REVIEW 4 cited by
Learning More Generalized Experts by Merging Experts in Mixture-of-Experts
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We observe that incorporating a shared layer in a mixture-of-experts can lead to performance degradation. This leads us to hypothesize that learning shared features poses challenges in deep learning, potentially caused by the same feature being learned as various different features. To address this issue, we track each expert's usage frequency and merge the two most frequently selected experts. We then update the least frequently selected expert using the combination of experts. This approach, combined with the subsequent learning of the router's expert selection, allows the model to determine if the most frequently selected experts have learned the same feature differently. If they have, the combined expert can be further trained to learn a more general feature. Consequently, our algorithm enhances transfer learning and mitigates catastrophic forgetting when applied to multi-domain task incremental learning.
Forward citations
Cited by 4 Pith papers
-
Expert Routing with Synthetic Data for Continual Learning
G2D trains a domain discriminator on synthetic data to route test samples to domain-specific experts, outperforming generative replay on several continual learning benchmarks.
-
ResidualDroppath: Enhancing Feature Reuse over Residual Connections
A two-phase training algorithm alternating droppath steps with frozen-path steps gives modest accuracy improvements on small image datasets, with inconsistent ImageNet results.
-
A Survey on Inference Optimization Techniques for Mixture of Experts Models
A structured survey of MoE inference optimization that categorizes existing techniques into model, system, and hardware levels and summarizes reported speedups and memory savings.
-
Mixture of Experts (MoE): A Big Data Perspective
A survey of MoE methods for big data that catalogs architectures, use cases, and open challenges without adding new results.
Discussion (0). Continue with ORCID to comment.