Improves neural relational inference for dynamic multi-agent trajectories.
problem Limited accuracy of NRI in short output sequences for relational inference in multi-agent trajectories.
method Proposes DYnamic multi-AgentRelational Inference (DYARI) model to handle changing interactions over time.
result DYARI model outperforms NRI in dynamic relational inference tasks.
VTIRT speeds up IRT inference for dynamic learner proficiency.
problem Expensive and slow inference algorithms for dynamic IRT models.
method Variational Temporal IRT (VTIRT) for fast, accurate inference.
result Orders of magnitude speedup in inference runtime with accurate results.
Efficiently infers switching nonlinear systems with collapsed amortized variational inference.
problem Inference in switching nonlinear dynamical systems with discrete latent variables.
method Learn an inference network as a proposal for continuous latent variables, performing exact marginalization of discrete variables.
result Successfully segments time series data into meaningful regimes using piece-wise nonlinear dynamics.
Framework infers Langevin dynamics from stochastic observations of latent systems.
problem Inferring non-stationary Langevin dynamics from indirect stochastic observations.
method Non-parametric framework explicitly modeling stochastic observation process and non-stationary latent dynamics.
result Correct inference of non-stationary dynamics requires accounting for non-equilibrium states and observation duration.
Proposes exact inference for continuous-time Gaussian process dynamics.
problem Inexact inference methods for continuous-time Gaussian process dynamics are impractical for irregularly-sampled data.
method Uses higher-order numerical integrators to discretize dynamics with arbitrary accuracy and proposes multistep and Taylor integrators for exact inference.
result Demonstrates accurate representation of continuous-time systems through exact GP inference.
New method for state inference in state-space models with unknown dynamics.
problem State inference in state-space models with computationally expensive and undefined dynamics.
method Estimate state transition dynamics using a multi-output Gaussian process and Bayesian Neural Network as a surrogate model.
result Significant improvement in accuracy for state inference and prediction in non-stationary user models.
A new method for efficient variational inference in dynamic models.
problem Performing variational inference in dynamical latent variable models efficiently.
method Amortized variational filtering algorithm derived from the filtering setting.
result Improves performance across various deep dynamical latent variable models.
Study analyzes inference dynamics in deep generative models to improve concept formation.
problem Understanding the mechanism of inference in deep generative models.
method Numerical analysis of a VAE model with added noise, focusing on latent space activity patterns and concept formation.
result Inference dynamics in VAEs approach a concept as input data noise increases, enhancing generalization ability.
New method infers dynamical systems from population data.
problem Inferring dynamical systems from population data.
method Deducing and estimating Fokker-Planck equation, projecting to test functions, sparse inference.
result Induces driving forces of dynamical systems.
Adaptive algorithm learns latent dynamical systems from sequential data.
problem Learning low-dimensional latent dynamical systems from high-dimensional sequential data.
method Combines amortized inference with path integral control to approximate inference.
result Proposed method leads to tighter lower bounds in sequential data learning.
Researchers develop a new SMC sampler for Wishart processes to improve dynamic covariance inference.
problem Challenging inference of dynamic covariance in various scientific fields.
method Introduce Sequential Monte Carlo (SMC) sampler for the Wishart process.
result SMC sampling provides more robust estimates and out-of-sample predictions of dynamic covariance.
We study the inference of a model of dynamic networks in which both communities and links keep memory of previous network states. By considering maximum likelihood inference from single snapshot observations of the network, we show that link persistence makes the inference of communities harder, decreasing the detectab…
Automated model predicts C. elegans escape behavior.
problem Complex escape behavior of C. elegans due to rising temperature.
method Phenomenological modeling using Sir Isaac dynamical inference platform.
result Inferred model accurately predicts worm behavior and is biologically interpretable.
Flexible Bayesian inference for complex dynamical systems.
problem Bayesian inference for nonlinear, hierarchical dynamical systems.
method Stochastic optimisation of a variational autoencoder for ODE dynamics.
result Efficient scaling to large datasets and interpretability.
New couplings improve understanding of molecular dynamics convergence.
problem Understanding convergence of Andersen dynamics in high dimensions.
method Presented couplings to obtain sharp convergence bounds in the Wasserstein sense.
result Sharp convergence bounds in the Wasserstein sense without global convexity.
