When ML models are deployed, data distributions evolving over time leads to a drop in performance. Our latest paper (theory and experiments) suggests we can use self-training on unlabeled data to maintain high performance https://arxiv.org/pdf/2002.11361.pdf
When ML models are deployed, data distributions evolving over time leads to a drop in performance. Our latest paper (theory and experiments) suggests we can use self-training on unlabeled data to maintain high performance https://arxiv.org/pdf/2002.11361.pdf
That growth environment will include rising inflation and interest rates. Those upward shifts naturally accompany healthy growth periods as the demand for resources, products and services rise. Importantly, the Federal Reserve has laid out the rationale for not interfering with that natural growth transition.It's not exactly a fad, but there is a widespread willingness to pay up for a growth story. Classic fundamental analysis takes a back seat. Even negative earnings are ignored. In fact, positive earnings seem to be a limiting measure, producing the question, "Is that all you've got?" The preference is a vision of untold riches when the exciting story plays out as expected.
A project of our size needs at least a few hundred million dollars per year to keep going,” Mr. Durov wrote in his public channel on Telegram late last year. “While doing that, we will remain independent and stay true to our values, redefining how a tech company should operate.
