Unified approach for neural networks with multi-compartmental neurons and non-Hebbian plasticity.
problem Limited computational power of existing neural network models for multi-compartmental neurons and non-Hebbian plasticity.
method Unified extension of similarity matching approach to derive neural networks with multi-compartmental neurons and local, non-Hebbian learning rules.
result Unified approach facilitates understanding of multi-compartmental neuronal structures and non-Hebbian plasticity.
Efficiently calibrates epidemiological models using Bayesian optimization.
problem Calibration of complex epidemiological models is computationally expensive and challenging.
method Graybox Bayesian optimization scheme leveraging Gaussian processes and functional structure of compartmental models.
result Proposed methods achieve efficient calibration and improved performance compared to existing schemes.
Modified PINN approach for analyzing COVID-19 data with incomplete information.
problem Analyzing incomplete data on COVID-19's U.S. development.
method Variation of Physics Informed Neural Networks (PINN) with modified loss function.
result Neural network can perform well even with incomplete information.
Study nationwide measures' impact on COVID-19 using models and machine learning.
problem Analyzing the impact of nationwide COVID-19 measures.
method Compartmental model and machine learning tools.
result Comparison of deterministic model and machine learning forecasts.
New model outperforms traditional disease models in forecasting COVID-19.
problem Forecasting COVID-19 spread with high accuracy and reliability.
method Developed a novel neural forecasting model called ACTS using inter-series attention.
result ACTS outperforms leading forecasters in multiple metrics.
Study uses PPLs to model and forecast COVID-19 spread and policy interventions.
problem Forecast and control the spread of COVID-19 using policy interventions.
method Compartmental models, probabilistic programming, inference, greedy algorithm.
result Optimal series of policy interventions can control the infected population.
Model predicts COVID-19 progression with interpretability.
problem Accurate and credible forecasting of COVID-19 progression.
method Integrates machine learning into disease modeling, uses interpretable encoders.
result More accurate forecasts than state-of-the-art alternatives.
Paper introduces a new method to model epidemic dynamics with varying parameters.
problem Capturing discontinuous variations in epidemic model parameters.
method Total variation regularization with Iterated Nelder--Mead optimization.
result The method accurately models epidemic dynamics with instant changes.
New distribution simplifies covariance matrix inference.
problem Efficient inference for covariance matrices in large models.
method Incorporates Inverse G-Wishart distribution for variational message passing.
result Elegant and succinct expression of variational message passing fragments.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
problem Impact of health care capacity on COVID-19 growth in Senegal.
method Compartmental model with logistic growth health care capacity, machine learning projection.
result Condition to avoid overwhelming health care system provided.
Generative network integrates into ROM for PDEs, matching measurements and estimating uncertainties.
problem Predicting and quantifying uncertainties in numerical simulations of PDEs.
method Generative network (GN) integrated into a reduced-order model (ROM) framework for inverse problems.
result GN-based ROM efficiently quantifies uncertainty and matches measurements with high accuracy.
New method for epidemic model inference using multinomial approximations.
problem Inference in stochastic epidemic models with partial observations.
method Recursive multinomial approximations to integrate over unobserved variables.
result Accuracy demonstrated through real and simulated data.
Paper uses bond pricing and convexity adjustments to explain herd immunity paradox.
problem Early onset of herd immunity contradicts R value estimates from early stage growth.
method Utilizes Vasicek's bond pricing formula and de Finetti's Theorem approach.
result Reduces modeling discrepancy to simple convexity formulas.
Method estimates parameters for disease spread models robustly.
problem Estimating parameters for disease spread models.
method Statistical Learning applied to Approximate Bayesian Computation.
result Qualitative properties of disease evolution can be assessed.
Epidemiological model updates infection rates in China, US, Italy.
problem Evaluate policy interventions for mitigating the 2019-nCov pandemic.
method Custom SITR model with variational data assimilation for real-time updates.
result Model robust to initial conditions, infers infection numbers and parameters.
New method combines neural nets with epidemic models for better prediction.
problem Improving epidemic prediction and forecasting using deep neural networks.
method Integrates machine learning with compartmental disease models for data-driven analysis.
result Data augmentation strategy improves neural network reliability for epidemic forecasting.
