Source: SPICE Model Original Author: Ruo Ming The rapid adoption of Gallium Nitride (GaN) technology in 5G base stations, satellite communications, and other applications has raised the bar for transistor modeling. In particular, the latest version of ADS supports ASM-HEMT 101.4 and MVSG_CMC 3.2.0 GaN HEMT models. For device modeling engineers, accurately extracting the model … Read more
Case Introduction Generative Adversarial Networks (GANs) are a type of deep learning model consisting of a generator network and a discriminator network. They improve their capabilities through adversarial training, competing against each other. The generator network attempts to produce samples that resemble real data, while the discriminator network tries to distinguish between samples generated by … Read more
Chasing 5G, GaAs/GaN Remains the Absolute Star. Due to the higher frequency bands and larger bandwidths of 5G solutions compared to 4G, the path loss is relatively greater, which poses new requirements for the material performance of RF front-end devices: 1) larger bandgap; 2) higher critical breakdown electric field; 3) higher thermal conductivity; 4) higher … Read more
0 Introduction Channel estimation is a fundamental issue in wireless communication systems, and its accuracy has a significant impact on applications such as signal recovery, interference management, and wireless resource allocation. Based on whether pilot signals are sent, channel estimation methods can be divided into three categories: blind channel estimation, pilot-based channel estimation, and semi-blind … Read more
Introduction: Due to the cold start problem in recommendation systems, recommending new videos to users in video recommendations is a highly challenging issue. The effectiveness of new video recommendations directly affects the stability of the recommendation system’s “metabolism” and the healthy development of the content ecosystem. To address this issue, this article mainly introduces the … Read more
Click the "Xiaobai Learns Vision" above, select "Add to Favorites" or "Pin" Heavyweight content delivered first time Introduction This article explains in detail how the minimax game and total loss function in GAN optimization functions are derived. It will introduce the meaning and reasoning of the optimization function in the original GAN, as well as … Read more
Source: Machine Heart Author: Jiang Siyuan The length of this article is 8300 words, recommended reading time is 8 minutes This article will start from the original paper, using Goodfellow’s speech at NIPS 2016 and Li Hongyi’s explanation from National Taiwan University, to complete the derivation, proof, and implementation of the original GAN. This article … Read more
On September 17, Professor Fan Lei from Capital Normal University shared a presentation titled “Generative Adversarial Networks – Intuitive Principles and Simple Applications” at the Yuanzhuo Academy. He introduced the basic concepts of Generative Adversarial Networks (GANs) and constructed a simple GAN model to help everyone understand the memory of familiar problems. Professor Fan introduced … Read more
Follow the WeChat public account “ML_NLP“ Set as “Starred“, to receive heavy content promptly! Reprinted from: AI Park Author: Sam Nolen Translation: ronghuaiyang Introduction Applicable in cases with very few samples. Even imperfect synthetic data can improve classifier performance. Generative Adversarial Networks (GANs) were introduced by Ian Goodfellow in 2014 and have become a very … Read more
Source: New Intelligence This article contains approximately 2600 words, and is recommended for a 10 minute read. This article compiles everything you need to know about GANs. [ Guide ]Want to learn everything about GANs?Someone has already organized it for you!From paper resources to application examples, to books, tutorials, and beginner guides, whether you are … Read more