AIMC Topic: Space Simulation
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Deep Neural Network-Embedded Stochastic Nonlinear State-Space Models and Their Applications to Process Monitoring.
IEEE transactions on neural networks and learning systems
Nov 30, 2022
Process complexities are characterized by strong nonlinearities, dynamics, and uncertainties. Monitoring such a complex process requires a high-quality model describing the corresponding nonlinear dynamic behavior. The proposed model is constructed u...
Two-Stage Bayesian Optimization for Scalable Inference in State-Space Models.
IEEE transactions on neural networks and learning systems
Oct 5, 2022
State-space models (SSMs) are a rich class of dynamical models with a wide range of applications in economics, healthcare, computational biology, robotics, and more. Proper analysis, control, learning, and decision-making in dynamical systems modeled...
Extracting Chinese events with a joint label space model.
PloS one
Sep 27, 2022
The task of event extraction consists of three subtasks namely entity recognition, trigger identification and argument role classification. Recent work tackles these subtasks jointly with the method of multi-task learning for better extraction perfor...
Parallelograms revisited: Exploring the limitations of vector space models for simple analogies.
Cognition
Aug 31, 2020
Classic psychological theories have demonstrated the power and limitations of spatial representations, providing geometric tools for reasoning about the similarity of objects and showing that human intuitions sometimes violate the constraints of geom...
Achieving bioinspired flapping wing hovering flight solutions on Mars via wing scaling.
Bioinspiration & biomimetics
Jun 26, 2018
Achieving atmospheric flight on Mars is challenging due to the low density of the Martian atmosphere. Aerodynamic forces are proportional to the atmospheric density, which limits the use of conventional aircraft designs on Mars. Here, we show using n...
Encoding sequential information in semantic space models: comparing holographic reduced representation and random permutation.
Computational intelligence and neuroscience
Apr 7, 2015
Circular convolution and random permutation have each been proposed as neurally plausible binding operators capable of encoding sequential information in semantic memory. We perform several controlled comparisons of circular convolution and random pe...
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