The graphics processing unit (GPU) has emerged as a powerful and cost effective processor for general performance computing. GPUs are capable of an order of magnitude more floating-point operations per second as compared to modern central processing units (CPUs), and thus provide a great deal of promise for computation…
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48 results for “Graphics Processing Units”
GPU speeds up Monte Carlo simulations for large time steps.
problem Slow convergence and inaccurate solutions with large time steps in Monte Carlo simulations.
method Generalizes the Seven League scheme for GPU acceleration.
result Significantly improved computational speed.
Investigates the impact of batch size on GPU and TPU performance.
problem Optimizing performance of GPUs and TPUs during training and inference phases.
method Investigated the impact of batch size on performance of GPUs and TPUs using standard MNIST and Fashion-MNIST datasets.
result Significant speedup was achieved even with low-scale usage of TPUv2 units, up to 10x for training and 2x for prediction.
Block based Singular Value Decomposition approach to matrix factorization for recommender systemscs.LG
This paper improves SVD for recommender systems using block-based matrix factorization.
problem Scalability and performance issues in recommender systems.
method Block-based Singular Value Decomposition (BMF) for matrix factorization.
result BMF paired with SVD enhances performance and scalability.
Neural networks speed up statistical inference.
problem Efficient statistical inference for complex models.
method Neural networks for learning complex mappings.
result Amortized inference speeds up inference processes.
A new CNN-based algorithm improves Fourier ptychography for faster, more robust image reconstruction.
problem Slow and inefficient Fourier ptychography reconstruction under system aberrations.
method A CNN-based iterative phase retrieval algorithm trained on GPUs.
result Significantly faster and more robust image reconstruction under system aberrations.
New GPU algorithm speeds up Gaussian Process analysis.
problem Reducing computational complexity for large spatial datasets.
method Implemented three GPU methods for Vecchia Approximation.
result New GPU method outperforms existing methods.
The paper develops a new simulation technique for estimating conditional expectations in financial models.
problem Estimating conditional expectations in financial models with expensive simulation of endogenous variables.
method Introduces a hierarchical simulation scheme with oversimplified defaults to address variance issues.
result The hierarchical simulation technique significantly improves the success of neural net regression for conditional expectation estimation.
Extensive neural networks eliminate the need for SABR pricing formulas.
problem Lack of exact pricing formulas for the SABR model.
method Used a GPU-based simulation and an extensive neural network to learn implied volatilities.
result Neural networks achieve high accuracy and efficiency comparable to Monte-Carlo simulations.
A Weak Supervision Approach to Detecting Visual Anomalies for Automated Testing of Graphics Unitscs.LG
Automated graphics testing detects novel corruptions without manual labeling.
problem Detecting novel visual corruptions in graphics unit testing without manual labeling.
method Reproduces driver bugs to generate corruptions and uses Multiple Instance Learning (MIL) methods.
result Significantly outperforms unsupervised methods and discovers novel corruptions.
Introduces BMF for efficient matrix factorization of large data.
problem Efficiently factorizing large scale matrices with limited memory.
method Uses block matrix approach and factorization at a block level.
result Demonstrates faster convergence on large matrices.
New algorithms speed up EMD computation by four orders of magnitude.
problem High computational complexity of EMD for discrete probability distributions.
method Data-parallel approximation algorithms for linear time complexity.
result Four orders of magnitude faster than existing methods.
Surface parameterizations have been widely used in computer graphics and geometry processing. In particular, as simply-connected open surfaces are conformally equivalent to the unit disk, it is desirable to compute the disk conformal parameterizations of the surfaces. In this paper, we propose a novel algorithm for the…
Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single…
Graph neural networks extend neural Bayes estimators to irregular spatial data.
problem Estimating parameters from irregular spatial data with computational efficiency.
method Employing graph neural networks to approximate Bayes estimators for irregular spatial data.
result Extending neural Bayes estimation to irregular spatial data with computational benefits.
DREAM model improves computational efficiency for non-linear effects in relational event models.
problem Efficiently modeling non-linear effects in dynamic relational networks.
method Introduces Deep Relational Event Additive Model (DREAM) using Neural Additive Models.
result Demonstrates superior computational efficiency compared to traditional REM approaches.
