Search Results for author:
Found 7 papers, 2 papers with code
Towards a General Framework for ML-based Self-tuning Databases
Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases.
SnapBoost: A Heterogeneous Boosting Machine
At each boosting iteration, their goal is to find the base hypothesis, selected from some base hypothesis class, that is closest to the Newton descent direction in a Euclidean sense.
Compiling Neural Networks for a Computational Memory Accelerator
Computational memory (CM) is a promising approach for accelerating inference on neural networks (NN) by using enhanced memories that, in addition to storing data, allow computations on them.
SySCD: A System-Aware Parallel Coordinate Descent Algorithm
In this paper we propose a novel parallel stochastic coordinate descent (SCD) algorithm with convergence guarantees that exhibits strong scalability.
Breadth-first, Depth-next Training of Random Forests
In this paper we analyze, evaluate, and improve the performance of training Random Forest (RF) models on modern CPU architectures.
Parallel training of linear models without compromising convergence
The combined set of optimizations result in a consistent bottom line speedup in convergence of up to 12x compared to the initial asynchronous parallel training algorithm and up to 42x, compared to state of the art implementations (scikit-learn and h2o) on a range of multi-core CPU architectures.
Snap ML: A Hierarchical Framework for Machine Learning
We describe a new software framework for fast training of generalized linear models.
