Quantum computers can speed up machine learning optimization problems.
problem Long computation times and high resource requirements for classical optimization algorithms in machine learning.
method Developed a mathematical model to leverage quantum parallelism for machine learning.
result Quantum machine learning applied to a 3D time-varying image demonstrated significant speedup.
Paper proposes KSHMM for short-term wind-speed forecasting.
problem Short-term wind-speed prediction challenge.
method Kernel Spectral Hidden Markov Model (KSHMM) for time series forecasting.
result KSHMM-based technique offers comparable or better performance than other methods.
This study compares Matlab and OpenCV for machine learning algorithms.
problem Comparing execution speeds of Matlab and OpenCV for machine learning.
method 20 real datasets, 20 different machine learning algorithms.
result OpenCV is significantly faster than Matlab in execution.
New method speeds up nuclear-norm constrained learning over multiple machines.
problem Synchronization slowdown and high communication costs in large-scale learning.
method Asynchronous Stochastic Frank-Wolfe (SFW-asyn) method.
result SFW-asyn achieves the same convergence rate as vanilla SFW but with speed-ups almost linear to the number of machines.
The study corrects misconceptions in GBDT speed benchmarks.
problem Misleading speed benchmarks of GBDT algorithms.
method Explained and criticized several straightforward benchmarking methods, outlined fair benchmark requirements.
result A fair GBDT speed benchmark requires specific conditions.
Improved wind speed forecasts for power generation using machine learning.
problem Improving the accuracy and reliability of wind speed predictions for power generation.
method A novel machine learning approach for calibrating wind speed ensemble forecasts.
result The proposed method improves the calibration and accuracy of probabilistic and point forecasts.
Meta-learning speeds up learning new tasks.
problem Designing and improving machine learning pipelines.
method Observing and learning from different machine learning approaches.
result Learning new tasks much faster than traditional methods.
ELM speeds up financial machine learning tasks.
problem Efficiently solving time-sensitive financial tasks with machine learning.
method Single-layer neural networks with random initialization and convex optimization.
result ELM achieves significant computational efficiency in financial applications.
Helix speeds up iterative ML development by optimizing workflow execution.
problem Inefficient manual tuning of ML workflows.
method Declarative system that optimizes workflow execution end-to-end and across iterations, minimizing runtime per iteration.
result Up to an order of magnitude reduction in cumulative run time compared to state-of-the-art tools.
Study short-term wind power and speed predictions using machine learning.
problem Accurate short-term wind power and speed predictions for energy systems.
method Combining numerical weather prediction models with local observations, using machine learning for variable selection and forecasting.
result Improved wind power and speed predictions for 4-hour ahead using machine learning.
Machine learning improves earnings forecasting accuracy and speed.
problem Improving earnings forecasting accuracy and speed.
method Adopting machine learning models for earnings forecasting.
result Machine learning model outperforms traditional models in accuracy and speed.
This paper studies parallelization schemes for stochastic Vector Quantization algorithms in order to obtain time speed-ups using distributed resources. We show that the most intuitive parallelization scheme does not lead to better performances than the sequential algorithm. Another distributed scheme is therefore intro…
Paper proposes a method to identify wind hazard types and predict extreme wind speeds.
problem Difficulty in identifying wind hazard types from meteorological data records.
method Numerical pattern recognition method with feature extraction and generalization.
result Algorithm performance validated using K-fold cross-validation and real-world data.
Machine learning speeds up FLIM analysis in biomedical research.
problem Complex, slow, and computationally expensive FLIM analysis.
method Machine learning techniques for faster and smarter FLIM data extraction and interpretation.
result Higher accuracy in classifying and segmenting FLIM images compared to conventional methods.
Network embedding helps predict speed limits on incomplete Danish road network.
problem Incomplete speed limit data on Danish roads limits machine learning applications.
method Applied node2vec network embedding to Danish road network.
result Network embedding can derive useful features for predicting speed limits.
Accelerates coordinate descent methods for machine learning problems.
problem Slowness of coordinate descent methods in machine learning.
method Extrapolation-based accelerated coordinate descent.
result Significant speed-up in practice compared to existing methods.
