Modern neural networks are known to give overconfident prediction for
ou...
Estimating the generalization performance is practically challenging on
...
Partial-label learning (PLL) is an important weakly supervised learning
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In the presence of noisy labels, designing robust loss functions is crit...
Deep neural networks usually perform poorly when the training dataset su...
Detecting out-of-distribution inputs is critical for safe deployment of
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Adversarial training, originally designed to resist test-time adversaria...
This paper studies weakly supervised domain adaptation(WSDA) problem, wh...
Learning with noisy labels is a practically challenging problem in weakl...
Portfolio management via reinforcement learning is at the forefront of
f...
Deep neural networks have been shown to easily overfit to biased trainin...
Terrestrial communication networks can provide high-speed and ultra-reli...
Due to the rapid development of Internet of Things (IoT), a massive numb...
Deep Learning with noisy labels is a practically challenging problem in
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Blockchain has emerged as a promising technology that can guarantee data...