Latent chain-of-thought via recurrent feedback tokens from compressed hidden states improves transformer performance on time-series forecasting and tabular prediction across 36 datasets.
Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.LG 3roles
method 1polarities
use method 1representative citing papers
SGNNs pretrain neural networks on synthetic corpora from multiple mechanistic models and noise levels to enable robust forecasting and back-to-simulation attribution across epidemiology, ecology, and other fields.
Teger is a backbone-agnostic structured uncertainty module that uses discrete Forman curvature for spatial graph rewiring inside a low-rank-plus-diagonal covariance head to mitigate over-squashing and improve residual error propagation in spatio-temporal forecasting.
citing papers explorer
-
Latent Chain-of-Thought Improves Structured-Data Transformers
Latent chain-of-thought via recurrent feedback tokens from compressed hidden states improves transformer performance on time-series forecasting and tabular prediction across 36 datasets.