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Found 8 papers, 3 papers with code
A Deep Learning Approach for Characterizing Major Galaxy Mergers
Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation.
Gated Linear Networks
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs).
Progress & Compress: A scalable framework for continual learning
This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task.
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and scope of their sensors.
Online Learning with Gated Linear Networks
This paper describes a family of probabilistic architectures designed for online learning under the logarithmic loss.
Overcoming catastrophic forgetting in neural networks
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence.
Ranked #3 on
Continual Learning
on F-CelebA (10 tasks)
Optimal prior-dependent neural population codes under shared input noise
The brain uses population codes to form distributed, noise-tolerant representations of sensory and motor variables.
Demixing odors - fast inference in olfaction
Thus, at the behavioral level, the two algorithms make very similar predictions.
