The paper parallelizes HMM inference for efficient long-term computations.
problem Efficiently computing inference in long-term hidden Markov models.
method Parallelization using associative elements and operators for sum-product and max-product algorithms.
result The proposed parallel algorithms are computationally efficient for long time horizons.
New algorithms improve causal direction inference accuracy using parallel ensemble methods.
problem Stability of causal direction inference results from observational data.
method Parallel ensemble frameworks to map and improve inference accuracy.
result Significant improvement in accuracy of causal direction inference.
New algorithm for parallel inference without block partitioning.
problem Slow inference in probabilistic models due to single-variable updates.
method Lower bounds ELBO with forest mixture bound for parallel inference.
result Algorithm converges faster for 'forest-like' models.
Parallelizes MCMC for spatially dependent models.
problem Inference in models with spatially determined conditional independence.
method Coloring a factor graph over regions of latent space.
result Significant speedups achieved with no loss in inference quality.
Accelerator synthesizes deep learning inference from C code using FPGA hardware.
problem Efficiently implementing deep learning models on FPGAs.
method Parallelized C code using Pthreads, high-level synthesis, reduced precision, zero-weight-skipping.
result Peak performance of 138 effective GOPS on VGG-16 on Intel Arria 10 SoC FPGA.
A new framework for efficient Bayesian network inference.
problem High-dimensional Bayesian networks are hard to infer due to computational scaling.
method Directed convex subgraphs and minimal d-decomposition tree for decomposition, enabling parallel computation.
result The method reduces computational cost and enables parallel computation.
Sequential coordinate ascent is more robust in high-dimensional linear regression.
problem Behavior difference between sequential and parallel coordinate ascent in variational inference.
method Comparison of sequential and parallel coordinate ascent algorithms in high-dimensional linear regression.
result Sequential algorithm converges under more relaxed conditions than parallel algorithm.
New method speeds up Bayesian non-parametric model inference.
problem Difficult inference in high-dimensional Bayesian non-parametric models.
method Accelerated feature proposal mechanism and parallel approximate inference.
result Can quickly reach local convergence to posterior distribution.
A new parallel MCMC method for Indian Buffet Process models.
problem Slow inference in Indian Buffet Process models.
method Hybrid sampler combining collapsed and uncollapsed MCMC for parallel computation.
result Asymptotically exact parallel inference for Indian Buffet Process models.
QEM uses parallel importance weighting for fast approximate Bayesian inference.
problem Bayesian inference challenges in large models with many observations and latent variables.
method Expectation Maximization (EM) with massively parallel importance weighting.
result QEM is faster and more scalable than RWS and VI.
We consider the problem of maximum a posteriori (MAP) inference in discrete graphical models. We present a parallel MAP inference algorithm called Bethe-ADMM based on two ideas: tree-decomposition of the graph and the alternating direction method of multipliers (ADMM). However, unlike the standard ADMM, we use an inexa…
Parallelizes active learning for Bayesian inference using Nested Sampler.
problem Expensive likelihood evaluations in complex experiments.
method Uses Nested Sampler to generate nearly-optimal batches of candidates in parallel.
result Comparable accuracy to sequential conditioning with efficient parallelization.
A distributed Bayesian matrix factorization method speeds up inference by 10x with minimal accuracy loss.
problem Scaling Bayesian matrix factorization for large datasets with limited communication.
method Hierarchical decomposition of the joint posterior distribution, coupled subset inferences, and efficient approximate implementation.
result Achieves a speed-up of almost an order of magnitude over full posterior inference with negligible accuracy loss.
We develop a parallel variational inference (VI) procedure for use in data-distributed settings, where each machine only has access to a subset of data and runs VI independently, without communicating with other machines. This type of "embarrassingly parallel" procedure has recently been developed for MCMC inference al…
A new method combines MCMC results to avoid failures in parallel computing.
problem Parallel MCMC's sensitivity to subposterior sampling issues leads to failures.
method Parallel Active Inference (PAI) uses Gaussian Process (GP) surrogate modeling and active learning.
result PAI successfully combines MCMC results where previous methods fail.
Improved voice conversion with semi-supervised learning.
problem Voice conversion with limited parallel data.
method Amortized variational inference with parallel and non-parallel utterances.
result Semi-supervised training improves voice conversion performance.
