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Liquid_time_constant_networks Alternatives
Similar projects and alternatives to liquid_time_constant_networks based on common topics and language
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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liquid_time_constant_networks reviews and mentions
Posts with mentions or reviews of liquid_time_constant_networks.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-10.
- Liquid AI, a new MIT spinoff, wants to build an new type of AI
- Code Repository for Liquid Time-Constant Networks (LTCs)
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LNNs - Liquid Neural Networks: Seeking general advice, papers, implementations
Here’s the paper for anyone that wants to save some googling. Liquid Time-constant Networks https://arxiv.org/abs/2006.04439
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MIT solved a century-old differential equation to break 'liquid' AI's computational bottleneck
Liquid neural networks paper
- Neural networks know what they're doing
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Could someone explain to me how Ramin Hasani derived the Fused Euler method?
Pics are from Liquid Time-Constant Networks
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[R]Liquid Time-constant Networks: “..Instead of declaring a learning system’s dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates..”
Abstract: We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems modulated via nonlinear interlinked gates. The resulting models represent dynamical systems with varying (i.e., liquid) time-constants coupled to their hidden state, with outputs being computed by numerical differential equation solvers. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations, and give rise to improved performance on time-series prediction tasks. To demonstrate these properties, we first take a theoretical approach to find bounds over their dynamics and compute their expressive power by the trajectory length measure in latent trajectory space. We then conduct a series of time-series prediction experiments to manifest the approximation capability of Liquid Time-Constant Networks (LTCs) compared to classical and modern RNNs. Code and data are available at this https URL
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www.saashub.com | 3 May 2024
Stats
Basic liquid_time_constant_networks repo stats
7
1,258
3.0
9 months ago
raminmh/liquid_time_constant_networks is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of liquid_time_constant_networks is Python.
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