Liquid Time-constant Networks

December 2024
Vol-10, Issue-6
Paper ID: 25536
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
INFORMATION SCIENCE AND EGINEERING
Keywords
-
Abstract
Liquid Time-Constant Networks (LTCs) are a new type of time-continuous recurrent neural networks (RNNs) that use linear first-order dynamical systems with nonlinear connected gates. This review study investigates LTCs. Compared to conventional RNNs, LTCs are more stable and expressive in capturing intricate temporal connections since they dynamically modulate their time-constants. We provide an overview of the theoretical underpinnings of LTCs, highlighting their expressive potential in latent trajectory space and their stable and constrained behaviour. Their improved performance on time-series prediction problems is demonstrated empirically. There is also discussion of the ramifications of these developments and possible future paths.

Author Information

# Name Institute / Affiliation
1 Pradeep Nayak ALVAS INSTITUE OF ENGINEERING AND TECHNOLOGY
2 Ankitha B ALVAS INSTITUE OF ENGINEERING AND TECHNOLOGY
3 Bhumika S K ALVAS INSTITUE OF ENGINEERING AND TECHNOLOGY
4 Satheesh D S ALVAS INSTITUE OF ENGINEERING AND TECHNOLOGY
5 Sreejith R ALVAS INSTITUE OF ENGINEERING AND TECHNOLOGY

How to Cite

Use the following formats to cite this article in your research.

APA Style
Nayak, Pradeep, B, Ankitha, K, Bhumika S, S, Satheesh D, & R, Sreejith (2024). Liquid Time-constant Networks. International Journal of Advance Research and Innovative Ideas In Education, 10(6), 2051-2060.
MLA Style
Nayak, Pradeep, et al. "Liquid Time-constant Networks." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 6, 2024, pp. 2051-2060.
IEEE Style
Pradeep Nayak, Ankitha B, Bhumika S K, Satheesh D S, and Sreejith R, "Liquid Time-constant Networks," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 6, pp. 2051-2060, 2024.
Vancouver Style
Nayak Pradeep, B Ankitha, K Bhumika S, S Satheesh D, R Sreejith. Liquid Time-constant Networks. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(6):2051-2060.
Harvard Style
Nayak, Pradeep, B, Ankitha, K, Bhumika S, S, Satheesh D, & R, Sreejith (2024) 'Liquid Time-constant Networks', International Journal of Advance Research and Innovative Ideas In Education, 10(6), pp. 2051-2060.
Chicago Style
Nayak, Pradeep, et al. "Liquid Time-constant Networks." International Journal of Advance Research and Innovative Ideas In Education 10, no. 6 (2024): 2051-2060.
Turabian Style
Nayak, Pradeep, et al. "Liquid Time-constant Networks." International Journal of Advance Research and Innovative Ideas In Education 10, no. 6 (2024): 2051-2060.

Export Citation

Related Research

Pneumonia detection using CNN's algorithm
K.v.v.Ananthsai 2024 Electronic instrumentation on medical devices
PDF Unavailable
Biometric Voting Machine Using Fingerprint Sensor
More Rushikesh Ravsaheb et al. 2024 Instrumentation and Control
PDF Unavailable
OVEN TEMPERATURE CONTROL USING PID WITH SMARTPHONE MONITORING VIA FIREBASE
Mr. Gaware Sudam Laxman et al. 2024 Instrumentation and Control Engineering
PDF Unavailable
Attendance Management using Facial Recognition
ANUSH A et al. 2024 Information Engineering
PDF Unavailable
HYBRID WIND SOLAR POWER GENERATION SYSTEM
GUPTA SHREYA BRIJESHKUMAR et al. 2024 INSTRUMENTATION AND CONTROL ENGINEERING
PDF Unavailable
DESIGN AND FABRICATION OF 3D PRINTER WITH 2 AXIS MOVING BED 4 DEGREE OF FREEDOM
Mohit et al. 2024 Mechanical engineering
PDF Unavailable
MODELLING AND SIMULATION OF A BOX-TYPE SOLAR FURNACE UNDER MADAGASCAR METEOROLOGICAL CONDITIONS
Julien RAJOMALAHY et al. 2023 Thermal engineering
PDF Unavailable
Customized SCADA System to Monitor Water Distribution Network using PowerBI
SathiyaSivam.B et al. 2023 Electrical and Instrumentation
PDF Unavailable
IoT Application for Real-time PLC Monitoring through the MQTT Protocol
Prof. V. A. Ahirrao et al. 2023 instrumentation engineering
PDF Unavailable
Chronic Kidney Disease Classification using Machine Learning Methods
Bhumi P Patel et al. 2023 Applied Instrumentation
PDF Unavailable