Neuro-Symbolic Reinforcement Learning with First-Order Logic 带有一阶逻辑的神经符号强化学习 emnlp 2021 论文地址:aclanthology.org/2021.e Abstract 深度强化学习(RL)方法在收敛之前往往需要多次试验,并且没有提供训练后的策略的直接可解释性。为了能在RL中实现快速收敛并且提供策略的可解释性,本文提出了一种基于文...
Marra G, Giannini F, Diligenti M, Gori M (2019) Lyrics: a general interface layer to integrate logic inference and deep learning. In: Joint European conference on machine learning and knowledge discovery in databases, Springer, pp 283–298 McCarthy J (1988) Epistemological challenges for connec...
Taking inspiration from Embed2Sym, this paper introduces a novel method for scalable neuro-symbolic learning of first-order logic programs from raw data. The learned clusters are optimally labelled using sampled predictions of a pre-trained vision-language model. A SOTA symbolic learner, robust to ...
This potential is given by a multi-layer perceptron trained on a learning database. A base of 26 brains manually labelled by a neuroanatomist is used ... D Rivière,Jean-Franois Mangin,D Papadopoulos-Orfanos,... - Medical Image Computing & Computer-assisted Intervention-miccai, Third Internatio...
symbolic AI, which encompasses logic-based and knowledge-based systems. This synergy is designed to capitalize on the strengths of each approach to overcome their respective weaknesses, leading to AI systems that can both reason with human-like logic and adapt to new situations through learning. ...
Most machine learning techniques employ various forms of statistical processing. In neural networks, the statistical processing is widely distributed across numerous neurons and interconnections, which increases the effectiveness of correlating and distilling subtle patterns in large data sets. On the other ...
In short, symbolic AI is rule-driven and best suited for structured, logical tasks, while machine learning works best in areas where data is messy. What Are the Advantages of Neuro-Symbolic AI? In a nutshell, neuro-symbolic AI is paving the way for smarter, more reliable AI systems that ...
An efficient Python toolkit for Abductive Learning (ABL), a novel paradigm that integrates machine learning and logical reasoning in a unified framework. pythonmachine-learningsklearnpytorchneuro-symbolic-learningneuro-symbolicabductive-learning UpdatedMar 28, 2024 ...
M.: SUSTAIN: a network model of category learning. Psychol. Rev. 111(2), 309–332 (2004). https://doi.org/10.1037/0033-295x.111.2.309 Grossberg, S.: Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world. Neural Networks Off. J. ...
成立时间:December 20, 2016 研究组 Deep Learning Group The Deep Learning group advances the state-of-the-art in deep learning to achieve general intelligence. We develop algorithms, models, and systems in deep supervised and unsupervised learning, deep reinforcement learning, and ne...