Scaling laws for nonlinear dynamical models of articulatory control
- URL: http://arxiv.org/abs/2411.12720v2
- Date: Mon, 16 Dec 2024 18:24:53 GMT
- Title: Scaling laws for nonlinear dynamical models of articulatory control
- Authors: Sam Kirkham,
- Abstract summary: We show how the addition of a nonlinear restoring force to task dynamic models introduces challenges with parameterization and interpretability.<n>We apply the scaling laws to a cubic model and show how they facilitate interpretable simulations of articulatory dynamics.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Dynamical theories of speech use computational models of articulatory control to generate quantitative predictions and advance understanding of speech dynamics. The addition of a nonlinear restoring force to task dynamic models is a significant improvement over linear models, but nonlinearity introduces challenges with parameterization and interpretability. We illustrate these problems through numerical simulations and introduce solutions in the form of scaling laws. We apply the scaling laws to a cubic model and show how they facilitate interpretable simulations of articulatory dynamics, and can be theoretically interpreted as imposing physical and cognitive constraints on models of speech movement dynamics.
Related papers
- Discovering dynamical laws for speech gestures [0.0]
We discover models in the form of symbolic equations that govern articulatory gestures during speech.
A sparse symbolic regression algorithm is used to discover models from kinematic data on the tongue and lips.
arXiv Detail & Related papers (2025-04-07T09:03:32Z) - No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs [56.78271181959529]
This paper proposes a conceptual shift to modeling low-dimensional dynamical systems by departing from the traditional two-step modeling process.
Instead of first discovering a closed-form equation and then analyzing it, our approach, direct semantic modeling, predicts the semantic representation of the dynamical system.
Our approach not only simplifies the modeling pipeline but also enhances the transparency and flexibility of the resulting models.
arXiv Detail & Related papers (2025-01-30T18:36:48Z) - Hybrid Adaptive Modeling using Neural Networks Trained with Nonlinear Dynamics Based Features [5.652228574188242]
This paper introduces a novel approach that departs from standard techniques by uncovering information from nonlinear dynamical modeling and embedding it in data-based models.
By explicitly incorporating nonlinear dynamic phenomena through perturbation methods, the predictive capabilities are more realistic and insightful compared to knowledge obtained from brute-force numerical simulations.
arXiv Detail & Related papers (2025-01-21T02:38:28Z) - Modeling Latent Non-Linear Dynamical System over Time Series [7.534744211716623]
We study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data.
We introduce a latent state to allow time-dependent modeling and formulate this problem as a dynamics estimation problem in latent states.
arXiv Detail & Related papers (2024-12-11T05:45:30Z) - Projected Neural Differential Equations for Learning Constrained Dynamics [3.570367665112327]
We introduce a new method for constraining neural differential equations based on projection of the learned vector field to the tangent space of the constraint manifold.
PNDEs outperform existing methods while requiring fewer hyper parameters.
The proposed approach demonstrates significant potential for enhancing the modeling of constrained dynamical systems.
arXiv Detail & Related papers (2024-10-31T06:32:43Z) - Latent Space Energy-based Neural ODEs [73.01344439786524]
This paper introduces a novel family of deep dynamical models designed to represent continuous-time sequence data.
We train the model using maximum likelihood estimation with Markov chain Monte Carlo.
Experiments on oscillating systems, videos and real-world state sequences (MuJoCo) illustrate that ODEs with the learnable energy-based prior outperform existing counterparts.
arXiv Detail & Related papers (2024-09-05T18:14:22Z) - Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics [5.841659874892801]
Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals.
Existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity.
We propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise.
arXiv Detail & Related papers (2024-08-29T18:58:39Z) - Training Dynamics of Nonlinear Contrastive Learning Model in the High Dimensional Limit [1.7597525104451157]
An empirical distribution of the model weights converges to a deterministic measure governed by a McKean-Vlasov nonlinear partial differential equation (PDE)
Under L2 regularization, this PDE reduces to a closed set of low-dimensional ordinary differential equations (ODEs)
We analyze the fixed point locations and their stability of the ODEs unveiling several interesting findings.
arXiv Detail & Related papers (2024-06-11T03:07:41Z) - Observational Scaling Laws and the Predictability of Language Model Performance [51.2336010244645]
We propose an observational approach that bypasses model training and instead builds scaling laws from 100 publically available models.
We show that several emergent phenomena follow a smooth, sigmoidal behavior and are predictable from small models.
