To realize this possibility, we observe that a neural network classifier with N parameters can be interpreted as a weighted ensemble of N classifiers, and that in the lazy regime limit these classifiers are fixed throughout learning. We term these classifiers the neural tangent experts and show ...
This paper presents a novel approach using a Reinforcement Learning (RL) agent with Continual Learning (CL) capabilities to navigate a visual robotic structure, achieving advanced proficiency in Tic-Tac-Toe. The system integrates a webcam for environmental perception, specialized neural blocks for ...
Continual learning Lifelong learning Catastrophic forgetting Developmental systems Memory consolidation 1. Introduction Computational systems operating in the real world are exposed to continuous streams of information and thus are required to learn and remember multiple tasks from dynamic data distributions. Fo...
Continual learning techniques could enable models to acquire specialized solutions without forgetting previous ones, potentially learning over a lifetime, as a human does. In fact, continual learning is generally considered one of the attributes necessary for human-level artificial general intelligence [1...
of previously seen data, a problem that neural networks are well known to suffer from. The work described in this thesis has been dedicated to the investigation of continual learning and solutions to mitigate the forgetting phenomena in neural networks. To approach the continual learning problem, ...
Continual Learning for Neural Semantic Parsing2020 语义解析:query->slot-value tree presentation。incremental learning 在这里被看作是 continually learning 的一个分支,incremental class 分布会有很大差异。采用三种方法: episode memory,抽样方式为按比例静态抽样和在每个轮次动态抽样/正则/freeze。 解决的问题是 ...
Deep neural networks are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but change ac
Lifelong learning has recently attracted attention in building machine learning systems that continually accumulate and transfer knowledge to help future learning. Unsupervised topic modeling has been popularly used to discover topics from document collections. However, the application of topic modeling is ch...
Code for paperContinual Learning of Context-dependent Processing in Neural Networks. You can also get the free version fromhttps://rdcu.be/bOaa3 There is a new version based on TF2https://github.com/xuejianyong/OWM-tf2provided by Dr. Jianyong Xue, and it basically reproduces our work. Than...
CH-HNN算法通过模拟大脑皮层-海马回路的双重表征机制,利用人工神经网络(ANN)提取泛化规律并生成调制信号,指导脉冲神经网络(SNN)学习特定记忆,同时引入元可塑性机制调节学习速率,从而实现高效的持续学习和知识整合。 2 创新点 受大脑皮层-海马回路启发的混合神经网络架构: 提出了一种基于皮层-海马回路(corticohippocampal ...