Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p

allergy

SEMI

cauda-equina

Distributional Correlation–Aware Knowledge Distillation for?Stock Trading Volume Predictionel. We evaluate the framework on a real-world stock volume dataset with two different time window settings. Experiments demonstrate that our framework is superior to strong baseline models, compressing the model size by . while maintaining . prediction accuracy. The extensive analysis further reveal

Mundane

Glucocorticoids

拋物線

Uncertainty Awareness for?Predicting Noisy Stock Price Movementsstimation methods focus on model uncertainty, we transform the aleatoric uncertainty in financial markets to model uncertainty by removing samples with similar historical price trajectories and different future movements. The Bayesian neural network is then adopted to estimate the model uncertainty

Madrigal

壓迫

Risk-Aware Reinforcement Learning for?Multi-Period Portfolio Selectionn previous approaches, as it only requires training of a single agent for the full approximate risk-return Pareto front. Additionally, it is more stable in training and only requires per time step . risk estimations . of the policy. Such risk control per time step is a common regulatory requirement

嬉耍

Waypoint Generation in?Row-Based Crops with?Deep Learning and?Contrastive Clusteringect the points to a separable latent space. The proposed deep neural network can simultaneously predict the waypoint position and cluster assignment with two specialized heads in a single forward pass. The extensive experimentation on simulated and real-world images demonstrates that the proposed ap

Sarcoma