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Found 7 papers, 1 papers with code
FreDF: Learning to Forecast in Frequency Domain
Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences.
Coexistence of Mobile Broadband IMT Systems and UWB Keyless Entry Systems above 6.5 GHz
The ever-increasing need for spectrum for mobile broadband systems has led to the recent allocation of spectral resources for International Mobile Telecommunication (IMT) services in the upper mid band (6. 425 - 7. 125 GHz) at the World Radio Conference (WRC-23) as well as to the creation of an agenda item on identifying future IMT bands in the frequency region 7. 125 - 10. 5 GHz at WRC-27.
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data
Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios.
TMoE-P: Towards the Pareto Optimum for Multivariate Soft Sensors
We further present a Task-aware Mixture-of-Experts framework for achieving the Pareto optimum (TMoE-P) in multi-variate soft sensor, which consists of a stacked OMoE module and a POR module.
Directed Acyclic Graphs With Tears
The basis of Bayesian network is structure learning which learns a directed acyclic graph (DAG) from data.
Entire Space Counterfactual Learning: Tuning, Analytical Properties and Industrial Applications
As a basic research problem for building effective recommender systems, post-click conversion rate (CVR) estimation has long been plagued by sample selection bias and data sparsity issues.
ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion.
