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Found 7 papers, 1 papers with code
Revolutionizing Disease Diagnosis with simultaneous functional PET/MR and Deeply Integrated Brain Metabolic, Hemodynamic, and Perfusion Networks
To fully exploit the inherent complex and nonlinear relation among modalities while producing fine-grained representations for uni-modal inference, we subsequently add a modal alignment module to line up a dominant modality (e. g., PET) with representations of auxiliary modalities (MR).
PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction
In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors.
A Robbins--Monro Sequence That Can Exploit Prior Information For Faster Convergence
We propose a new method to improve the convergence speed of the Robbins-Monro algorithm by introducing prior information about the target point into the Robbins-Monro iteration.
A Social-aware Gaussian Pre-trained Model for Effective Cold-start Recommendation
We propose a novel recommendation model, the Social-aware Gaussian Pre-trained model (SGP), which encodes the user social relations and interaction data at the pre-training stage in a Graph Neural Network (GNN).
Graph Neural Pre-training for Enhancing Recommendations using Side Information
For the GCN-P model, two single-relational graphs are constructed from all the users' and items' side information respectively, to pre-train entity representations by using the Graph Convolutional Networks.
An optimized Capsule-LSTM model for facial expression recognition with video sequences
To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed.
Facial Expression Recognition
Facial Expression Recognition (FER)
BPLF: A Bi-Parallel Linear Flow Model for Facial Expression Generation from Emotion Set Images
In this paper, a bi-parallel linear flow model for facial emotion generation from emotion set images is constructed, and a series of improvements have been made in terms of the expression ability of the model and the convergence speed in training.
