Papers Sequential Bayesian Inference
“Sequential Bayesian Inference” 태그가 달린 논문 20편 · 필터 해제
Surprise Calibration for Better In-Context Learning
In-context learning (ICL) has emerged as a powerful paradigm for task adaptation in large language models (LLMs), where models infer underlying task structures from a few demonstrations. However, ICL remains susceptible …
Bayesian InferenceIn-Context LearningSequential Bayesian InferenceConnections between sequential Bayesian inference and evolutionary dynamics
It has long been posited that there is a connection between the dynamical equations describing evolutionary processes in biology and sequential Bayesian learning methods. This manuscript describes new research in which t…
Bayesian InferenceSequential Bayesian InferenceBayesian Online Natural Gradient (BONG)
We propose a novel approach to sequential Bayesian inference based on variational Bayes (VB). The key insight is that, in the online setting, we do not need to add the KL term to regularize to the prior (which comes from…
Bayesian InferenceSequential Bayesian InferenceContinual Learning via Sequential Function-Space Variational Inference
Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved challenging in practice. Addressing the drawb…
Bayesian InferenceContinual LearningSequential Bayesian InferenceVariational InferenceLearning Differentiable Particle Filter on the Fly
Differentiable particle filters are an emerging class of sequential Bayesian inference techniques that use neural networks to construct components in state space models. Existing approaches are mostly based on offline su…
Bayesian InferenceObject Trackingparameter estimationSequential Bayesian Inference+1A transport approach to sequential simulation-based inference
We present a new transport-based approach to efficiently perform sequential Bayesian inference of static model parameters. The strategy is based on the extraction of conditional distribution from the joint distribution o…
Bayesian Inferenceparameter estimationSequential Bayesian InferenceA digital twin framework for civil engineering structures
The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, …
Bayesian InferenceCantilever BeamDecision MakingManagement+1An overview of differentiable particle filters for data-adaptive sequential Bayesian inference
By approximating posterior distributions with weighted samples, particle filters (PFs) provide an efficient mechanism for solving non-linear sequential state estimation problems. While the effectiveness of particle filte…
Bayesian InferenceSequential Bayesian InferenceState EstimationOn Sequential Bayesian Inference for Continual Learning
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revisit sequential Bayesian inference and tes…
Bayesian InferenceContinual LearningSequential Bayesian InferenceActive Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation
Autonomous robots need to learn the categories of various places by exploring their environments and interacting with users. However, preparing training datasets with linguistic instructions from users is time-consuming …
Bayesian InferenceEfficient ExplorationLanguage AcquisitionSequential Bayesian InferenceCan Sequential Bayesian Inference Solve Continual Learning?
Previous work in Continual Learning (CL) has used sequential Bayesian inference to prevent forgetting and accumulate knowledge from previous tasks. A limiting factor to performing Bayesian CL has been exact inference in …
Bayesian InferenceContinual LearningSequential Bayesian InferenceDiscriminative Bayesian filtering lends momentum to the stochastic Newton method for minimizing log-convex functions
To minimize the average of a set of log-convex functions, the stochastic Newton method iteratively updates its estimate using subsampled versions of the full objective's gradient and Hessian. We contextualize this optimi…
Sequential Bayesian InferenceStochastic OptimizationThe Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models
The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalma…
Brain Computer InterfaceregressionSequential Bayesian InferenceState Estimation+1Hidden Markov Model: Tutorial
This is a tutorial paper for Hidden Markov Model (HMM). First, we briefly review the background on Expectation Maximization (EM), Lagrange multiplier, factor graph, the sum-product algorithm, the max-product algorithm, a…
Action RecognitionBayesian InferenceBayesian Optimisationmodel+5Thompson Sampling on Symmetric $α$-Stable Bandits
Thompson Sampling provides an efficient technique to introduce prior knowledge in the multi-armed bandit problem, along with providing remarkable empirical performance. In this paper, we revisit the Thompson Sampling alg…
Bayesian InferenceDecision MakingSequential Bayesian InferenceSequential Decision Making+1Particle Flow Bayes' Rule
We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation. We prove that such an ODE operator exists. I…
Bayesian InferenceMeta-LearningSequential Bayesian InferenceFunctional Regularisation for Continual Learning with Gaussian Processes
We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network. This method, referred to as functional regularisation for Cont…
Bayesian InferenceContinual LearningGaussian ProcessesSequential Bayesian InferenceA Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding
Given a stationary state-space model that relates a sequence of hidden states and corresponding measurements or observations, Bayesian filtering provides a principled statistical framework for inferring the posterior dis…
Sequential Bayesian InferenceKernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter
In this work, a novel sequential Monte Carlo filter is introduced which aims at efficient sampling of high-dimensional state spaces with a limited number of particles. Particles are pushed forward from the prior to the p…
Bayesian InferenceSequential Bayesian InferenceStochastic OptimizationEfficient Low-Order Approximation of First-Passage Time Distributions
We consider the problem of computing first-passage time distributions for reaction processes modelled by master equations. We show that this generally intractable class of problems is equivalent to a sequential Bayesian …
Bayesian InferenceSequential Bayesian Inference