Transfer between long-term and short-term memory using Conceptors

Using Multi-task and Transfer Learning to Solve Working Memory Tasks

We propose a new architecture called Memory-Augmented Encoder-Solver (MAES) that enables transfer learning to solve complex working memory tasks adapted from cognitive psychology. It uses dual recurrent neural network controllers, inside the encoder and solver, respectively, that interface with a shared memory module and is completely differentiable. We study different types of encoders in a systematic manner and demonstrate a unique advantage of multi-task learning in obtaining the best possible encoder. We show by extensive experimentation that the trained MAES models achieve task-size gener…

Overcome Anterograde Forgetting with Cycled Memory Networks

Learning from a sequence of tasks for a lifetime is essential for an agent towards artificial general intelligence. This requires the agent to continuously learn and memorize new knowledge without interference. This paper first demonstrates a fundamental issue of lifelong learning using neural networks, named anterograde forgetting, i.e., preserving and transferring memory may inhibit the learning of new knowledge. This is attributed to the fact that the learning capacity of a neural network will be reduced as it keeps memorizing historical knowledge, and the fact that conceptual confusion may…

EngramNCA：一种记忆传递的神经细胞自动机模型

EngramNCA: a Neural Cellular Automaton Model of Memory Transfer

本研究针对记忆存储的细胞内机制启发，提出EngramNCA模型，通过整合可见状态与私有记忆通道的两个NCA组件，实现复杂形态的编码传播与分层共存形态涌现，为人工系统记忆存储传递提供新架构与见解。

持久结构记忆在神经架构中的坐标系统问题

The Coordinate System Problem in Persistent Structural Memory for Neural Architectures

本文提出DPPN架构，通过持久信息素场发现持久结构记忆的两个独立要求：坐标稳定性与优雅转移机制。