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Efforts to regulate AI safety are still a work in progress. Governments are mostly leaving it up to AI companies since the risks are poorly understood, and...
Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly show that many generic techniques or “tricks”, such as data augmentation, pre-training, knowledge distillation, and self-supervision, may greatly boost the performance of a few-shot learning method. Moreover, different works may employ different software platforms, backbone architectures and input image sizes, making fair comparisons difficult and practitioners struggle with reproducibility. To address these situat...
A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization - GitHub - continuousml/Awesome-Out-Of-Distribution-Detection: A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization
A deep dive into Transformer a neural network architecture that was introduced in the famous paper “attention is all you need” in 2017, its applications, impacts, challenges and future directions
How it started... Before tax reform (the “Tax Cuts and Jobs Act” (TCJA)), the United States employed a worldwide system of taxation, which was antiquated and uncompetitive. American companies were taxed here on their U.S. profits, taxed abroad on their foreign profits, and then taxed again when those foreign profits were brought back home. Thus, their foreign earnings risked double taxation. In contrast, virtually all foreign countries employed a territorial system of taxation – foreign companies paid taxes on their home country profits in their home country, and on their foreign profit...