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Found 12 papers, 1 papers with code
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph
As far as we know, by applying abstract algebra in statistical learning, this work develops the first formal language for general knowledge graphs, and also sheds light on the problem of neural-symbolic integration from an algebraic perspective.
Dense Keypoints via Multiview Supervision
(2) Geometric consistency: every point in the continuous correspondence fields must satisfy the multiview consistency collectively.
Semi-supervised Dense Keypoints Using Unlabeled Multiview Images
(2) Geometric consistency: every point in the continuous correspondence fields must satisfy the multiview consistency collectively.
Variance Regularization for Accelerating Stochastic Optimization
While nowadays most gradient-based optimization methods focus on exploring the high-dimensional geometric features, the random error accumulated in a stochastic version of any algorithm implementation has not been stressed yet.
A Deep Learning Approach for COVID-19 Trend Prediction
In this work, we developed a deep learning model-based approach to forecast the spreading trend of SARS-CoV-2 in the United States.
End-to-End Camera Calibration for Broadcast Videos
We evaluate our method on a new college basketball dataset and demonstrate state of the art performance in variable and dynamic environments.
NagE: Non-Abelian Group Embedding for Knowledge Graphs
We demonstrated the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential for designing embedding models.
Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks
Deep neural networks (DNNs) can be huge in size, requiring a considerable a mount of energy and computational resources to operate, which limits their applications in numerous scenarios.
Improved Structural Discovery and Representation Learning of Multi-Agent Data
Central to all machine learning algorithms is data representation.
A Group-Theoretic Framework for Knowledge Graph Embedding
We have rigorously proved the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential for model design.
Context Dependent Modulation of Activation Function
This modification produces statistically significant improvements in comparison with traditional ANN nodes in the context of Convolutional Neural Networks and Long Short-Term Memory networks.
Generating Multi-Agent Trajectories using Programmatic Weak Supervision
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay.
