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Found 5 papers, 3 papers with code
Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
There are two levels of symbol grounding problems among the core challenges: the first is symbol assignment, i. e. mapping latent factors of neural visual generators to semantic-meaningful symbolic factors from the reasoning systems by learning from limited labeled data.
Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification
Additionally, a bottleneck Transformer is introduced to facilitate the fusion of the dual-stream information.
Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems
In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed.
Enhancing Population Persistence by a Protection Zone in a Reaction-Diffusion Model with Strong Allee Effect
Protecting endangered species has been an important issue in ecology.
Dynamical Systems
Learning Graph-Level Representations with Recurrent Neural Networks
In this work, we develop a new approach to learn graph-level representations, which includes a combination of unsupervised and supervised learning components.
