New GPU algorithm boosts machine learning with larger datasets.
problem Limited GPU memory restricts training data size.
method Out-of-core GPU gradient boosting algorithm.
result Training larger datasets on GPUs without accuracy loss.
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11 results for “out-of-core”
Improved Bayesian network classifiers using HDPs for better parameter estimation.
problem Inaccurate parameter estimation in Bayesian network classifiers limits their performance.
method Hierarchical Dirichlet Processes (HDPs) for accurate parameter estimation.
result HDPs improve BNCs' performance, matching or outperforming Random Forest on categorical datasets.
biglasso solves memory and computation issues for lasso models on large data.
problem Memory and computational limitations in fitting lasso models to large datasets.
method Memory-mapped files, out-of-core computation, efficient feature screening rules.
result biglasso efficiently handles ultrahigh-dimensional, multi-gigabyte data sets.
We present RandomizedCCA, a randomized algorithm for computing canonical analysis, suitable for large datasets stored either out of core or on a distributed file system. Accurate results can be obtained in as few as two data passes, which is relevant for distributed processing frameworks in which iteration is expensive…
Efficient kernel methods for large datasets using GPU acceleration.
problem Handling large-scale nonparametric learning problems efficiently.
method Preconditioned gradient solver, GPU acceleration, parallelization, out-of-core linear algebra, numerical precision optimization.
result Dramatic speedups on datasets with billions of points, maintaining state-of-the-art performance.
FIt-SNE accelerates t-SNE for large datasets.
problem Slow computation of t-SNE for large datasets.
method Interpolation-based t-SNE (FIt-SNE) using FFT and oocPCA.
result Significant acceleration of t-SNE computation for large datasets.
We propose a new analytical approximation to the kernel that converges geometrically. The analytical approximation is derived with elementary methods and adapts to the input distribution for optimal convergence rate. Experiments show the new approximation leads to improved performance in image classification and …
Residual Networks are shown to be equivalent to boosting feature representation.
problem Improving feature representation in deep learning models.
method Proved ResNet's equivalence to Online Gradient Boosting and proposed decision tree residual modules.
result ResNet can achieve Online Gradient Boosting regret bounds through architectural changes.
Paper proposes deep learning models for k-NN classification.
problem Imbalanced datasets and complex feature vectors in classification.
method Sequence to sequence model and memory network models.
result Models outperform k-NN and other state-of-the-art models.
pomegranate simplifies probabilistic modeling in Python.
problem Complexity in probabilistic modeling algorithms.
method Abstracts away complexities, enabling simple code for complex features.
result pomegranate outperforms other implementations.
Stabilizes online learning by using weighted reservoir sampling.
problem Real-world deployment sensitivity to outliers causes low accuracy in final solutions.
method Weighted reservoir sampling to stabilize ensemble model without additional data passes.
result Risk of ensemble classifier is bounded with respect to the underlying online learning method's regret.