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Stochastic Processes

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Is Learning in Biological Neural Networks Based on Stochastic Gradient Descent? An Analysis Using Stochastic Processes.

Neural computation Jun 7, 2024
In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the brain relies only on local infor...
Neural Networks, Computer Supervised Machine Learning Nerve Net Stochastic Processes Deep Learning Models, Neurological
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SEINN: A deep learning algorithm for the stochastic epidemic model.

Mathematical biosciences and engineering : MBE Aug 14, 2023
Stochastic modeling predicts various outcomes from stochasticity in the data, parameters and dynamical system. Stochastic models are deemed more appropriate than deterministic models accounting in terms of essential and practical information about a ...
Nonlinear Dynamics Stochastic Processes Algorithms Deep Learning Neural Networks, Computer
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Stochastic Gradient Descent Introduces an Effective Landscape-Dependent Regularization Favoring Flat Solutions.

Physical review letters Jun 9, 2023
Generalization is one of the most important problems in deep learning, where there exist many low-loss solutions due to overparametrization. Previous empirical studies showed a strong correlation between flatness of the loss landscape at a solution a...
Neural Networks, Computer Stochastic Processes Algorithms
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Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training.

Proceedings of the National Academy of Sciences of the United States of America Oct 26, 2021
In this paper, we introduce the , a nonconvex, yet analytically tractable, optimization program, in a quest to better understand deep neural networks that are trained for a sufficiently long time. As the name suggests, this model is derived by isolat...
Humans Computer Heuristics Neural Networks, Computer Deep Learning Nonlinear Dynamics Databases, Factual Stochastic Processes
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Finite-time stability and optimal control of an impulsive stochastic reaction-diffusion vegetation-water system driven by Lévy process with time-varying delay.

Mathematical biosciences and engineering : MBE Sep 27, 2021
In this paper, a reaction-diffusion vegetation-water system with time-varying delay, impulse and Lévy jump is proposed. The existence and uniqueness of the positive solution are proved. Meanwhile, mainly through the principle of comparison, we obtain...
Neural Networks, Computer Diffusion Stochastic Processes Water Time Factors
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New role for circuit expansion for learning in neural networks.

Physical review. E Feb 1, 2021
Many sensory pathways in the brain include sparsely active populations of neurons downstream from the input stimuli. The biological purpose of this expanded structure is unclear, but it may be beneficial due to the increased expressive power of the n...
Models, Neurological Stochastic Processes Neural Networks, Computer Learning
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Learning dynamical systems in noise using convolutional neural networks.

Chaos (Woodbury, N.Y.) Oct 1, 2020
The problem of distinguishing deterministic chaos from non-chaotic dynamics has been an area of active research in time series analysis. Since noise contamination is unavoidable, it renders deterministic chaotic dynamics corrupted by noise to appear ...
Deep Learning Image Processing, Computer-Assisted Nonlinear Dynamics Stochastic Processes Regression Analysis Reproducibility of Results
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Mantis-ml: Disease-Agnostic Gene Prioritization from High-Throughput Genomic Screens by Stochastic Semi-supervised Learning.

American journal of human genetics May 7, 2020
Access to large-scale genomics datasets has increased the utility of hypothesis-free genome-wide analyses. However, gene signals are often insufficiently powered to reach experiment-wide significance, triggering a process of laborious triaging of gen...
Stochastic Processes Neural Networks, Computer Exome Amyotrophic Lateral Sclerosis Reproducibility of Results Supervised Machine Learning Humans ROC Curve Genetic Association Studies Animals Area Under Curve Mice Renal Insufficiency, Chronic Disease Models, Animal Deep Learning Epilepsy Genomics
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Bayesian framework for simulation of dynamical systems from multidimensional data using recurrent neural network.

Chaos (Woodbury, N.Y.) Dec 1, 2019
We suggest a new method for building data-driven dynamical models from observed multidimensional time series. The method is based on a recurrent neural network with specific structure, which allows for the joint reconstruction of both a low-dimension...
Stochastic Processes Atmosphere Bayes Theorem Signal Processing, Computer-Assisted Probability Computer Simulation Models, Theoretical Neural Networks, Computer Data Analysis Algorithms
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Machine-Learning and Stochastic Tumor Growth Models for Predicting Outcomes in Patients With Advanced Non-Small-Cell Lung Cancer.

JCO clinical cancer informatics Sep 1, 2019
PURPOSE: The prediction of clinical outcomes for patients with cancer is central to precision medicine and the design of clinical trials. We developed and validated machine-learning models for three important clinical end points in patients with adva...
Tumor Burden Neoplasm Metastasis Molecular Targeted Therapy Models, Biological Combined Modality Therapy Lung Neoplasms Algorithms Kaplan-Meier Estimate Treatment Outcome Prognosis Humans Stochastic Processes Carcinoma, Non-Small-Cell Lung Randomized Controlled Trials as Topic Machine Learning Neoplasm Staging
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