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Found 5 papers, 1 papers with code
Trap Attention: Monocular Depth Estimation With Manual Traps
Recently, some studies introduced multi-head attention (MHA) modules to perform long-range interaction, which have shown significant progress in regressing the depth maps. The main functions of MHA can be loosely summarized to capture long-distance information and report the attention map by the relationship between pixels.
Distributionally Robust Day-ahead Scheduling for Power-traffic Network under a Potential Game Framework
In order to ensure the individual optimality of the two networks in a unified framework in day-ahead power scheduling, a two-stage distributionally robust centralized optimization model is established to carry out the equilibrium of power-transportation coupled network.
Online Learning Based Risk-Averse Stochastic MPC of Constrained Linear Uncertain Systems
We propose a novel online learning based risk-averse stochastic MPC framework in which Conditional Value-at-Risk (CVaR) constraints on system states are required to hold for a family of distributions called an ambiguity set.
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming
This paper reviews recent advances in the field of optimization under uncertainty via a modern data lens, highlights key research challenges and promise of data-driven optimization that organically integrates machine learning and mathematical programming for decision-making under uncertainty, and identifies potential research opportunities.
Data-Driven Stochastic Robust Optimization: A General Computational Framework and Algorithm for Optimization under Uncertainty in the Big Data Era
A DDSRO framework is further proposed based on the data-driven uncertainty model through a bi-level optimization structure.
