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Apache Submarine: A Unified Machine Learning Platform Made Simple
As machine learning is applied more widely, it is necessary to have a machine learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists to improve their productivity.
Boundary Control of Traffic Congestion Modeled as a Non-stationary Stochastic Process
The success of the proposed traffic boundary control is demonstrated by simulation of traffic congestion control in Center City Philadelphia.
Conservation-Based Modeling and Boundary Control of Congestion with an Application to Traffic Management in Center City Philadelphia
By classifying the streets as inlets, outlets, and interior nodes, the model predictive control (MPC) method is applied to alleviate the network traffic congestion by optimizing the traffic inflow and outflow across the boundary of the NOIR with consideration of the inner traffic dynamics as a stochastic process.
What can computational models learn from human selective attention? A review from an audiovisual crossmodal perspective
Selective attention plays an essential role in information acquisition and utilization from the environment.
Hybrid Low-order and Higher-order Graph Convolutional Networks
With higher-order neighborhood information of graph network, the accuracy of graph representation learning classification can be significantly improved.
Assessing the Contribution of Semantic Congruency to Multisensory Integration and Conflict Resolution
The efficient integration of multisensory observations is a key property of the brain that yields the robust interaction with the environment.
Expectation Learning for Adaptive Crossmodal Stimuli Association
The human brain is able to learn, generalize, and predict crossmodal stimuli.
PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System
Then we extend the model family to a variety of bayesian online models with increasing feature embedding capabilities, such as Sparse-MLP, FM-MLP and FFM-MLP.