Asynchronous SVI scales up Bayesian computation for massive data.
problem Scaling stochastic variational inference to massive data.
method Lock-free asynchronous parallel implementation of SVI.
result Linear speed-up and asymptotic convergence rate O ( 1 / ( T ) ) O(1/\sqrt(T)) O ( 1/ ( T )) . Parallel Gaussian process surrogate for noisy likelihood evaluations in Bayesian inference.
problem Bayesian inference with limited noisy log-likelihood evaluations from complex models.
method Hierarchical Gaussian process surrogate model for log-likelihood, batch-sequential design strategies.
result Robust, highly parallelizable, and sample-efficient method.
A new method uses surrogate models to speed up landscape evolution model inference.
problem Complex and computationally expensive landscape evolution models.
method Surrogate-assisted parallel tempering for Bayesian inversion.
result Significant reduction in computational cost with preserved solution quality.
It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to specify the model and having a compiler automatically generate an inference procedure for it. For this approach to be practical, it is important…
New method ensures consistent inference across different tensor parallel sizes for large language models.
problem Non-deterministic inference in large language models due to inconsistent reduction orders across GPUs.
method Tree-Based Invariant Kernels (TBIK) that align intra- and inter-GPU reduction orders through a unified hierarchical binary tree structure.
result Bit-wise identical results across different tensor parallel sizes for RL training.
Examines parallels between human subjects and texts for causal inference.
problem Ambiguity and fallacies in causal inference using textual data.
method Two strategies: shifting from traits to perceptions and from concepts to parts.
result Highlights the importance of clarifying fundamental concepts.
Unified framework for statistical inference in gradient boosting regression.
problem Challenges in statistical inference and uncertainty quantification for gradient boosting.
method Integrates dropout or parallel training with regularization for CLT in boosting.
result Increasing dropout rate and parallel trees enhances signal recovery and performance.
New parallel algorithms optimize on manifolds, reducing communication costs.
problem Optimization on non-Euclidean spaces like manifolds.
method Generalized parallel inference algorithms for optimization on manifolds.
result Communication-efficient and convergent algorithms for manifold optimization.
Efficient method for high confidence level inference using parallel stochastic optimization.
problem Uncertainty quantification for online estimation.
method Small number of independent multi-runs to construct t-based confidence intervals.
result Rigorous theoretical guarantee for exact coverage of confidence intervals.
Celeste learns astronomical catalogs from large datasets.
problem Inferring astronomical catalogs from large-scale datasets.
method Scalable Bayesian inference with Julia, parallel optimization.
result Learned catalogs from modern large-scale astronomical datasets.
Parallel Bayesian model selection improves accuracy and robustness.
problem Efficient model selection for large datasets and robustness to outliers.
method Divide and conquer approach, parallel inference, geometric median aggregation.
result Improved model selection accuracy and robustness to outliers.
Parallelizes graph embedding for large graphs.
problem Large graphs make existing graph embedding techniques inefficient.
method Distributed parallel computation framework using a cluster of compute nodes.
result Parallel computation scales well and maintains embedding quality.
Nonparametric mixture models based on the Dirichlet process are an elegant alternative to finite models when the number of underlying components is unknown, but inference in such models can be slow. Existing attempts to parallelize inference in such models have relied on introducing approximations, which can lead to in…
Method predicts how probability distributions evolve over time.
problem Predicting how systems described by probability distributions evolve under different conditions.
method Wasserstein Parallel Transport
result Wasserstein Parallel Transport provides counterfactual comparisons of distributional dynamics.
ParaMonte simplifies Monte Carlo simulations for various scientific fields.
problem Efficiently performing Monte Carlo simulations for complex models.
method Unified, high-performance, parallelized library for C, C++, Fortran.
result Automates and streamlines Monte Carlo sampling for arbitrary-dimensional functions.
Improves parallel deep model performance by restructuring and pruning.
problem Latency in parallel deep model execution due to interdependency among sub-models.
method Layer-wise model restructuring and pruning, using ℓ 0 \ell_0 ℓ 0 optimization and Munkres assignment algorithm. result Significantly improves efficiency of distributed inference in terms of communication and computational complexity.
An infinite parallel tempering bouncy particle sampler improves sampling efficiency for multimodal distributions.
problem Sampling from complex posterior distributions with high accuracy and efficiency.
method Introduced an infinite parallel tempering bouncy particle sampler (BPS-PT) to accelerate convergence.
result Demonstrated improved sampling efficiency for multimodal distributions through numerical simulations.
