This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centering results in a different but equivalent parameterization for artificial neural …
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48 results for “DBMS performance”
Paper proposes a new machine learning-based approach to improve DBMS performance.
problem Improving cardinality estimation for better query optimization.
method Adaptive cardinality estimation using query execution statistics.
result Significantly increases DBMS performance for some queries.
End-to-end training of DBMs with improved gradient estimation.
problem Biased gradient estimation in DBMs, especially with high-dimensional states.
method Unbiased contrastive divergence using MH coupling and local mode initialization.
result End-to-end training of DBMs without greedy pretraining, achieving FID score of 10.33 for MNIST.
Paper proposes an anomaly detection system for DBMS diagnosis.
problem Difficulty in detecting anomalies in DBMS due to increasing metrics.
method Uses deep autoencoder and statistical process control for anomaly detection, and time series similarity for event finding.
result Demonstrates effectiveness of the proposed model in detecting anomalies and finding related events.
Boltzmann machines as two-dimensional tensor networkscond-mat.stat-mech
RBM and DBM are represented as 2D tensor networks, revealing their expressive power and efficiency.
problem Understanding and optimizing RBM and DBM models.
method Representing RBM and DBM as 2D tensor networks and developing an efficient tensor network contraction algorithm.
result The proposed algorithm for computing partition functions is more accurate than state-of-the-art methods.
We present a layered Boltzmann machine (BM) that can better exploit the advantages of a distributed representation. It is widely believed that deep BMs (DBMs) have far greater representational power than its shallow counterpart, restricted Boltzmann machines (RBMs). However, this expectation on the supremacy of DBMs ov…
Inflationary flows use DBMs for accurate Bayesian inference.
problem Calibrated uncertainty quantification in Bayesian inference.
method Inflationary flows leverage DBMs to map data to a Gaussian latent space.
result Inflationary flows produce accurate, identifiable posterior distributions.
Design-by-Morphing creates radical airfoil designs without geometric constraints.
problem Design constraints limit airfoil design novelty and small changes.
method Design-by-Morphing (DbM) creates a search space without geometric constraints.
result DbM generates radical airfoils with remarkable lift-over-drag ratio and stall angle tolerance.
We introduce a new method for training deep Boltzmann machines jointly. Prior methods of training DBMs require an initial learning pass that trains the model greedily, one layer at a time, or do not perform well on classification tasks. In our approach, we train all layers of the DBM simultaneously, using a novel train…
DBMs model Fitbit usage patterns revealing two distinct weekly usage habits.
problem Challenges in modeling activity tracker data due to unlabeled data.
method Deep Boltzmann Machines (DBMs) for unsupervised learning of weekly usage patterns.
result Two distinct weekly usage patterns identified: frequent Monday-Tuesday use and consistent weekly use.
Proposes VPF for efficient training of DBMs without Gibbs sampling or feedback phases.
problem Efficient training of deep neural networks with biological plausibility.
method Variational Probability Flow (VPF) for binary Deep Boltzmann Machines (DBMs).
result VPF learns features quickly and generates high-likelihood samples.
Large-scale automated meta-analysis of neuroimaging data has recently established itself as an important tool in advancing our understanding of human brain function. This research has been pioneered by NeuroSynth, a database collecting both brain activation coordinates and associated text across a large cohort of neuro…
New model for community detection with side information improves recovery accuracy.
problem Community detection in networks with additional node data.
method Data Block Model (DBM) with Chernoff--TV divergence for threshold characterization and efficient algorithm.
result Sharp exact recovery threshold and efficient algorithm for DBM.
Multimodal learning with deep Boltzmann machines (DBMs) is an generative approach to fuse multimodal inputs, and can learn the shared representation via Contrastive Divergence (CD) for classification and information retrieval tasks. However, it is a 2-fan DBM model, and cannot effectively handle multiple prediction tas…
Deep learning detects anomalies in SAP HANA KPIs.
problem Detecting problems in high-dimensional KPIs from DBMSs.
method Two complementary DL approaches: temporal and spatial learning.
result Experimental results confirm the effectiveness of the system and models.
Deep learning approximates system moments from data.
problem Approximating moments of spatial probabilistic systems.
method Dynamic Boltzmann Distributions (DBDs) with deep Boltzmann machines (DBMs).
result Learned moment closures improve generalization over traditional methods.