Quantum LS-SVM simplifies matrix inversion for faster machine learning.
problem Speeding up machine learning algorithms for large datasets.
method Introduces a novel quantum algorithm using continuous variables to simplify matrix inversion in LS-SVM, and proposes a hybrid quantum-classical approach for sparse solutions.
result Quantum LS-SVM achieves exponential speed-up and can solve classically difficult tasks.
Paper analyzes learning from ghost imaging without reconstruction bottleneck.
problem High-speed cell classification bottleneck in ghost cytometry.
method Theoretical analysis of learning from ghost imaging without reconstruction.
result Theoretical analysis supports learning from ghost imaging without reconstruction.
GPU acceleration speeds up financial machine learning training time.
problem Time-intensive classifier training in financial machine learning.
method Deployed NVIDIA GPUs for parallel high-speed arithmetic operations.
result Significantly faster training time achieved.
Improves kernel machine training and execution speed with RSKPCA.
problem Speeding up kernel manifold learning algorithms.
method Reduced Set KPCA (RSKPCA) method for spectral decomposition.
result Improves training and evaluation time of KPCA by up to an order of magnitude.
Quantum SVM clustering speeds up big data analysis.
problem Performance degradation of classical SVM clustering on big data.
method Developed a quantum version of SVM clustering using quantum support vector machine and kernels.
result Significant speed-up gain on run-time complexity.
Optimizes PCB stack-up design for high-speed circuits.
problem Efficiently optimize many parameters in PCB stack-up design.
method Parallel and intelligent Bayesian optimization for stripline design.
result Improves accuracy and efficiency of PCB stack-up optimization.
The paper uses deep learning to speed up spatial and visual connectivity analysis.
problem Slow calculation of spatial and visual connectivity metrics.
method Investigates machine learning models and a pipeline for training them on spatial and visual connectivity analysis.
result Deep learning models significantly speed up the analysis process.
Poseidon optimizes deep learning training on GPU clusters by reducing network communication.
problem Substantial parameter synchronization over the network in distributed DL implementations.
method Overlap communication and computation, use a hybrid communication scheme.
result Achieves significant speed-ups in DL training on GPU clusters.
Dual SVM training with budget constraint for faster accuracy.
problem Efficient support vector machine training with limited resources.
method Dual subspace ascent algorithm with budget constraint.
result Significant speed-up over primal budget training methods.
Simulation of high-speed train aerodynamics using RANS and machine learning.
problem Aerodynamic analysis of high-speed trains under turbulent flow conditions.
method RANS equations with turbulence model, machine learning (GEP, GPR, RF) for predictions.
result Random Forest (RF) provides the most accurate predictions for aerodynamic coefficients.
Hybrid model speeds up galaxy simulations by incorporating baryonic properties.
problem Inaccurate baryonic properties in dark matter-only simulations.
method Combining analytic models and machine learning for faster, more accurate simulations.
result Hybrid model outperforms machine learning alone for some baryonic properties.
Machine learning speeds up search procedures for sorted tables.
problem Improving the speed of sorted table search procedures.
method Systematic experimental comparison of efficient implementations with learned counterparts.
result Learned data structures can significantly speed up search procedures.
Non-autoregressive model speeds up sequence generation tasks.
problem Efficiency in sequence generation tasks.
method Iterative refinement based on latent variable models and denoising autoencoders.
result Significant speedup in decoding with comparable quality.
Quantum machine learning aims to speed up classical algorithms.
problem Speeding up classical machine learning algorithms using quantum computing.
method Review and discussion of quantum machine learning techniques.
result Quantum algorithms offer advantages for certain learning problems.
New teaching method for iterative learners, reducing examples and speeding up convergence.
problem Teaching iterative machine learners more efficiently.
method Iterative machine teaching with sequential, intelligent example feeding.
result Teaching complexity differs significantly from batch teaching, focusing on fast learner convergence.
Two algorithms improve K-means clustering speed without sacrificing quality.
problem Improving clustering quality of K-means while speeding up the process.
method Divisive K-means and Parallel Two-Phase K-means.
result Achieved empirically global optimum clustering results with lower complexity.
