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Found 10 papers, 5 papers with code
Fast Inference of Tree Ensembles on ARM Devices
Second, we extend our implementation from ranking models to classification models such as Random Forests.
Shrub Ensembles for Online Classification
Among the most successful online learning methods are Decision Tree (DT) ensembles.
There is no Double-Descent in Random Forests
Last, we study the diversity of an ensemble as a tool the estimate its performance.
Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement
In this paper, we revisit ensemble pruning in the context of `modernly' trained Random Forests where trees are very large.
Providing Meaningful Data Summarizations Using Exemplar-based Clustering in Industry 4.0
Data summarizations are a valuable tool to derive knowledge from large data streams and have proven their usefulness in a great number of applications.
Bit Error Tolerance Metrics for Binarized Neural Networks
In this study, our objective is to investigate the internal changes in the NNs that bit flip training causes, with a focus on binarized NNs (BNNs).
GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar Clustering
The optimization of submodular functions constitutes a viable way to perform clustering.
Generalized Negative Correlation Learning for Deep Ensembling
Ensemble algorithms offer state of the art performance in many machine learning applications.
Very Fast Streaming Submodular Function Maximization
Data summarization has become a valuable tool in understanding even terabytes of data.
Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks
Finally, we explore the influence of a novel regularizer that optimizes with respect to this metric, with the aim of providing a configurable trade-off in accuracy and BET.