We introduce a Deep Boltzmann Machine model suitable for modeling and extracting latent semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This parameter tying enables an efficient pretraining algorithm and a …
Generative models create artificial patient data for distributed analysis.
problem Privacy restrictions prevent pooling individual patient data for research.
method Deep Boltzmann machines (DBMs) and DataSHIELD software implementation.
result Patterns from real data can be recovered in artificial data sets.
Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function. Annealed importance sampling (AIS) is widely used to estimate MRF partition functions, and often yields quite accurate results. However, AIS is prone to ov…
The paper tackles backtest overfitting in cryptocurrency trading using deep reinforcement learning.
problem Backtest overfitting in deep reinforcement learning for cryptocurrency trading.
method Formulated hypothesis test for overfitting detection, trained agents, estimated overfitting probability, and rejected overfitted agents.
result Less overfitted deep reinforcement learning agents outperformed more overfitted agents and market benchmarks.
This paper surveys deep learning applications in machine health monitoring.
problem Data-driven machine health monitoring in modern manufacturing systems.
method Review of deep learning techniques and their applications in machine health monitoring.
result Deep learning provides useful tools for processing and analyzing machinery data.
Optimizes hydrokinetic turbine design using morphing and Bayesian optimization.
problem Designing optimal hydrokinetic turbine shapes due to high cost and geometric constraints.
method Design-by-Morphing (DbM) and Mixed variable, Multi-Objective Bayesian Optimization (MixMOBO).
result Optimized shapes lead to maximum power output with minimal evaluations.
New method to decompose portfolio performance ratios.
problem Understanding the drivers of portfolio performance ratios.
method Using Euler's theorem, decomposes performance ratios into modified ratios.
result Derives condition for new asset to improve portfolio performance.
Direct convolution eliminates memory overhead and improves performance.
problem Memory overhead and suboptimal performance in convolution layers.
method Implementing direct convolution without additional memory overhead.
result Performance improvement between 10% to 400% on various architectures.
This paper optimizes performative risk by focusing on convex properties and developing efficient algorithms.
problem Performative risk, the loss experienced by decision makers, is not optimized by stable models.
method Identifying convex properties of loss function and model-induced distribution shift, developing algorithms for optimization.
result Optimization of performative risk with better sample efficiency than generic methods.
Plug-in method improves performative prediction accuracy.
problem Learning under performative feedback with slow convergence rates.
method Plug-in performative optimization using models.
result Plug-in method can be superior to model-agnostic strategies.
New causal models perform poorly when evaluated on biased training sets.
problem Sample selection bias affects the evaluation of causal models' prediction performance.
method Re-evaluated prediction performance of causal models on a genetic perturbation data set, proposing a less-biased evaluation set.
result Causal models have similar or worse performance when evaluated on a less-biased set compared to standard association-based estimators.
A new framework for performative prediction robust to distributional misspecification.
problem Performative prediction models can be influenced by their own predictions, leading to suboptimal outcomes.
method Introduces distributionally robust performative prediction (DRPO) to approximate the true performative optimum (PO) robustly.
result DRPO provides provable guarantees as a robust approximation to the true PO when the nominal distribution map is misspecified.
Evaluation of Accounting and Market Performance: A Study on Listed Islamic Banks of Bangladeshq-fin.GN
Study compares Islamic banks' accounting and market performance.
problem Assessing the relationship between Islamic banks' accounting and market performance.
method Selected six Islamic banks, collected data from 2009-2013, used random-effect models.
result Superior accounting performance does not correlate with superior market performance.
Performance evaluation of predictive AI models to support medical decisions: Overview and guidancecs.LG
The study evaluates AI model performance measures for medical use.
problem Selecting appropriate performance measures for AI models in medical practice.
method Assessed 32 performance measures across five domains for binary outcomes.
result 17 measures are both proper and reflect decision-analytic performance.
SHIFT framework identifies subgroups with large ML model performance decay.
problem Large model performance decay in subgroups when deployed.
method Subgroup-scanning Hierarchical Inference Framework (SHIFT) for performance drift.
result SHIFT identifies interpretable subgroups with large performance decay and suggests targeted actions to mitigate it.
New framework for predicting decisions that influence their own outcomes.
problem Predictions that affect the outcomes they predict, leading to undesirable distribution shift.
method Risk minimization framework combining statistics, game theory, and causality.
result Necessary and sufficient conditions for retraining to converge to a performatively stable point of minimal loss.
