Overview of Transformer Privacy Inference Technology 1. Introduction The Transformer model, especially large language models (LLMs) based on Transformers, has made significant progress in the field of artificial intelligence in recent years. The emergence of applications such as ChatGPT and Bing has made the capabilities of these models known and utilized by the public. These … Read more
This is the fourth translation in the Illustrated Transformer series. The series is authored by Ketan Doshi and published on Medium. During the translation process, I modified some illustrations and optimized and supplemented some descriptions based on the code provided in Li Mu’s “Hands-On Deep Learning with Pytorch”. The original article link can be found … Read more
Object detection is a fundamental task in computer vision that requires us to locate and classify objects. The groundbreaking R-CNN family[1]-[3] and ATSS[4], RetinaNet[5], FCOS[6], PAA[7], and a series of variants[8][10] have made significant breakthroughs in the object detection task. One-to-many label assignment is the core solution, which assigns each ground truth box as a … Read more
AliMei Guide Learn about Transformer, and come write one with the author. As an engineering student, when learning about Transformer, it always feels like understanding is not solid enough unless I write one myself. Knowledge gained from books is often superficial; true understanding requires practice, so take time to debug a few times! Note: No … Read more
Inspired by these studies, Shilong Liu and others conducted an in-depth study on the cross-attention module in the Transformer decoder and proposed using 4D box coordinates (x, y, w, h) as queries in DETR, namely anchor boxes. By updating layer by layer, this new query method introduces better spatial priors in the cross-attention module, simplifying … Read more
Selected from interconnects Author: Nathan Lambert Translated by Machine Heart Machine Heart Editorial Team State-space models are on the rise; has attention reached its end? In recent weeks, there has been a hot topic in the AI community: implementing language modeling with attention-free architectures. In short, this refers to a long-standing research direction in the … Read more
Machine Heart reports Machine Heart Editorial Team Do language models plan for future tokens? This paper gives you the answer. “Don’t let Yann LeCun see this.” Yann LeCun said it was too late; he has already seen it. Today, we introduce the paper that “LeCun insists on seeing,” which explores the question: Is the Transformer … Read more
Big Data Digest authorized reprint from Xi Xiaoyao Technology Author | Xie Nian Nian In interpersonal communication, especially when using a language as profound as Chinese, people often do not answer questions directly but instead adopt implicit, obscure, or indirect expressions. Humans can make accurate judgments about some implied meanings based on past experiences or … Read more
1. Quick Start: Run AI on Your Computer Want your own AI assistant without spending money? GPT4All lets you run powerful AI models locally! No internet needed, no privacy concerns, just like having a personal AI assistant! from gpt4all import GPT4All def quick_chat(): # Initialize the model (will automatically download on first use) model = … Read more
“What matters is not collecting information, but how to use it.” Recently, we have seen more and more friends sharing tips on using Kimi Chat and powerful prompts. We have compiled a collection to help everyone work and live better~ Kimi Usage Address & Previous Episodes 👇 kimi.moonshot.cn Kimi Chat Introduction and User Guide | … Read more