213,356 research outputs found
Liquid Time-constant Networks
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 https://github.com/raminmh/liquid_time_constant_networksComment: Accepted to the Thirty-Fifth AAAI Conference on Artificial
Intelligence (AAAI-21
Closed-form continuous-time neural networks
Continuous-time neural networks are a class of machine learning systems that can tackle representation learning on spatiotemporal decision-making tasks. These models are typically represented by continuous differential equations. However, their expressive power when they are deployed on computers is bottlenecked by numerical differential equation solvers. This limitation has notably slowed down the scaling and understanding of numerous natural physical phenomena such as the dynamics of nervous systems. Ideally, we would circumvent this bottleneck by solving the given dynamical system in closed form. This is known to be intractable in general. Here, we show that it is possible to closely approximate the interaction between neurons and synapses—the building blocks of natural and artificial neural networks—constructed by liquid time-constant networks efficiently in closed form. To this end, we compute a tightly bounded approximation of the solution of an integral appearing in liquid time-constant dynamics that has had no known closed-form solution so far. This closed-form solution impacts the design of continuous-time and continuous-depth neural models. For instance, since time appears explicitly in closed form, the formulation relaxes the need for complex numerical solvers. Consequently, we obtain models that are between one and five orders of magnitude faster in training and inference compared with differential equation-based counterparts. More importantly, in contrast to ordinary differential equation-based continuous networks, closed-form networks can scale remarkably well compared with other deep learning instances. Lastly, as these models are derived from liquid networks, they show good performance in time-series modelling compared with advanced recurrent neural network models
Multilayer Aggregation with Statistical Validation: Application to Investor Networks
Multilayer networks are attracting growing attention in many fields,
including finance. In this paper, we develop a new tractable procedure for
multilayer aggregation based on statistical validation, which we apply to
investor networks. Moreover, we propose two other improvements to their
analysis: transaction bootstrapping and investor categorization. The
aggregation procedure can be used to integrate security-wise and time-wise
information about investor trading networks, but it is not limited to finance.
In fact, it can be used for different applications, such as gene,
transportation, and social networks, were they inferred or observable.
Additionally, in the investor network inference, we use transaction
bootstrapping for better statistical validation. Investor categorization allows
for constant size networks and having more observations for each node, which is
important in the inference especially for less liquid securities. Furthermore,
we observe that the window size used for averaging has a substantial effect on
the number of inferred relationships. We apply this procedure by analyzing a
unique data set of Finnish shareholders during the period 2004-2009. We find
that households in the capital have high centrality in investor networks,
which, under the theory of information channels in investor networks suggests
that they are well-informed investors
Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks
Magnetic navigation (MagNav) is a rising alternative to the Global
Positioning System (GPS) and has proven useful for aircraft navigation.
Traditional aircraft navigation systems, while effective, face limitations in
precision and reliability in certain environments and against attacks. Airborne
MagNav leverages the Earth's magnetic field to provide accurate positional
information. However, external magnetic fields induced by aircraft electronics
and Earth's large-scale magnetic fields disrupt the weaker signal of interest.
We introduce a physics-informed approach using Tolles-Lawson coefficients for
compensation and Liquid Time-Constant Networks (LTCs) to remove complex, noisy
signals derived from the aircraft's magnetic sources. Using real flight data
with magnetometer measurements and aircraft measurements, we observe up to a
64% reduction in aeromagnetic compensation error (RMSE nT), outperforming
conventional models. This significant improvement underscores the potential of
a physics-informed, machine learning approach for extracting clean, reliable,
and accurate magnetic signals for MagNav positional estimation.Comment: Accepted at the NeurIPS 2023 Machine Learning and the Physical
Sciences workshop, 7 pages, 4 figures, see code here:
https://github.com/fnerrise/LNNs_MagNav
LTC-SE: Expanding the Potential of Liquid Time-Constant Neural Networks for Scalable AI and Embedded Systems
We present LTC-SE, an improved version of the Liquid Time-Constant (LTC)
neural network algorithm originally proposed by Hasani et al. in 2021. This
algorithm unifies the Leaky-Integrate-and-Fire (LIF) spiking neural network
model with Continuous-Time Recurrent Neural Networks (CTRNNs), Neural Ordinary
Differential Equations (NODEs), and bespoke Gated Recurrent Units (GRUs). The
enhancements in LTC-SE focus on augmenting flexibility, compatibility, and code
organization, targeting the unique constraints of embedded systems with limited
computational resources and strict performance requirements. The updated code
serves as a consolidated class library compatible with TensorFlow 2.x, offering