Global constraints improve cognates detection performance.
problem Improving cognates detection accuracy.
method Rescoring of score matrices using global constraints.
result Significant performance improvements across various datasets.
The paper explores how machine learning models can be learnable despite label shifts.
problem Learnability of binary classification models in the presence of label shifts.
method Developed a performative empirical risk function that is an unbiased estimate of the true risk on the shifted distribution.
result PAC-learnable hypothesis spaces remain PAC-learnable for performative scenarios.
New approach tackles decision-making under predictions that shape outcomes.
problem Challenges in learning optimal decision rules when predictions influence outcomes.
method Introduces performative omniprediction, a predictor that encodes optimal decision rules for multiple objectives.
result Efficient performative omnipredictors exist under a natural restriction of outcome performativity.
This paper analyzes mobile device training of deep learning models.
problem Performance characterization of training deep learning models on mobile devices.
method Experiments on NVIDIA TX2, benchmark suite, and tools for performance analysis.
result Interesting performance problems and opportunities revealed.
This paper extends performative prediction to nonlinear cases.
problem Performative prediction's effectiveness is limited by linear assumptions in real-world applications.
method Formulated a maximum margin approach loss function and extended it to nonlinear spaces using kernel methods.
result Derived conditions for performative stability in both linear and nonlinear cases.
Learn2Evaluate uses learning curves to estimate high-dimensional prediction performance.
problem Estimating test performance in high-dimensional data settings is challenging.
method Learn2Evaluate uses learning curves to estimate test performance at the total sample size.
result Learn2Evaluate provides a lower confidence bound for performance estimation.
Machine learning predicts ship performance changes over time.
problem Estimating ship hydrodynamic performance over time.
method Machine learning methods (NL-PCR, NL-PLSR, probabilistic ANN) calibrated with in-service data.
result Probabilistic ANN model performs best in predicting ship performance changes.
New method improves consistency of reinforcement learning performance evaluations.
problem Inconsistent performance results in reinforcement learning due to flawed evaluation metrics.
method Proposes a new comprehensive evaluation methodology for reinforcement learning algorithms.
result Demonstrates improved reliability of performance measurements for reinforcement learning algorithms.
Survey of performative prediction, a machine learning setup causing distribution shifts.
problem Machine learning models causing shifts in the environment they predict.
method Classification of performative prediction settings based on distribution map information.
result Introduction of new solution concepts and theoretical analyses.
Proposes a Siamese NN for algorithm selection focusing on alike performing instances.
problem Lack of effective meta-features for algorithm selection via meta-learning.
method Siamese Neural Network architecture with 'Algorithm-Performance Personas' concept.
result Proposed metric outperforms standard performance metrics in training sample selection.
The paper explores conditions for predicting optimization performance.
problem Lack of formal theoretical guarantees linking prediction and optimization performance.
method Exploring conditions for asymptotic convergence and exact quantification of optimization performance.
result Explicit theoretical relationship between prediction and optimization performance.
MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
problem Challenges in generating diverse, high-performance designs with multiple metrics.
method MO-PaDGAN uses a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
result MO-PaDGAN expands the design space towards high-performance regions and generates new designs with high diversity and performances.
Proposes a new cross-validation method to estimate model performance.
problem The standard cross-validation method does not accurately estimate the performance of the recommended model.
method Develops a new random-effects model framework to improve naive cross-validation estimators.
result Proposed estimators outperform conventional and naive methods in estimating model performance.
Paper proposes GP-NAS-ensemble for fast neural architecture performance prediction.
problem Estimating neural network performance without training time-consuming evaluations.
method GP-NAS-ensemble framework using ensemble learning improvements.
result Ranked second in a NAS performance prediction challenge.
Deep learning speeds up design of organic photovoltaic structures.
problem Designing optimal organic photovoltaic structures is expensive and intractable.
method Introduced a CNN architecture as a fast surrogate for structure-property mapping and used it for robust microstructural design.
result Deep learning accelerates the design process for enhancing photovoltaic device performance.
Partially performative prediction studies how predictive models influence future data.
problem Distribution shift in predictive models due to endogenous and exogenous factors.
method Generalizing performative prediction to capture both endogenous and exogenous sources of distribution shift.
result Developed online analogues of performative stability and optimality for partially performative environments.