In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …

Open-vocabulary models like CLIP achieve high accuracy across many image classification
tasks. However, there are still settings where their zero-shot performance is far from optimal …

We present the Supermasks in Superposition (SupSup) model, capable of sequentially
learning thousands of tasks without catastrophic forgetting. Our approach uses a randomly …

Deep convolutional neural networks are hindered by training instability and feature
redundancy towards further performance improvement. A promising solution is to impose …

This paper discusses a three‐level model that synthesizes and unifies existing learning
theories to model the roles of artificial intelligence (AI) in promoting learning processes. The …

When training a neural network for a desired task, one may prefer to adapt a pre-trained
network rather than starting from randomly initialized weights. Adaptation can be useful in …

This article reviews recent progress in the development of the computing framework vector
symbolic architectures (VSA)(also known as hyperdimensional computing). This framework …

We introduce Continual Learning via Neural Pruning (CLNP), a new method aimed at
lifelong learning in fixed capacity models based on neuronal model sparsification. In this …

Despite rapid advances in continual learning, a large body of research is devoted to
improving performance in the existing setups. While a handful of work do propose new …