A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. - catboost/catboost
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. cuDF is a GPU DataFrame library for loading, joining, aggregatin...
Based on this basic assumption of the algorithm, wedefine the design goal of the first stage to support the parameter scale below 100G. This can better adapt to the video memory of the A100 and store it on a single machine with multiple cards. The two-way bandwidth between GPU cards is ...
To prove termination, many existing methods intend to search for ranking functions, which map program states to certain values [26]. Show abstract Regular Abstractions for Array Systems 2024, Proceedings of the ACM on Programming Languages Optimization of kernel learning algorithm based on parallel ...
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. cuDF is a GPU DataFrame library for loading, joining, aggregatin...
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. cuDF is a GPU DataFrame library for loading, joining, aggregatin...
The deep learning algorithm takes in English text as input and uses an encoder-decoder model with an attention mechanism based on Google’s Transformer to translate the text to Chinese output. The model was trained using a simple self-designed entropy loss function and an Adam optimizer on ...
the number of the best biclusters to be returned by gMSR, X, and the Mean Squared Residue (MSR) value threshold. Then, the gMSR algorithm is composed by two phases, the first one is executed in CPU, while the second one is executed in GPU. Finally, the output is a ranking of the...
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. cuDF is a GPU DataFrame library for loading, joining, aggregatin...