Representation learning systems typically rely on massive amounts of labeled data in order to be trained to high accuracy. Recently, high-dimensional parametric models like neural networks have succeeded in building rich representations using either compressive, reconstructive or supervised criteria. However, the seman…
Paper uses RL to optimize ICU load during COVID-19.
problem Optimizing ICU load during a pandemic.
method Combines epidemic model, Bayesian inference, and RL for adaptive intervention levels.
result RL policies reduce ICU burden compared to historical interventions.
New findings show a balance between data fit and complexity in kernel hyperparameters.
problem Overcorrelation due to reparametrization of kernel hyperparameters.
method Reparametrization of kernel hyperparameters and analysis of marginal likelihood.
result Data fit term influences all other kernel hyperparameters, not just the complexity penalty.
DA-PredGAN uses GANs for accurate COVID-19 spread predictions and data assimilation.
problem Predicting and understanding the spread of COVID-19.
method Generative adversarial network (GAN) for predictions and data assimilation.
result DA-PredGAN accurately predicts and assimilates COVID-19 spread data.
This paper analyzes forecasting models for COVID-19 cases and deaths.
problem Reliable forecasting of COVID-19 cases and deaths is crucial for managing the disease.
method Quantitative analysis of forecasting models across different regions in the US, evaluating model selection, hyperparameter tuning, and training time.
result Model selection is the most influential factor in forecasting performance.
EGDL predicts TB outbreaks with deep learning, integrating epidemiological models.
problem Predicting TB outbreaks with complex spatiotemporal dynamics.
method Modified MN-SIR model with Bayesian inference, deep neural networks.
result EGDL delivers robust and accurate TB outbreak predictions.
Biological neural network mimics CCA for multi-channel data.
problem Implementing CCA in a biologically plausible neural network.
method Derive an online CCA algorithm with local synaptic updates for multi-compartmental neurons.
result The derived neural network architecture and synaptic updates resemble cortical pyramidal neuron behavior.
Unified model forecasts epidemics with spatial and temporal dynamics.
problem Limited accuracy in traditional models and lack of interpretability in deep learning models.
method CSTGNN integrates Spatio-Contact SIR model with Graph Neural Networks.
result Effective spatiotemporal epidemic forecasting with interpretability.
Paper develops fine-grain spatiotemporal risk scores using high-resolution mobility data.
problem Developing reliable spatiotemporal risk scores for safe economic reopening.
method Hawkes process-based technique leveraging high-resolution cell-phone location signals.
result Fine-grain spatiotemporal risk scores based on high-resolution mobility data provide useful insights for safe re-opening.
New method uses machine learning to estimate drug parameters in brain models.
problem Estimating unknown parameters in complex brain drug models.
method Physics-Informed Neural Networks (PINNs) for inverse problem solving.
result Accurate parameter estimation leads to precise drug concentration profiles.
A new machine learning model forecasts COVID-19 incidence at county level in the USA.
problem Inaccurate disease spread forecasting due to spatiotemporal homogeneity assumptions.
method Spatiotemporal machine learning using LSTM architecture with spatial and temporal features.
result COVID-LSTM outperforms COVID-19 Forecast Hub's Ensemble model in accuracy.
This paper proposes a hybrid model for real-time COVID-19 case forecasting.
problem Accurate forecasting of COVID-19 cases, recoveries, and deaths.
method Hybrid Holt's Model embedded with Wavelet-based ANN.
result The proposed model outperforms other models in forecasting accuracy.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
problem Interpreting complex machine learning models.
method Using model-based trees to partition feature space and create interpretable models.
result Model-based trees generate optimal surrogate models that balance interpretability and performance.
Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.
problem Improving generative model performance.
method Integrates a learnable Gauge Field into Flow ODEs.
result Gauge Flow Models outperform traditional Flow Models in Flow Matching experiments.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
Revises Bayesian model averaging for foundation models.
problem Ensemble pre-trained and lightly-finetuned foundation models for improved classification performance.
method Introduces trainable linear classifiers and computationally cheaper model averaging scheme (OMA).
result Ensembled models can better predict on various datasets.