We show how to predict the impact of post-training interventions like Chain-of-Thought and Self-Consistency as language model capabilities continue to improve.
arXiv Detail & Related papers (2024-05-17T17:49:44Z) - Discovering Interpretable Physical Models using Symbolic Regression and
Discrete Exterior Calculus [55.2480439325792]
We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models.
DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems.
We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
arXiv Detail & Related papers (2023-10-10T13:23:05Z) - A Solvable Model of Neural Scaling Laws [72.8349503901712]
Large language models with a huge number of parameters, when trained on near internet-sized number of tokens, have been empirically shown to obey neural scaling laws.
We propose a statistical model -- a joint generative data model and random feature model -- that captures this neural scaling phenomenology.
Key findings are the manner in which the power laws that occur in the statistics of natural datasets are extended by nonlinear random feature maps.
arXiv Detail & Related papers (2022-10-30T15:13:18Z) - Capturing Actionable Dynamics with Structured Latent Ordinary
Differential Equations [68.62843292346813]
We propose a structured latent ODE model that captures system input variations within its latent representation.
Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
arXiv Detail & Related papers (2022-02-25T20:00:56Z) - A Priori Denoising Strategies for Sparse Identification of Nonlinear
Dynamical Systems: A Comparative Study [68.8204255655161]
We investigate and compare the performance of several local and global smoothing techniques to a priori denoise the state measurements.
We show that, in general, global methods, which use the entire measurement data set, outperform local methods, which employ a neighboring data subset around a local point.
arXiv Detail & Related papers (2022-01-29T23:31:25Z) - Time varying regression with hidden linear dynamics [74.9914602730208]
We revisit a model for time-varying linear regression that assumes the unknown parameters evolve according to a linear dynamical system.
Counterintuitively, we show that when the underlying dynamics are stable the parameters of this model can be estimated from data by combining just two ordinary least squares estimates.
arXiv Detail & Related papers (2021-12-29T23:37:06Z) - Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown.
We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
arXiv Detail & Related papers (2021-11-02T12:33:42Z) - Constructing Neural Network-Based Models for Simulating Dynamical
Systems [59.0861954179401]
Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system.
This paper provides a survey of the different ways to construct models of dynamical systems using neural networks.
In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
arXiv Detail & Related papers (2021-11-02T10:51:42Z) - Physics-informed regularization and structure preservation for learning
stable reduced models from data with operator inference [0.0]
Operator inference learns low-dimensional dynamical-system models with nonlinear terms from trajectories of high-dimensional physical systems.
A regularizer for operator inference that induces a stability bias onto quadratic models is proposed.
A formulation of operator inference is proposed that enforces model constraints for preserving structure.
arXiv Detail & Related papers (2021-07-06T13:15:54Z) - Hessian Eigenspectra of More Realistic Nonlinear Models [73.31363313577941]
We make a emphprecise characterization of the Hessian eigenspectra for a broad family of nonlinear models.
Our analysis takes a step forward to identify the origin of many striking features observed in more complex machine learning models.
arXiv Detail & Related papers (2021-03-02T06:59:52Z) - Coarse-Grained Nonlinear System Identification [0.0]
We introduce Coarse-Grained Dynamics, an efficient and universal parameterization of nonlinear system dynamics based on the Volterra series expansion.
We demonstrate the properties of this approach on a simple synthetic problem.
We also demonstrate this approach experimentally, showing that it identifies an accurate model of the nonlinear voltage to dynamics of a tungsten filament with less than a second of experimental data.
arXiv Detail & Related papers (2020-10-14T06:45:51Z) - Sparse Identification of Nonlinear Dynamical Systems via Reweighted
$\ell_1$-regularized Least Squares [62.997667081978825]
This work proposes an iterative sparse-regularized regression method to recover governing equations of nonlinear systems from noisy state measurements.
The aim of this work is to improve the accuracy and robustness of the method in the presence of state measurement noise.
arXiv Detail & Related papers (2020-05-27T08:30:15Z) - Operator inference for non-intrusive model reduction of systems with
non-polynomial nonlinear terms [6.806310449963198]
This work presents a non-intrusive model reduction method to learn low-dimensional models of dynamical systems with non-polynomial nonlinear terms that are spatially local.
The proposed approach requires only the non-polynomial terms in analytic form and learns the rest of the dynamics from snapshots computed with a potentially black-box full-model solver.
The proposed method is demonstrated on three problems governed by partial differential equations, namely the diffusion-reaction Chafee-Infante model, a tubular reactor model for reactive flows, and a batch-chromatography model that describes a chemical separation process.
arXiv Detail & Related papers (2020-02-22T16:27:05Z)
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.