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Found 5 papers, 2 papers with code
A GPU-Accelerated Moving-Horizon Algorithm for Training Deep Classification Trees on Large Datasets
Decision trees are essential yet NP-complete to train, prompting the widespread use of heuristic methods such as CART, which suffers from sub-optimal performance due to its greedy nature.
Multilingual Lexical Simplification via Paraphrase Generation
After feeding the input sentence into the encoder of paraphrase modeling, we generate the substitutes based on a novel decoding strategy that concentrates solely on the lexical variations of the complex word.
A Global Optimization Algorithm for K-Center Clustering of One Billion Samples
This paper presents a practical global optimization algorithm for the K-center clustering problem, which aims to select K samples as the cluster centers to minimize the maximum within-cluster distance.
Deep Learning-based Predictive Control of Battery Management for Frequency Regulation
The design procedure of the proposed scheme consists of two sequential processes: (1) the SL process, in which we first run a simulation with an MPC embedding a low-fidelity battery model to generate a training data set, and then, based on the generated data set, we optimize a DNN-approximated policy using SL algorithms; and (2) the RL process, in which we utilize RL algorithms to improve the performance of the DNN-approximated policy by balancing short-term economic incentives and long-term battery degradation.
Data ultrametricity and clusterability
The increasing needs of clustering massive datasets and the high cost of running clustering algorithms poses difficult problems for users.