Rgtsvm provides a fast and flexible support vector machine (SVM) implementation for the R language. The distinguishing feature of Rgtsvm is that support vector classification and support vector regression tasks are implemented on a graphical processing unit (GPU), allowing the libraries to scale to millions of examples…
Modeling massive highly-multivariate nonstationary spatial data with the basis graphical lassostat.ME
New framework models complex spatial data with basis functions and graphical vectors.
problem Modeling highly-multivariate spatial processes with varying resolutions.
method Extends graphical lasso to multivariate Gaussian processes with independent graphical vectors at different resolutions, using an orthogonal basis and fusion penalty.
result Linear complexity and parsimonious conditional independence structure in multilevel graphical model.
A Hierarchical Graphical Model for Big Inverse Covariance Estimation with an Application to fMRIstat.ME
Brain networks has attracted the interests of many neuroscientists. From functional MRI (fMRI) data, statistical tools have been developed to recover brain networks. However, the dimensionality of whole-brain fMRI, usually in hundreds of thousands, challenges the applicability of these methods. We develop a hierarchica…
Bayesian method estimates Kronecker graphical models from autoregressive processes.
problem Estimating Kronecker graphical models from autoregressive Gaussian processes.
method Bayesian approach to estimate Kronecker graphical models.
result Effectiveness demonstrated through numerical experiments and real-world data application.
Estimates network causal effects considering contagion and latent confounding.
problem Determining if correlations in network studies are due to contagion or latent confounding.
method Segregated graph representation, likelihood ratio tests, network causal effect estimation strategies.
result Proposes methods to estimate network causal effects under full interference scenarios.
Hybrid model combines graphical and learned inference for better data estimation.
problem Suboptimal estimation due to poor graphical model approximation of complex data generating process.
method Combines graphical inference with a learned inverse model structured as a graph neural network and formulated as a recurrent neural network.
result Hybrid model estimates chaotic trajectory more accurately than graphical or learned inference alone.
Graphical notation simplifies tensor operations and decompositions.
problem Complex tensor operations are difficult to understand and represent.
method Introduces graphical notation to represent tensor operations.
result Simplified representation of tensor operations and decompositions.
Chainer accelerates deep learning research with a flexible framework.
problem Slow development and application of deep learning models.
method Flexible, intuitive, high-performance framework with GPU acceleration and dynamic models.
result Accelerates deep learning research and development.
For large matrix factorisation problems, we develop a distributed Markov Chain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics (SGLD) that we call Parallel SGLD (PSGLD). PSGLD has very favourable scaling properties with increasing data size and is comparable in terms of computational requiremen…
The thesis tackles structure learning and parameter estimation for PGMs using penalized maximum likelihood methods.
problem Recovering the true structure of probabilistic graphical models (PGMs) for decision-making and interpretation.
method Penalized maximum likelihood estimation with the LASSO penalty.
result The approach successfully recovers the true structure of PGMs, including Bayesian networks and continuous time Bayesian networks, for both complete and incomplete data.
Hierarchical Causal Modelsstat.ME
Hierarchical causal models help understand cause and effect in nested data.
problem Learning cause and effect from nested hierarchical data.
method Extend structural causal models and causal graphical models with inner plates, develop graphical identification technique and estimation methods.
result Hierarchical data can enable causal identification even when non-hierarchical data cannot.
Motivated by Ilmanen's correspondence, we present an explicit solution to the prescribed Hoffman-Osserman Gauss map problem for non-minimal translators to the mean curvature flow in Euclidean 4-space. We propose a conjecture on the non-existence of Jenkins-Serrin type unit-speed graphical translators.
GP3 framework efficiently analyzes Gaussian processes on GPUs.
problem Certifiable safety in machine learning applications.
method GP3 framework using interval analysis and multi-resolution sampling on GPUs.
result Efficient analysis of Gaussian processes with certifiable safety.
Paper estimates non-causal graphical models using covariance extension and transportation distance.
problem Estimating non-causal graphical models with smoothing relations.
method Proposes a covariance extension problem and uses transportation distance to minimize error with white noise.
result Solution is a double-sided autoregressive non-causal graphical model.
