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Found 6 papers, 0 papers with code
Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles
Motivated by the promising advances of deep-reinforcement learning (DRL) applied to cooperative multi-agent systems we propose a model and learning procedure to solve the Capacitated Multi-Vehicle Routing Problem (CMVRP) with fixed fleet size.
A fast multi-object tracking system using an object detector ensemble
Multiple-Object Tracking (MOT) is of crucial importance for applications such as retail video analytics and video surveillance.
Learning from multivariate discrete sequential data using a restricted Boltzmann machine model
A restricted Boltzmann machine (RBM) is a generative neural-network model with many novel applications such as collaborative filtering and acoustic modeling.
Collaborative Filtering using Denoising Auto-Encoders for Market Basket Data
The contribution of this work is twofold: (1) to identify missing items in observed baskets and, thus, directly providing a CF model; and, (2) to construct a generative model of baskets which may be used, for instance, in simulation analysis or as part of a more complex analytical method.
A Probabilistic Adaptive Search System for Exploring the Face Space
In this paper we propose a novel evolutionary approach for searching the face space that can be used as a facial composite system.
A Dynamic Bayesian Network Model for Inventory Level Estimation in Retail Marketing
Many retailers today employ inventory management systems based on Re-Order Point Policies, most of which rely on the assumption that all decreases in product inventory levels result from product sales.