A new method for faster neural network responses reduces training and inference times.
problem Efficiently integrating temporal features in deep neural networks for real-time applications.
method Theoretical framework and demonstration of different rollouts, including streaming rollout, to achieve faster responses and parallel execution.
result The streaming rollout maximizes early and frequent responses, leading to better performance and reduced runtime.
NeLLoC improves image compression with parallel decoding.
problem Image compression with OOD generalization.
method Local autoregressive model with parallel decoding.
result Significant gains in compression runtime.
FastKCI speeds up KCI tests for causal inference on large datasets.
problem Cubic computational complexity of kernel-based conditional independence tests.
method Mixture-of-experts approach with parallel Gaussian process inference.
result Substantial computational speedups with maintained statistical power.
In real world industrial applications of topic modeling, the ability to capture gigantic conceptual space by learning an ultra-high dimensional topical representation, i.e., the so-called "big model", is becoming the next desideratum after enthusiasms on "big data", especially for fine-grained downstream tasks such as …
Massively parallel RWS improves inference in complex models.
problem Exponential sample requirement for effective importance weighting.
method Draws K K K samples of all n n n latent variables and individually reasons through all combinations. result Significant improvements over standard RWS.
EP-GFlowNets parallelize GFlowNet training for large-scale Bayesian inference.
problem Prohibitive repeated evaluations of unnormalized distributions for large-scale posterior sampling.
method Divide-and-conquer approach with server learning from local models.
result EP-GFlowNets enable efficient parallel and federated Bayesian inference.
ELFI is a Python library for likelihood-free inference.
problem Performing inference when likelihood functions are intractable.
method ELFI provides a network of components for likelihood-free inference, including Bayesian Optimization for Likelihood-Free Inference (BOLFI).
result ELFI accelerates likelihood-free inference up to several orders of magnitude.
New method speeds up diffusion models inference to sub-linear time.
problem Efficient inference of diffusion models for high-dimensional data.
method Parallel sampling with Picard iterations within blocks.
result Achieves sub-linear time complexity of O ~ ( p o l y log d ) \widetilde{\mathcal{O}}(\mathrm{poly} \log d) O ( poly log d ) . dSMC improves parallel processing of state-space models.
problem Processing multiple observations efficiently in state-space models.
method A parallel-in-time particle smoother that reduces complexity to log(T).
result dSMC achieves O(log(T)) time complexity on parallel architectures.
Functional tensors unify probabilistic programming with automatic differentiation.
problem Designing probabilistic programming systems that can handle diverse inference strategies.
method Introducing functional tensors that capture benefits of tensors and continuous probability distributions.
result Functional tensors enable parallel exact inference for various modeling motifs.
This paper improves parallel belief propagation for scalable machine learning.
problem Efficient parallelization of belief propagation for large-scale machine learning tasks.
method Use of scalable relaxed schedulers to parallelize belief propagation.
result Our approach outperforms previous methods in scalability and convergence time.
A new method uses deep invertible transformations to parallelize MCMC for large datasets.
problem Scaling MCMC methods to large distributed datasets.
method Introduces a deep invertible transformation to approximate subposteriors, enabling efficient parallel MCMC.
result Demonstrates superior performance compared to existing methods in various challenging scenarios.
A new method infers causal gene regulatory networks from parallel CRISPR interventions and transcriptomic data.
problem Learning causal gene regulatory networks from observational data is complicated by lack of identifiability and a combinatorial solution space.
method A continuous optimization framework that leverages observational and interventional data to infer a single causal structure, assuming a linear Structural Equation Model (SEM).
result A provably consistent estimator of the true DAG under mild assumptions.
A fundamental aspect of biological information processing is the ubiquity of sequence-function relationships -- functions that map the sequence of DNA, RNA, or protein to a biochemically relevant activity. Most sequence-function relationships in biology are quantitative, but only recently have experimental techniques f…
This research models cancer progression using Bayesian Networks and parallel algorithms to infer the order of genetic mutations.
problem Inferring the order of genetic mutations leading to cancer progression.
method Bayesian Networks, Genetic Algorithms, multi-threading, multi-node architecture.
result The parallel approach reduces inference time by 84x and maintains high accuracy.