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...
The goal of neural-symbolic computation is to integrate the connectionist and symbolist paradigms. Prior methods learn the neural-symbolic models using reinforcement learning (RL) approaches, which ignore the error propagation in the symbolic reasoning module and thus converge slowly with sparse rewards...
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...
reinforcement-learningimitation-learningrobot-manipulationneural-symbolicfoundation-modelsvisual-language-modelslanguage-conditioned-learninglarge-languge-models UpdatedJun 5, 2024 Symbolic DNN-Tuner is a system to drive the training of a Deep Neural Network, analysing the performance of each training experiment...
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...
We propose a neural network method for the generation of symbolic expressions using reinforcement learning. According to the proposed method, a human decides on the kind and number of primitive functions which, with the appropriate composition (in the mathematical sense), can represent a mapping betw...
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...
In our proposed neural symbolic framework, a general substructure called NSKE is devised to implement neural symbolic reasoning for knowledge extraction, including three modules: (1) feature representation, (2) rule learning, and (3) correlation inference. Fig. 2 shows the key concepts of the thre...