Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
June 13, 2024Open Access

Separations in the Representational Capabilities of Transformers and Recurrent Architectures

View Full Paper
Ask AI
Bookmark
Share

Authors

SBSatwik BhattamishraMHMichael G. HahnPBPhil Blunsom

Discussion

Loading...

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Cite This Study

Bhattamishra et al. (2024) studied this question.

synapsesocial.com/papers/68e64f88b6db6435875e018bhttps://doi.org/10.48550/arxiv.2406.09347
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval2024 · 1 citations
  2. 2Does Transformer Interpretability Transfer to RNNs?2024 · 2 citations
  3. 3Revenge of the Fallen? Recurrent Models Match Transformers at Predicting Human Language Comprehension Metrics2024 · 2 citations
  4. 4On the Design Space Between Transformers and Recursive Neural Nets2024
  5. 5On the Resurgence of Recurrent Models for Long Sequences -- Survey and Research Opportunities in the Transformer Era2024 · 1 citations