53、Learning to Decode:Reinforcement Learningfor Decoding of Sparse Graph-Based Channel Codes Salman Habib (New Jersey Institute of Tech) · Allison Beemer (New Jersey Institute of Technology) · Joerg Kliewer (New Jersey Institute of Technology) 54、BAIL: Best-ActionImitation Learningfor Batch Deep...
Logical Optimal Actions (LOA) is an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games. This repository has an implementation of LOA ex...
A curated list of papers on Neural Symbolic and Probabilistic Logic. Papers are sorted by their uploaded dates in descending order. Each paper is with a descrip...
Sun, R.: Supplementing neural reinforcement learning with symbolic methods. In: Sun, R., Wermter, S. (eds.) Hybrid Neural Systems 1998. LNCS, vol. 1778, pp. 333–347. Springer, Heidelberg (2000)Sun, R. 2000. Supplementing neural reinforcement learn- ing with symbolic methods. In Wer...
论文(Zaremba & Sutskever, 2015) 设计了一些任务: ✅ 符号推理(Symbolic Reasoning):NTM 需要学会推理数学公式。✅ 强化学习导航(RL-based Navigation):NTM 作为智能体在网格世界中学习最优路径。✅ 程序执行(Learning to Execute):NTM 读取 Python 代码并预测输出。
Anderson G, Verma A, Dillig I, Chaudhuri S (2020) Neurosymbolic reinforcement learning with formally verified exploration. Adv Neural Inf Process Syst 33:6172–6183 Google Scholar Gaur M, Kursuncu U, Sheth A, Wickramarachchi R, Yadav S (2020) Knowledge-infused deep learning. In: Proceedings...
machine-learningontologiesneural-symbolicneuro-symbolicneurosymbolic UpdatedJan 27, 2025 Python https://arxiv.org/abs/2312.10807 reinforcement-learningimitation-learningrobot-manipulationneural-symbolicfoundation-modelsvisual-language-modelslanguage-conditioned-learninglarge-languge-models ...
The best solvers today rely on complex hand-crafted rules, without using machine learning at all. We revisit whether recent advances in neural networks allow progress on this task, or whether an entirely different class of models are required. First, we adapt the DreamCoder neurosymbolic reasoning...
● 神经符号 Neural-Symbolic hinton * [Neural-Symbolic Learning Systems (豆瓣)](http://t.cn/A62p3Nt0) * [Neural-Symbolic Cognitive Reasoning (豆瓣)](http://t.cn/A62p3Ntp) * [IJCLR 2020: Internation...
4 Relevance to neuroscience: Reinforcement learning and spike-timing-dependent plasticity (STDP) 我们的具有指数逃逸噪声的概率IF神经元算法所暗示的可塑性规则具有 z 的增强对突触前和突触后脉冲之间的时间间隔的指数依赖性,以及 z 的非关联抑制依赖于突触前脉冲,如第2节所示。如果强化 r 为负,则 z 的增强决...