ServiceNow Research

Continual Learning

Continual Learning with self-selecting specialized modules through expansion and pruning
Continual learning (CL) aims to design algorithms that can learn from non-stationarystreams of stationary tasks without forgetting. …
Continual Learning with Foundation Models: An Empirical Study of Latent Replay
Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a …
Continual Learning with Foundation Models: An Empirical Study of Latent Replay
Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a …
Continual Learning via Local Module Composition
Modularity is a compelling solution to continual learning (CL), the problem of modeling sequences of related tasks. Learning and then …
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two …
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing …
Online Learned Continual Compression with Adaptive Quantization Modules
We introduce and study the problem of Online Continual Compression, where one attempts to simultaneously learn to compress and store a …
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two …