Overview: Algorithm selection is an engineering decision: the wrong choice can freeze a system at scale, regardless of ...
We developed a novel algorithm to train robust decision tree based models (notably, Gradient Boosted Decision Tree). This repo contains our implementation under the XGBoost framework. We plan to merge ...
Abstract: In this paper, we propose a two-stage soft-decision decoding (SDD) algorithm for BCH codes. At the first stage, we search for test error patterns (TEPs ...
Abstract: In this study, we explore the efficiency of the Monte Carlo Tree Search (MCTS), a prominent decision-making algorithm renowned for its effectiveness in complex decision environments, ...
The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...
In 2016, an artificial intelligence program called AlphaGo from Google’s DeepMind AI lab made history by defeating a champion player of the board game Go. Now Demis Hassabis, DeepMind’s cofounder and ...
In 1998, I unintentionally created a racially biased artificial intelligence algorithm. There are lessons in that story that resonate even more strongly today. The systems often fail on women of color ...
Decision trees are useful for relatively small datasets that have a relatively simple underlying structure, and when the trained model must be easily interpretable, explains Dr. James McCaffrey of ...
Binary Classification Using a scikit Decision Tree Dr. James McCaffrey of Microsoft Research says decision trees are useful for relatively small datasets and when the trained model must be easily ...
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