Logic or automa
Externí odkaz:
http://arxiv.org/abs/2408.08677
Autor:
Umili, Elena, Capobianco, Roberto
In this work, we introduce DeepDFA, a novel approach to identifying Deterministic Finite Automata (DFAs) from traces, harnessing a differentiable yet discrete model. Inspired by both the probabilistic relaxation of DFAs and Recurrent Neural Networks
Externí odkaz:
http://arxiv.org/abs/2408.08622
Compositional Explanations is a method for identifying logical formulas of concepts that approximate the neurons' behavior. However, these explanations are linked to the small spectrum of neuron activations (i.e., the highest ones) used to check the
Externí odkaz:
http://arxiv.org/abs/2310.18443
Since the introduction of artificial intelligence in medicinal chemistry, the necessity has emerged to analyse how molecular property variation is modulated by either single atoms or chemical groups. In this paper, we propose to train graph-to-graph
Externí odkaz:
http://arxiv.org/abs/2202.05704
It is well known that Drug Design is often a costly process both in terms of time and economic effort. While good Quantitative Structure-Activity Relationship models (QSAR) can help predicting molecular properties without the need to synthesize them,
Externí odkaz:
http://arxiv.org/abs/2202.05703
The recent success of deep learning models in solving complex problems and in different domains has increased interest in understanding what they learn. Therefore, different approaches have been employed to explain these models, one of which uses hum
Externí odkaz:
http://arxiv.org/abs/2109.07804
Due to their black-box and data-hungry nature, deep learning techniques are not yet widely adopted for real-world applications in critical domains, like healthcare and justice. This paper presents Memory Wrap, a plug-and-play extension to any image c
Externí odkaz:
http://arxiv.org/abs/2106.01440
Autor:
Kompella, Varun, Capobianco, Roberto, Jong, Stacy, Browne, Jonathan, Fox, Spencer, Meyers, Lauren, Wurman, Peter, Stone, Peter
The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting public health, while keeping the economy running to the greatest exte
Externí odkaz:
http://arxiv.org/abs/2010.10560
Research on reinforcement learning has demonstrated promising results in manifold applications and domains. Still, efficiently learning effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state di
Externí odkaz:
http://arxiv.org/abs/1803.08501
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state di
Externí odkaz:
http://arxiv.org/abs/1803.00297