Temporal Convolutional Networks


Temporal convolutional networks (TCNs) are deep learning models that use 1D convolutions for sequence modeling tasks.

Graph Learning for Cooperative Cell-Free ISAC Systems: From Optimization to Estimation

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Jul 09, 2025
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Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat Transport

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Jul 08, 2025
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Variational Graph Convolutional Neural Networks

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Jul 02, 2025
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mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling

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Jul 02, 2025
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USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network

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Jul 03, 2025
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Towards Foundation Auto-Encoders for Time-Series Anomaly Detection

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Jul 02, 2025
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Age Sensitive Hippocampal Functional Connectivity: New Insights from 3D CNNs and Saliency Mapping

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Jul 02, 2025
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AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification

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Jun 26, 2025
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Improved Image Reconstruction and Diffusion Parameter Estimation Using a Temporal Convolutional Network Model of Gradient Trajectory Errors

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Jun 17, 2025
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Dense Video Captioning using Graph-based Sentence Summarization

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Jun 25, 2025
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