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Found 6 papers, 2 papers with code
Better Explain Transformers by Illuminating Important Information
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings.
CEIR: Concept-based Explainable Image Representation Learning
In modern machine learning, the trend of harnessing self-supervised learning to derive high-quality representations without label dependency has garnered significant attention.
A Spatial-channel-temporal-fused Attention for Spiking Neural Networks
Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing.
A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks
Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons.
Monocular Depth Estimation with Sharp Boundary
In order to mitigate the boundary blur problem, we focus on the above two impact factors.
KETG: A Knowledge Enhanced Text Generation Framework
Embedding logical knowledge information into text generation is a challenging NLP task.
