Finally, in the last part, the author presents some applications of tensor network methods to equilibrium and out-of-equilibrium correlated quantum matter.\nThe book can be used for a graduate computational physics course. After successfully completing such a course, a student should be able to ...
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前言推荐一个网站 Tensors.net,包含了示例代码,适合像我一样纯萌新的入门。这篇个人笔记从纠缠态的出发引出MPS和Penrose graphical notation。建立了Tensor network这一概念后,便开始讨论SVD分解和Canonical fo…
Second, a concise and self-contained introduction to the tensor-network methods employed in the modern version of DMRG is given, thus allowing the reader to effortlessly cross the deep chasm between the two formulations of DMRG without having to explore the broad literature on tensor networks. We...
“looking” for three pixels in a row, f1 vertically and f2 horizontally . Assuming our input image is n × n, then the result of filtering with these two filters is an n × n × 2 tensor. Now we apply a tensor filter (hard to draw!) that “looks for” a combination of two ...
before training a regression network. There is a trade-off between getting a good sub-problem you're sure the network can solve, and spending the least amount of time plumbing the code to hook up the appropriate data. Creativity will help here. ...
So far GANs have been primarily applied to modelling natural images. They are now producingexcellent results in image generation tasks, generating images that are significantly sharper than those trained using other leading generative methods based on maximum likelihood training objectives. Here are some...
Here’s how to use Autoencoders to detect signals with anomalies in a few lines of… Piero Paialunga August 21, 2024 12 min read 3 AI Use Cases (That Are Not a Chatbot) Machine Learning Feature engineering, structuring unstructured data, and lead scoring ...
“dimension of the image stays the same”throughout the process, unlike GANs, where the latent tensor can be of different sizes. This can be an issue when generating high-quality images due to limited GPU memory. However, the authors of “Stable Diffusion” (“latent diffusion,” to be ...
Hardware-efficient ML methods:这部分研究的内容其实就比较偏向设计算法来高效结合硬件落地的话题,比如binary network,addernet,以及任意bit的量化,网络的重参数化(RepVGG, RepLKNet) 等。 all in all:都别急,以上这些topic我都会伴随这门课程的各个章节穿插对其进行深入讲解,希望大家能够一键三连啊 (收藏+点赞+关注...