MINT
发表于 2025-3-23 10:29:21
Energy Consumption Forecasting Using a Stacked Nonparametric Bayesian Approachask are used in the prior and likelihood of the next level GP. We apply our model to a real-world dataset to forecast energy consumption in Australian households across several states. We compare intuitively appealing results against other commonly used machine learning techniques. Overall, the resu
thwart
发表于 2025-3-23 16:54:20
Reconstructing the Past: Applying Deep Learning to Reconstruct Pottery from Thousands Shardsovel 3D Convolutional Neural Networks and Skip-dense layers to achieve these objectives. Our model first processes a 3D point cloud data of each shard and predicts the shape of the pottery, which a shard possibly belongs to. We first apply Dynamic Graph CNN to effectively perform learning on 3D poin
scrutiny
发表于 2025-3-23 19:34:48
CrimeForecaster: Crime Prediction by Exploiting the Geographical Neighborhoods’ Spatiotemporal Depenpendencies at the same time. Empirical experiments on two real-world datasets showcase the effectiveness of CrimeForecaster, where CrimeForecaster outperforms the current state-of-the-art algorithm by up to 21%. We also collect and publish a ten-year crime dataset in Los Angeles for future use by th
大喘气
发表于 2025-3-24 00:46:01
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misshapen
发表于 2025-3-24 06:22:43
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Cupping
发表于 2025-3-24 06:31:28
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半身雕像
发表于 2025-3-24 13:22:27
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CEDE
发表于 2025-3-24 15:27:43
Deep Reinforcement Learning for Large-Scale Epidemic Controlmodel. Finally, we consider a large-scale problem, by conducting an experiment where we aim to learn a joint policy to control the districts in a community of 11 tightly coupled districts, for which no ground truth can be established. This experiment shows that deep reinforcement learning can be use
丰满有漂亮
发表于 2025-3-24 22:22:41
GLUECK: Growth Pattern Learning for Unsupervised Extraction of Cancer Kinetics) a novel, data-driven model based on a neural network capable of unsupervised learning of cancer growth curves. Employing mechanisms of competition, cooperation, and correlation in neural networks, GLUECK learns the temporal evolution of the input data along with the underlying distribution of the
功多汁水
发表于 2025-3-25 02:29:28
Automated Integration of Genomic Metadata with Sequence-to-Sequence Modelsexplicitly mentioned in the input text..We experiment with two types of seq2seq models: an LSTM with attention and a transformer (in particular GPT-2), noting that the latter outperforms both the former and also a multi-label classification approach based on a similar transformer architecture (RoBER