New method speeds up training of large kernel models.
problem Scaling kernel machines to large datasets and model sizes.
method Delayed projections in Preconditioned Stochastic Gradient Descent (PSGD).
result Significant training speed up over existing methods.
Quantum method speeds up VB estimation in machine learning.
problem Prohibitively expensive natural gradient in high dimensions.
method Regression-based natural gradient estimation with quantum matrix inversion.
result Quantum method enables efficient VB estimation.
Improves SVM speed by 2 orders of magnitude for 12 out of 17 datasets.
problem Slowness of kernel classifiers like SVM for large problems.
method Piecewise linear classifier trained from kernel-based classifier.
result Improves classification speed by up to 2 orders of magnitude.
Machine learning speeds up quantum chemical calculations of excited states.
problem Accurate quantum chemical calculations of excited states are computationally expensive.
method Employing machine learning to speed up and advance excited-state simulations in various fields.
result Machine learning techniques can significantly reduce computational time for excited-state simulations.
sGBM speeds up gradient boosting by parallelizing and adapting base learners.
problem Infeasibility of parallelizing GBM training and sub-optimal performance in online settings.
method Integrates multiple differentiable base learners, jointly optimizing them with linear speed-up.
result sGBM achieves higher time efficiency and better accuracy than traditional GBM.
Efficiently merges multiple points to speed up BSGD SVM training.
problem Costly merging of points in BSGD SVM training.
method Merges more than two points at once to reduce training time.
result Significant speed-ups achieved without loss of accuracy.
A new method uses CML to speed up social signal annotation.
problem Manual annotation of social signals in large multi-modal corpora is time-consuming and exhausting.
method CML techniques to predict local parts first, followed by a session-independent classification model.
result The method has the potential to significantly reduce human labelling efforts.
New trends explore quantum machine learning to speed up computations and analyze data.
problem Speeding up machine learning computations and analyzing large quantum data.
method Interplay between quantum physics and machine learning, including new algorithms and hardware.
result Breakthroughs in quantum machine learning can provide advantages over classical methods.
Researchers measure distances between quantum states to speed up machine learning.
problem Calculating distances between quantum states for machine learning is complex.
method Three-step method using many-particle interference to measure Hilbert-Schmidt distance.
result The method reduces complexity in calculating Euclidean distances between quantum states.
TAPAS speeds up encrypted machine learning predictions.
problem Data privacy and computation time in encrypted machine learning.
method Combining binarization, sparsification, and homomorphic encryption techniques.
result Significantly reduces computation time for encrypted predictions.
This paper uses RNN to speed up high-speed channel simulation.
problem Computational intensity of generating eye diagrams in high-speed channels.
method Trains a recurrent neural network (RNN) to generate black-box macromodels.
result Reduces computation time significantly without complex simulations.
Generative Adversarial Active Learning improves learning speed by synthesizing queries.
problem Efficiently increase learning speed in machine learning models.
method Uses Generative Adversarial Networks (GAN) to adaptively synthesize training instances.
result The proposed algorithm outperforms traditional methods in some settings.
Machine learning accelerates Lie algebra computations.
problem Computing tensor products and branching rules of Lie algebras.
method Machine learning for Lie algebra computations.
result Achieves significant speed-ups in Lie algebra computations.
We optimize large Random Forests into faster, smaller decision diagrams.
problem Efficiency and size of large Random Forests.
method Aggregating large Random Forests into a single, semantically equivalent decision diagram.
result Significant speed-ups and reduction in data structure size.
Machine learning clusters mutations in cancer exomes, improving diagnostic speed and cost.
problem Extracting stable mutation structures from cancer exome data for early diagnostics.
method Statistically deterministic machine learning algorithm *K-means applied to exome samples.
result Majority of cancer types exhibit stable mutation clustering, while NMF methods are unstable.
This paper speeds up OCSSVM training using SMO.
problem Training One-Class Slab SVMs is slow.
method Uses updated SMO to divide large problems into smaller, analytically solvable subproblems.
result Training OCSSVMs scales better with large datasets.