A CNN-based receiver detects packet boundaries in asynchronous grant-free C2C communications and achieves low end-to-end packet loss via LDPC soft information and successive interference cancellation.
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4 Pith papers cite this work. Polarity classification is still indexing.
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D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.
A simulation-to-real navigation policy enables a quadrotor to locate an odor source using only basic olfaction sensors and optional vision, validated in indoor real-world flights.
Activation maximization applied to a speech command DNN, followed by WaveNet synthesis, produces class-specific utterances that human evaluators can interpret, supporting its use for model debugging.
citing papers explorer
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A Deep Learning-based Receiver for Asynchronous Grant-Free Random Access in Control-to-Control Networks
A CNN-based receiver detects packet boundaries in asynchronous grant-free C2C communications and achieves low end-to-end packet loss via LDPC soft information and successive interference cancellation.
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D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks
D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.
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Chasing Ghosts: A Simulation-to-Real Olfactory Navigation Stack with Optional Vision Augmentation
A simulation-to-real navigation policy enables a quadrotor to locate an odor source using only basic olfaction sensors and optional vision, validated in indoor real-world flights.
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Towards Debugging Deep Neural Networks by Generating Speech Utterances
Activation maximization applied to a speech command DNN, followed by WaveNet synthesis, produces class-specific utterances that human evaluators can interpret, supporting its use for model debugging.