A model infers dynamic networks from partial observation of cascading processes.
problem Inferring evolving networks from partial observation of node and edge data.
method A novel framework based on a mixture of coupled hierarchical Dirichlet processes.
result Explicit predictive distribution over edges of the underlying network, including future edges.
Scalable Bayesian inference for state space models using variational inference and SMC.
problem Performing fully Bayesian inference in state space models.
method Combining variational methods with sequential Monte Carlo sampling.
result Fully Bayesian inference of dynamic latent states and static parameters.
Proposes DCNAR for dynamic causal inference from neural time series.
problem Uncertainty and evolution of causal structure in real-world domains.
method Two-stage neural causal modeling integrating discovery and inference.
result Dynamic causal inferences are more stable and meaningful than alternatives.
DVK model infers uncertainty-aware dynamical models for better control.
problem Uncertainty in nonlinear dynamical systems makes prediction and control challenging.
method Deep Variational Koopman (DVK) model infers distributions over observations.
result DVK model provides a distribution over dynamical models for long-term prediction and control.
Physics-informed methods infer spatial dynamics from static snapshots, but limits exist.
problem Inferring spatial dynamics from static molecular patterns.
method Combining flexible representations with mechanistic constraints, analyzing structural identifiability, and adapting physics-informed schemes.
result Static spatial patterns can identify spatially varying dynamics, but limits exist due to modeling choices.
Adaptive Langevin dynamics reduces bias in Bayesian inference with mini-batching.
problem Bias in posterior sampling due to mini-batching in Bayesian inference.
method Adaptive Langevin dynamics with dynamical friction to correct noise.
result Quantified bias in posterior distribution due to mini-batching.
New method improves variational inference for dynamical systems without extra computational cost.
problem Inexact variational inference leading to overconfident posterior and overestimation of process noise.
method Proposes a non-factorised posterior distribution for Gaussian process transition functions.
result Improves accuracy of posterior over transition function and process noise estimation.
Model infers latent variables in sparse coding models using Langevin dynamics.
problem Sampling posterior distribution in sparse coding models.
method Langevin dynamics for inference and simultaneous learning of parameters.
result Langevin dynamics efficiently sample from 'L0 sparse' posterior distribution.
New method speeds up inference for dynamical systems with sparse data.
problem Challenges in applying gradient matching methods to real-world, partially observable systems.
method Scalable variational inference framework for ordinary differential equations.
result Offers computational speedups, improved accuracy, and works well under model misspecifications.
Optimizes variational inference for dynamic network models.
problem Estimating pairwise inner products and intercepts in dynamic latent space models.
method Structured mean-field variational inference with block coordinate ascent algorithm.
result Variational risk attains minimax optimal rate with logarithmic factor under certain conditions.
DRNets dynamically route instances to efficient transformations.
problem High inference costs due to static model capacity.
method Dynamic Routing Networks (DRNets) with RouterNets for branch selection.
result DRNets reduce inference costs with comparable performance.
SING improves state inference in latent SDE models for better drift function estimation.
problem Intractable posterior inference in latent SDE models.
method Natural gradient variational inference.
result SING provides faster and more reliable inference in latent SDE models.
This paper addresses metaconsistency in Bayesian inference for metastable systems.
problem Inference for metastable systems may not be consistent, but can be metaconsistent over large but finite time intervals.
method Introduces metaconsistency in a Bayesian framework, discusses its relation to spectral properties of model dynamics.
result Metaconsistency can be exploited to infer sub-systems efficiently from larger systems.
Neural EKF improves structural dynamics prediction.
problem Accurately predicting structural response for health monitoring.
method Neural Extended Kalman Filter (Neural EKF) for learning dynamics.
result Significant predictive capabilities demonstrated on simulated and real-world data.
Learn object dynamics from unlabeled images.
problem Unsupervised learning of multiple object dynamics from unlabeled video sequences.
method Probabilistic model generating noisy positions, followed by non-linear rendering. Efficient inference method for querying the model.
result Efficient inference of object dynamics from unlabeled images.
Model captures complex neural dynamics with Gaussian process and RNN.
problem Challenges in extracting latent dynamics from noisy neural data.
method Gaussian process recurrent neural networks (GP-RNN) for nonlinear, non-Markovian dynamics.
result Outperforms state-of-the-art methods in reconstructing latent dynamics.
