Time series task extraction from large language models
Recent advancements in large language models (LLMs) have shown tremendous potential to revolutionize time series classification. These models possess newly improved capabilities, including impressive zero-shot learning and remarkable reasoning skills, without requiring any additional training...
Saved in:
Main Author: | Toh, Leong Seng |
---|---|
Other Authors: | Thomas Peyrin |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/180995 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evaluating TT-net capability on time series forecasting tasks
by: Nguyen, Tung Bach
Published: (2025) -
Contextual human object interaction understanding from pre-trained large language model
by: Gao ,Jianjun, et al.
Published: (2025) -
A comparison of global rule induction and HMM approaches on extracting story boundaries in news video
by: Chaisorn, L., et al.
Published: (2013) -
Large language model is not a good few-shot information extractor, but a good reranker for hard samples!
by: MA, Yubo, et al.
Published: (2023) -
A hierarchical multi-modal approach to story segmentation in news video
by: LEKHA CHAISORN
Published: (2010)