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Found 7 papers, 5 papers with code
Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills
We propose a framework called decoupled meta-reinforcement learning (DCMRL), which (1) contrastively restricts the learning of task contexts through pulling in similar task contexts within the same task and pushing away different task contexts of different tasks, and (2) utilizes a Gaussian quantization variational autoencoder (GQ-VAE) for clustering the Gaussian distributions of the task contexts and skills respectively, and decoupling the exploration and learning processes of their spaces.
SegT: A Novel Separated Edge-guidance Transformer Network for Polyp Segmentation
To address the aforementioned issues, we propose a novel separated edge-guidance transformer (SegT) network that aims to build an effective polyp segmentation model.
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints
Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas.
Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation
Yet, previous works tend to encode multimodal and contextual relationships in a loosely-coupled manner, which may harm relationship modelling.
Big-Data Clustering: K-Means or K-Indicators?
The K-means algorithm is arguably the most popular data clustering method, commonly applied to processed datasets in some "feature spaces", as is in spectral clustering.
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Data-driven learning provides a solution to address these challenges.
Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
To increase the flexibility and scalability of deep neural networks for image reconstruction, a framework is proposed based on bandpass filtering.
