In some remarkable cases, people have survived after their core temperature has plummeted into the 50s. The human body needs to maintain the same internal body temperature or else many vital systems fall apart. Breakthroughs, discoveries, and DIY tips sent every weekday. Whether you prefer sweltering summers or frigid winters, significant temperature changes mean only one thing to your body: bad news. Humans are homeotherms, meaning that our core body temperature stays roughly constant.

Given that warmup is standard in the practitioner's toolkit, it is important to understand it deeply and

Graph self-supervised learning, as a powerful pre-training paradigm for Graph Neural Networks (GNNs) without labels, has received considerable attention. We have witnessed the success of graph self-supervised learning on pre-training the parameters of GNNs, leading many not to doubt that whether the learned GNNs parameters are all useful. In this paper, by presenting the experimental evidence and analysis, we surprisingly discover that the graph self-supervised learning models are highly redundant at both of neuron and layer levels, e.g., even randomly removing 51 .

One of the most popular optimization algorithms is Adam [Kingma and Ba, 2014].

Adam is one of the most popular optimization algorithms in deep learning.

Adam has been shown to outperform gradient descent on large language models by a larger margin than on other tasks, but it is unclear why. We show that a key factor in this performance gap is the heavy-tailed class imbalance found in language tasks.

The SRM-based back-propagation can be summarized using the relationship between the potentials as follows. Hyper-parameters used for loss landscape estimation (Section 3.4) and random spike-train matching Some of the hyper-parameters were not mentioned in the paper. Table A1: Hyper-parameters used for loss landscape estimation (Section 3.4) and random spike-train matching

Despite its success in practice, theoretical analyses of Adam are still limited.