We will add these findings to Sec.4.2.

It has two parameters (k,): k which specifies the number of degrees of freedom (i.e., the number of X

For a better understanding of our method, we give the framework of CWAEE. We use the outputs of the one-vs-rest classifiers to detect known and unknown classes in unlabeled data. Then, the class-wise adaptive threshold is calculated with a two-component beta mixture model (BMM) which models the score distributions of known classes and unknown classes in an unsupervised way. The entire process is summarized in Figure 5. Figure 5: The process of detecting known and unknown classes. For Domain Generalization, it is important to exploit the inter-domain information which includes domain-dependent styles and domain-invariant semantics.

We propose the Quantization Model of neural scaling laws, explaining both the observed power law dropoff of loss with model and data size, and also the sudden emergence of new capabilities with scale. We derive this model from what we call the Quantization Hypothesis, where network knowledge and skills are "quantized" into discrete chunks (quanta). We show that when quanta are learned in order of decreasing use frequency, then a power law in use frequencies explains observed power law scaling of loss.

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