Liao CHANG Professor (Associate) PhD

Rock and mineral magnetism, paleomagnetism, paleoenvironmental and paleoclimate changes, marine geology and geophysics

Zhaowen PEIGerald DICKENSJule XIAOChenglong DENGChunhui TAOFeng YU

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OpenAI

OpenAI

MacBERT 的改进（Revisiting PreTrained Models for

MacBERT简介we also propose a new pretrained model called MacBERT, which MacBERT 的改进（Revisiting PreTrained Models for Chinese Natural Language Processing） 无为二里 于 1030 15:45:25 发布 3786 收藏 10

Variational PDE Models in Image Processing ams

edges. Two wellknown models have been introduced to recognize the existence of edges. One is the “objectedge” model of Mumford and Shah[13], and the other is the BV image model of Rudin, Osher, and Fatemi[15]. The objectedge model assumes that an ideal image u consists of disjoint homogeneous object patches [u k,Ω k]with u k ∈

ScispaCy: Fast and Robust Models for Biomedical

processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scis

计算机图形学与多媒体中国计算机学会

中国计算机学会推荐国际学术刊物 ( 计算机图形学与多媒体) A类

ROS中使用gazebo_ros的spawn_model时，显示[spawn

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Aligning Language Models to Follow

These InstructGPT models, which are trained with humans in the loop, are now deployed as the default language models on our API. InstructGPT is better than GPT3 at following English instructions. GPT3

segmentation_models_pytorch库学习 水木清扬 博客园

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Interpreting Deep Learning Models in Natural Language

Neural network models have achieved stateoftheart performances in a wide range of natural language processing (NLP) tasks. However, a longstanding criticism against neural network models is the lack of interpretability, which not only reduces the reliability of neural NLP systems but also limits the scope of their applications in areas

GitHub ymcui/MacBERT: Revisiting Pretrained

Revisiting Pretrained Models for Chinese Natural Language Processing Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu Published in Findings of EMNLP

GitHub Ranking666/Yolov5Processing: Multibackbone,

accomplished. .12.15. change backbone to Ghostnet. Finish EagleEye pruning YOLOv5 series. .12.27. change backbone to shufflenetv2. change backbone to efficientnetv2. .05.23. Finish network slimming pruning YOLOv5 series.

Introduction to modern natural language processing with

English. With the release of 8.0, Elastic is excited to introduce the ability to upload PyTorch machine learning models into Elasticsearch to provide modern natural language processing (NLP) in the Elastic Stack. Now Elasticsearch users are able to integrate one of the most popular formats for building NLP models and incorporate those

ScispaCy: Fast and Robust Models for

Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes

Accueil de DSpace

Accueil de DSpace

Processing Tree Models for Discrete and Continuous

processing tree models and statistical modeling in general. These articles are not included in the thesis, because they focus either on practical applications or on statistical aspects such as model selection, Bayesian inference, or software implementations. Nonetheless, several of these papers are referred to in the main text,

ScispaCy: Fast and Robust Models for Biomedical

processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scis

Aligning Language Models to Follow

These InstructGPT models, which are trained with humans in the loop, are now deployed as the default language models on our API. InstructGPT is better than GPT3 at following English instructions. GPT3

segmentation_models_pytorch库学习 水木清扬 博客园

import segmentation_models_pytorch as smp model = smp.Unet () 1 2 根据任务的不同，您可以通过选择具有更少或更多参数的主干并使用预训练的权重来初始化它来更改网络体系结构： model = smp.Unet ('resnet34', encoder_weights = 'imagenet') 1 更改模