Paper introduces symmetric divergence link models for probability distributions.
problem Symmetric divergence measures for probability distributions.
method Two general classes of link models: one for survival functions and another for cumulative probability distribution functions.
result Advantages of symmetric divergence measures over asymmetric measures for model averaging and feature assessment.
New method to handle credit portfolio model uncertainties.
problem Model risk in credit portfolio models.
method Demonstrates comprehensive yet easy-to-implement approach to uncertainty in model parameters.
result Comprehensive method to deal with model uncertainties.
The paper tests stock return models and uses LSTM to predict stock returns.
problem Validating stock return models and predicting stock returns.
method Used Fama-French three-factor, four-factor, and five-factor models; also used LSTM model.
result Fama-French five-factor model shows better validity for stock returns.
Researchers review challenges in interpreting additive models, especially neural additive models.
problem Challenges in interpreting additive models, particularly neural additive models.
method Review of generalized additive models and discussion of nonidentifiability.
result Challenges in claiming interpretability or suitability for safety-critical applications of additive models.
Novel hybrid modeling combines ML and physics for real-time diagnosis.
problem Real-time diagnosis of complex systems.
method Combines machine learning and physics-based models to create reduced-order models.
result Generated models are two orders of magnitude simpler, improving efficiency.
CRS model improves ranking data modeling with theoretical guarantees.
problem Lack of rich, multimodal models for ranking data.
method Contextual Repeated Selection (CRS) model for multimodal ranking data.
result CRS model significantly outperforms existing methods in various ranking contexts.
Sigma models linked to Gross-Neveu models via quiver varieties.
problem Understanding the relationship between sigma models and Gross-Neveu models.
method Exploring the mathematical correspondence between sigma models and Gross-Neveu models, including their geometric and trigonometric/elliptic deformations.
result Sigma models are mathematically equivalent to Gross-Neveu models under certain conditions.
Interpretable machine learning has become a strong competitor for traditional black-box models. However, the possible loss of the predictive performance for gaining interpretability is often inevitable, putting practitioners in a dilemma of choosing between high accuracy (black-box models) and interpretability (interpr…
Simple models are preferred over complex models, but over-simplistic models could lead to erroneous interpretations. The classical approach is to start with a simple model, whose shortcomings are assessed in residual-based model diagnostics. Eventually, one increases the complexity of this initial overly simple model a…
Matryoshka hides secret models in a carrier model, achieving high capacity and robustness.
problem Stealing functionality of private ML data by hiding models in a carrier model.
method Parameter sharing approach exploiting the learning capacity of the carrier model.
result Hides a 26x larger secret model or 8 secret models in the carrier model.
Seq2Seq models speed up epidemic model predictions.
problem Complex epidemic models are computationally expensive.
method Used deep seq2seq models as surrogates for complex models.
result Surrogates predict scenarios up to several thousand times faster.
This work develops scalable model selection methods with fast update and selection.
problem Efficient model selection for large pools of candidate models.
method Isolated model embedding, which supports asymptotically fast update and selection.
result Standardized Embedder achieves competitive model selection performances.
Paper proposes BMPO to optimize policies using bidirectional models.
problem Model-based reinforcement learning's reliance on forward model accuracy.
method Develops BMPO using both forward and backward models for policy optimization.
result BMPO outperforms state-of-the-art methods in sample efficiency and asymptotic performance.
Copulas outperform marginal models in multivariate risk forecasting, reducing model risk by narrowing down the set of models.
problem Model risk in multivariate risk forecasting, especially during crises.
method Comprehensive empirical study comparing Copula-GARCH models with fixed marginals, copulas, or neither.
result Model risk is almost entirely due to copula choice, not marginal models.
BayesBlend blends multiple models' predictions for better insurance loss predictions.
problem Improving insurance loss predictions by combining multiple models.
method Pseudo-Bayesian model averaging, stacking, and hierarchical stacking.
result BayesBlend provides a user-friendly way to blend model predictions and estimate weights.