This article connects reinforcement learning to probabilistic inference.
problem Intelligent decision making under uncertainty.
method Generalization of reinforcement learning to probabilistic inference.
result Maximum entropy reinforcement learning is equivalent to probabilistic inference.
Proposes a graph dynamics prior for more accurate relational inference.
problem Identifying interactions in dynamical systems from observed dynamics.
method Graph Dynamics Prior (GDP) that uses error amplification in non-local polynomial filters.
result Reconstructs graphs more accurately than previous methods, robust to under-sampling.
New method learns cell trajectories and network interactions from single-cell data.
problem Network inference in systems biology from steady-state data.
method Min-entropy estimation for stochastic dynamics, leveraging both temporal and perturbational data.
result Jointly learns cellular trajectories and network interactions.
VIND infers smooth nonlinear dynamics from electrophysiology data.
problem Analyzing smooth, nonlinear time series data from neuroscience experiments.
method Variational Inference for Nonlinear Dynamics (VIND) with structured approximate posterior and fixed-point iteration.
result VIND reconstructs 5D latent space variables similar to Hodgkin-Huxley models, and excels in predicting future neural activity.
Paper uses black-box inference to estimate non-linear latent force models.
problem Estimating posterior state and forcing term in non-linear systems with unknown forcing terms.
method Black-box variational inference with local inverse autoregressive flows.
result Demonstrates effectiveness of approximation on known posterior systems and non-linear dynamics.
Study analyzes Bayesian inference algorithms using dynamical functional approach.
problem Analysis of approximate inference algorithms for large Gaussian latent variable models.
method Dynamical functional approach to model nontrivial dependencies and obtain exact effective stochastic process.
result Closed-form expressions for the rate of convergence are derived and validated.
Optimally explores dynamical systems with varying properties using context inference.
problem Learning dynamics models for systems with varying properties.
method Formulates dynamics models as stochastic processes conditioned on a latent context variable inferred from system transitions. Uses probabilistic formulation to compute optimal action sequences for exploration.
result Demonstrates effectiveness of the method on non-linear toy-problems and reinforcement learning environments.
This paper introduces a new method to interpolate between MCMC and variational inference.
problem The tradeoff between accuracy and efficiency in MCMC and variational inference methods.
method Derives a distribution over variational parameters to minimize divergence, and provides sampling methods.
result A new method that interpolates between MCMC and variational inference, improving efficiency.
Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data should be analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer latent dynamics from simultaneous…
DISTANA improves weather prediction by inferring hidden factors from temperature data.
problem Inferring hidden factors in spatiotemporal processes without supervision.
method Enhanced DISTANA architecture for spatiotemporal data, active tuning for latent state inference.
result DISTANA achieves more accurate predictions than other methods, inferring hidden factors from temperature data.
Valid inference method for DTW distance for abnormal time-series detection.
problem Statistical inference on DTW distance under uncertain conditions.
method Conditional selective inference framework to derive valid p-values.
result First method to provide valid p-values for DTW distance.
Efficient EP algorithm improves smoothing distribution inference in financial models.
problem Computational intractability of smoothing distribution in high dimensions.
method Adapted expectation propagation (EP) algorithms for the unified skew-normal family.
result Accuracy gains in financial illustrations over existing approximate algorithms.
TSCI improves causal inference in dynamical systems using vector fields.
problem Challenges in causal discovery with time series data in dynamical systems.
method TSCI method using vector fields to check for synchronization between learned dynamics.
result TSCI outperforms traditional methods like CCM and its generalizations.
Proposes a new model for better speech segmentation.
problem Improving speech segmentation accuracy.
method Integrates recurrent explicit duration variables into rSLDS and uses Pólya-gamma augmentation for inference.
result Demonstrates improved segmentation on various datasets.
State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference \emph{and learning} (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gauss…
Efficiently trains dynamic word embedding models with structured variational inference.
problem Training continuous latent time series models with structured variational approximations.
method Analogous to the forward-backward algorithm, a BBVI algorithm that scales linearly in time.
result Efficiently samples from variational distribution and estimates ELBO gradients.
Machine learning infers fluid dynamics from data without prior knowledge.
problem Inferring complex fluid behavior from limited data.
method Reservoir computing for partial and full inference of fluid variables and energy functions.
result Reservoir systems can infer long-term fluid dynamics and energy spectra from past data.