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07/24/2023
Rethinking Data Distillation: Do Not Overlook Calibration
Neural networks trained on distilled data often produce over-confident o...
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07/19/2023
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
Rare categories abound in a number of real-world networks and play a piv...
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04/24/2023
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Biologically inspired Spiking Neural Networks (SNNs) have attracted sign...
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02/27/2023
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation
Gradients have been exploited in proposal distributions to accelerate th...
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02/18/2023
Calibrating the Rigged Lottery: Making All Tickets Reliable
Although sparse training has been successfully used in various resource-...
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01/09/2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
Despite impressive performance on a wide variety of tasks, deep neural n...
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11/30/2022
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
Over-parameterization of deep neural networks (DNNs) has shown high pred...
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05/02/2020
