Summary of Clasp: Learning Concepts For Time-series Signals From Natural Language Supervision, by Aoi Ito et al.
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision
by Aoi Ito, Kota Dohi, Yohei Kawaguchi
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a foundation model called “CLaSP” that allows users to search time series signals using natural language queries that describe the characteristics of the signals. The authors address previous limitations in representing time series signal data in natural language by introducing a neural network based on contrastive learning, trained on datasets TRUCE and SUSHI. The proposed method leverages common sense knowledge embedded in a large-scale language model (LLM) and does not require a dictionary of predefined synonyms. Experimental results demonstrate that CLaSP enables accurate natural language search of time series signal data and can learn the points at which signal data changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to find information about signals, like temperature or stock prices, using normal language instead of complex code. They designed a special kind of computer program called “CLaSP” that understands what words mean when talking about signals. The authors made sure this new tool works well by testing it with lots of different signals and descriptions. This will make it easier for people to find the information they need, like finding all the times when a certain temperature was reached. |
Keywords
» Artificial intelligence » Language model » Neural network » Temperature » Time series
