而现在Lafferty做的东西好像很杂,semi-supervised learning, kernel learning,graphical models甚至manifold learning都有涉及,可能就是像武侠里一样只要学会了九阳神功,那么其它的武功就可以一窥而知其精髓了。这里面我最喜欢的是semi- supervised learning,因为随着要处理的数据越来越多,进行全部label过于困难,而完全unsupe...
UC Berkeley的统计系在强手如林的北美高校中一直是top3,这就足以证明其肯定是群星荟萃,而其中,Peter L. Bartlett是相当亮的一颗星。关于他的研究,我想可以从他的一本书里得到答案:Neural Network Learning: Theoretical Foundations。也就是说,他主要做的是Theoretical Foundations。基础理论虽然没有一些直接可面向应用...
issue of Nature Machine Intelligence, the studies resulted from collaborations between researchers at Intel Labs and Forschungszentrum Jülich, University of Aachen, University of Zürich and ETH Zürich, ZHAW Wädenswil, Accenture Labs, and Redwood Center for Theoret...
pytorch-CycleGAN-and-pix2pix:图像翻译 (Image-to-image), in PyTorch (horse2zebra, edges2cats) 贡献者:Jun-Yan Zhu, Ph.D at BerkeleyNo.14 Faiss [2629 stars] Faiss: 相似性搜索和稠密矢量聚类库 (efficient similarity search & clustering of dense vectors) 贡献者:Facebook ResearchNo...
UC Berkeleybreaks out the learning system of a machine learning algorithm into three main parts. A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce ...
T. Agrawal, “Optuna and AutoML,” in Hyperparameter Optimization in Machine Learning, Berkeley, CA: Apress, 2021, pp. 109–129. https://doi.org/10.1007/978-1-4842-6579-6_5. Raptis TP, Passarella A, Conti M. Data management in industry 4.0: State of the art and open challenges. IE...
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - nikhil-ghosh-berkeley/transformers
University of California, Berkeley, AI at UC Berkeley University of California, Berkeley, Appropriate Use of Generative AI Tools University of California, Irvine, Generative AI for Teaching and Learning University of California, Irvine, Statement on Generative AI Detection University of California, Los ...
Nicolo Fusi: It was a joint collaboration between Microsoft Research and the Broad Institute. At the Broad Institute, the lead investigator was John Doench, and Microsoft Research was Jennifer Listgarten, who’s now at Berkeley, and me. And basically, we got this data. We figured out the...
“No kidding, this was the best hour of learning or knowledge-sharing I’ve had in my years at the Firm. Chris’ expertise and context-setting was super-thought provoking and perfectly delivered. I was side-slacking teammates throughout the session to share insights and ideas. Very energizing...