comprehensive configuration options for LTCCell, CTRNN, NODE, and CTGRU
classes. We evaluate LTC-SE against its predecessors, showcasing the advantages
of our optimizations in user experience, Keras function compatibility, and code
clarity. These refinements expand the applicability of liquid neural networks
in diverse machine learning tasks, such as robotics, causality analysis, and
time-series prediction, and build on the foundational work of Hasani et al.Comment: 11 pages, 5 figures, 5 tables, This research work is partially drawn
from the MSc thesis of Michael B. Khani. arXiv admin note: text overlap with
arXiv:2006.04439 by other author
Spatially Resolved Monitoring of Drying of Hierarchical Porous Organic Networks
Evaporation kinetics of water confined in hierarchal polymeric porous media is studied by low field nuclear magnetic resonance (NMR). Systems synthesized with various degrees of cross-linker density render networks with similar pore sizes but different response when soaked with water. Polymeric networks with low percentage of cross-linker can undergo swelling, which affects the porosity as well as the drying kinetics. The drying process is monitored macroscopically by single-sided NMR, with spatial resolution of 100 μm, while microscopic information is obtained by measurements of spin?spin relaxation times (T2). Transition from a funicular to a pendular regime, where hydraulic connectivity is lost and the capillary flow cannot compensate for the surface evaporation, can be observed from inspection of the water content in different sample layers. Relaxation measurements indicate that even when the larger pore structures are depleted of water, capillary flow occurs through smaller voids.Fil: Velasco, Manuel Isaac. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Silletta, Emilia Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Gomez, Cesar Gerardo. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; Argentina. Universidad Nacional de Córdoba. Instituto de Investigación y Desarrollo En Ingeniería de Procesos y Química Aplicada. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigación y Desarrollo En Ingeniería de Procesos y Química Aplicada.; ArgentinaFil: Strumia, Miriam Cristina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Stapf, Siegfried. Ilmenau University of Technology; AlemaniaFil: Monti, Gustavo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Mattea, Carlos. Ilmenau University of Technology; AlemaniaFil: Acosta, Rodolfo Hector. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentin
Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations
In this paper, we describe a new neuro-inspired, hardware-friendly readout
stage for the liquid state machine (LSM), a popular model for reservoir
computing. Compared to the parallel perceptron architecture trained by the
p-delta algorithm, which is the state of the art in terms of performance of
readout stages, our readout architecture and learning algorithm can attain
better performance with significantly less synaptic resources making it
attractive for VLSI implementation. Inspired by the nonlinear properties of
dendrites in biological neurons, our readout stage incorporates neurons having
multiple dendrites with a lumped nonlinearity. The number of synaptic
connections on each branch is significantly lower than the total number of
connections from the liquid neurons and the learning algorithm tries to find
the best 'combination' of input connections on each branch to reduce the error.
Hence, the learning involves network rewiring (NRW) of the readout network
similar to structural plasticity observed in its biological counterparts. We
show that compared to a single perceptron using analog weights, this
architecture for the readout can attain, even by using the same number of
binary valued synapses, up to 3.3 times less error for a two-class spike train
classification problem and 2.4 times less error for an input rate approximation
task. Even with 60 times larger synapses, a group of 60 parallel perceptrons
cannot attain the performance of the proposed dendritically enhanced readout.
An additional advantage of this method for hardware implementations is that the
'choice' of connectivity can be easily implemented exploiting address event
representation (AER) protocols commonly used in current neuromorphic systems
where the connection matrix is stored in memory. Also, due to the use of binary
synapses, our proposed method is more robust against statistical variations.Comment: 14 pages, 19 figures, Journa
Chirality transfer and stereo-selectivity of imprinted cholesteric networks
Imprinting of cholesteric textures in a polymer network is a method of
preserving a macroscopically chiral phase in a system with no molecular
chirality. By modifying the elastics properties of the network, the resulting
stored helical twist can be manipulated within a wide range since the
imprinting efficiency depends on the balance between the elastics constants and
twisting power at network formation. One spectacular property of phase
chirality imprinting is the created ability of the network to adsorb
preferentially one stereo-component from a racemic mixture. In this paper we
explore this property of chirality transfer from a macroscopic to the molecular
scale. In particular, we focus on the competition between the phase chirality
and the local nematic order. We demonstrate that it is possible to control the
subsequent release of chiral solvent component from the imprinting network and
the reversibility of the stereo-selective swelling by racemic solvents
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