CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision

• URL: http://arxiv.org/abs/2411.08397v1
• Date: Wed, 13 Nov 2024 07:32:58 GMT
• Title: CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision
• Authors: Aoi Ito, Kota Dohi, Yohei Kawaguchi,
• Abstract summary: "CLaSP" can search time series signals using natural language that describes the characteristics of the signals as queries. "CLaSP" enables natural language search of time series signal data and can accurately learn the points at which signal data changes.
• Score: 3.8506666685467352
• License:
• Abstract: This paper proposes a foundation model called "CLaSP" that can search time series signals using natural language that describes the characteristics of the signals as queries. Previous efforts to represent time series signal data in natural language have had challenges in designing a conventional class of time series signal characteristics, formulating their quantification, and creating a dictionary of synonyms. To overcome these limitations, the proposed method introduces a neural network based on contrastive learning. This network is first trained using the datasets TRUCE and SUSHI, which consist of time series signals and their corresponding natural language descriptions. Previous studies have proposed vocabularies that data analysts use to describe signal characteristics, and SUSHI was designed to cover these terms. We believe that a neural network trained on these datasets will enable data analysts to search using natural language vocabulary. Furthermore, our method does not require a dictionary of predefined synonyms, and it leverages common sense knowledge embedded in a large-scale language model (LLM). Experimental results demonstrate that CLaSP enables natural language search of time series signal data and can accurately learn the points at which signal data changes.

Related papers

• Metadata Matters for Time Series: Informative Forecasting with Transformers [70.38241681764738]
  We propose a Metadata-informed Time Series Transformer (MetaTST) for time series forecasting. To tackle the unstructured nature of metadata, MetaTST formalizes them into natural languages by pre-designed templates. A Transformer encoder is employed to communicate series and metadata tokens, which can extend series representations by metadata information.
  arXiv  Detail & Related papers  (2024-10-04T11:37:55Z)
• Part-of-Speech Tagging of Odia Language Using statistical and Deep Learning-Based Approaches [0.0]
  This research work is to present a conditional random field (CRF) and deep learning-based approaches (CNN and Bi-LSTM) to develop Odia part-of-speech tagger. It has been observed that Bi-LSTM model with character sequence feature and pre-trained word vector achieved a significant state-of-the-art result.
  arXiv  Detail & Related papers  (2022-07-07T12:15:23Z)
• Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition [0.76146285961466]
  This paper proposes a multi-view spatial-temporal continuous sign language recognition network. It is tested on two public sign language datasets SLR-100 and PHOENIX-Weather 2014T (RWTH)
  arXiv  Detail & Related papers  (2022-04-19T08:43:03Z)
• A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation [54.29679610921429]
  Existing sign language datasets contain only about 10K-20K pairs of sign videos, gloss annotations and texts. Data is thus a bottleneck for training effective sign language translation models. This simple baseline surpasses the previous state-of-the-art results on two sign language translation benchmarks.
  arXiv  Detail & Related papers  (2022-03-08T18:59:56Z)
• Time-Incremental Learning from Data Using Temporal Logics [3.167882687550935]
  We propose a method to predict the label of a signal that is received incrementally over time, referred to as prefix signal. We present a novel decision-tree based approach to generate a finite number of Signal Temporal Logic (STL) specifications from the given dataset. The effectiveness and classification performance of our algorithm are evaluated on an urban-driving and a naval-surveillance case studies.
  arXiv  Detail & Related papers  (2021-12-28T21:32:00Z)
• Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble [71.97020373520922]
  Sign language is commonly used by deaf or mute people to communicate. We propose a novel Multi-modal Framework with a Global Ensemble Model (GEM) for isolated Sign Language Recognition ( SLR) Our proposed SAM- SLR-v2 framework is exceedingly effective and achieves state-of-the-art performance with significant margins.
  arXiv  Detail & Related papers  (2021-10-12T16:57:18Z)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• NSL: Hybrid Interpretable Learning From Noisy Raw Data [66.15862011405882]
  This paper introduces a hybrid neural-symbolic learning framework, called NSL, that learns interpretable rules from labelled unstructured data. NSL combines pre-trained neural networks for feature extraction with FastLAS, a state-of-the-art ILP system for rule learning under the answer set semantics. We demonstrate that NSL is able to learn robust rules from MNIST data and achieve comparable or superior accuracy when compared to neural network and random forest baselines.
  arXiv  Detail & Related papers  (2020-12-09T13:02:44Z)
• Exploring Software Naturalness through Neural Language Models [56.1315223210742]
  The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
  arXiv  Detail & Related papers  (2020-06-22T21:56:14Z)
• Towards Relevance and Sequence Modeling in Language Recognition [39.547398348702025]
  We propose a neural network framework utilizing short-sequence information in language recognition. A new model is proposed for incorporating relevance in language recognition, where parts of speech data are weighted more based on their relevance for the language recognition task. Experiments are performed using the language recognition task in NIST LRE 2017 Challenge using clean, noisy and multi-speaker speech data.
  arXiv  Detail & Related papers  (2020-04-02T18:31:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.