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Found 7 papers, 1 papers with code
Machine Learning Techniques for Predicting the Short-Term Outcome of Resective Surgery in Lesional-Drug Resistance Epilepsy
In this study, we developed and tested machine learning models to predict epilepsy surgical outcome using noninvasive clinical and demographic data from patients.
RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm
We initialize the policy weights with the improved ABC algorithm.
Ranked #1 on
Answer Selection
on TrecQA
Semi-supervised Vector-Quantization in Visual SLAM using HGCN
In this paper, two semi-supervised appearance based loop closure detection technique, HGCN-FABMAP and HGCN-BoW are introduced.
Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System
The image recognition and neural network models were simulated and implemented using Xilinx simulators for the Xilinx ZCU102 board.
Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data
We experiment graph-based Semi-Supervised Learning (SSL) of Conditional Random Fields (CRF) for the application of Spoken Language Understanding (SLU) on unaligned data.
Dimensionality reduction based on Distance Preservation to Local Mean (DPLM) for SPD matrices and its application in BCI
In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of Symmetric Positive Definite (SPD) matrices that considers the geometry of SPD matrices and provides a low dimensional representation of the manifold with high class discrimination.
A Framework for learning multi-agent dynamic formation strategy in real-time applications
Due to distributed and complex nature of many multi-agent systems, it is impossible to include all features directly in the learning process; thus, a modular scheme is proposed in order to reduce the number of features.