We characterize the sample size required for accurate graphical model selection from non-stationary samples. The observed data is modeled as a vector-valued zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This model contains as special cases the standard setting …
Modeling and Estimation of Discrete-Time Reciprocal Processes via Probabilistic Graphical Modelsstat.ML
Reciprocal processes are acausal generalizations of Markov processes introduced by Bernstein in 1932. In the literature, a significant amount of attention has been focused on developing dynamical models for reciprocal processes. In this paper, we provide a probabilistic graphical model for reciprocal processes. This le…
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.
Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Datastat.ML
BCAE-2D compresses 3D data from a time projection chamber at high speed.
problem Compressing high-speed, sparse 3D data from a time projection chamber.
method 2D Bicephalous Convolutional Autoencoder (BCAE-2D) approach.
result 3x speedup in compression throughput with improved reconstruction accuracy.
Introduces a new model for directed relationships in Gaussian data.
problem Learning directed relationships in Gaussian data.
method Developed a new directed graphical model (GGIM) from Gaussian data, leveraging stationary Gaussian processes on graphs.
result GGIMs can be framed as a LASSO problem and have a bound on the difference from the -norm penalized maximum log-likelihood estimate.
End-to-end speech recognition system trained on GPUs and CPUs.
problem Building state-of-the-art speech recognition systems.
method Utilizes CPUs and GPUs for training, data augmentation, and neural network updates. Uses vocal tract length perturbation and acoustic simulator for data augmentation. Employed Horovod allreduce for training.
result Achieved 7.92% WER on proprietary English Bixby open domain test set using a Bidirectional Full Attention (BFA) model.
Principal component analysis (PCA) is a statistical technique commonly used in multivariate data analysis. However, PCA can be difficult to interpret and explain since the principal components (PCs) are linear combinations of the original variables. Sparse PCA (SPCA) aims to balance statistical fidelity and interpretab…
Graphical translating solitons for the inverse mean curvature flow and isoparametric functionsmath.DG
The paper classifies shapes of translating solitons for a specific flow.
problem Understanding the shapes of translating solitons in a specific flow.
method Analyzing functions on a unit sphere and solving an ODE.
result Classification of the shapes of translating solitons.
A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attributionstat.ME
New framework for attributing online marketing touchpoints.
problem Fine-grained attribution of individual touchpoints' effects.
method Graphical point process framework for studying conversion effects.
result Proposed methods allocate proper credit to touchpoints for each customer's path.
Graphical models for covariance matrices improve structure learning.
problem Learning structure in graphical models for covariance matrices.
method Structural learning via -penalized loss minimization.
result Method outperforms alternatives in simulations and real-world applications.
Memory-efficient learning for large-scale imaging systems.
problem Memory limitations in GPUs for real-world large-scale inverse problems.
method Exploits reversibility of network layers to enable data-driven design.
result Demonstrated on small-scale and large-scale real-world systems.
Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. A non-parametric Bayesian approach in the form o…
New method reduces sample complexity for learning Ising model dynamics exponentially.
problem Learning binary graphical models from correlated samples produced by a dynamical process.
method Two estimators based on interaction screening objective and conditional likelihood loss.
result Sample complexity reduces exponentially for samples from a dynamical process far from equilibrium.
Probabilistic graphical models combine the graph theory and probability theory to give a multivariate statistical modeling. They provide a unified description of uncertainty using probability and complexity using the graphical model. Especially, graphical models provide the following several useful properties: - Graphi…
Deep learning improves gamma-ray energy estimation and event selection.
problem Improving gamma-ray event selection and energy estimation.
method Adapted convolutional neural networks (CNN) for gamma-ray astronomy.
result Significant improvement in gamma-ray energy estimation and event selection.
AGM uses adversarial approach for robust prediction in structured prediction problems.
problem Structured prediction problems with complex relationships between variables.
method Adversarial Graphical Models (AGM) for distributionally robust prediction.
result AGM achieves Fisher consistency and flexibility in loss metrics.
Surveying joint Gaussian graphical models to identify shared structures across domains.
problem Estimating shared structures across different data sources.
method Statistical inference of joint Gaussian graphical models.
result Improved estimation power for high-dimensional data.
Bayesian nonparametric approach for clustering non-exchangeable groups.
problem Clustering grouped data with dependencies among groups.
method Graphical Dirichlet process modeling with Markov property.
result Efficient posterior inference algorithm developed.