Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Dynamical variational autoencoders: A comprehensive review
Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …
represent high-dimensional complex data through a low-dimensional latent space learned …
[HTML][HTML] The survey: Text generation models in deep learning
Deep learning methods possess many processing layers to understand the stratified
representation of data and have achieved state-of-art results in several domains. Recently …
representation of data and have achieved state-of-art results in several domains. Recently …
Versatile diffusion: Text, images and variations all in one diffusion model
Recent advances in diffusion models have set an impressive milestone in many generation
tasks, and trending works such as DALL-E2, Imagen, and Stable Diffusion have attracted …
tasks, and trending works such as DALL-E2, Imagen, and Stable Diffusion have attracted …
Glm: General language model pretraining with autoregressive blank infilling
There have been various types of pretraining architectures including autoencoding models
(eg, BERT), autoregressive models (eg, GPT), and encoder-decoder models (eg, T5) …
(eg, BERT), autoregressive models (eg, GPT), and encoder-decoder models (eg, T5) …
[معلومات الإصدار][C] An introduction to variational autoencoders
An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Mixtext: Linguistically-informed interpolation of hidden space for semi-supervised text classification
This paper presents MixText, a semi-supervised learning method for text classification,
which uses our newly designed data augmentation method called TMix. TMix creates a …
which uses our newly designed data augmentation method called TMix. TMix creates a …
An empirical survey of data augmentation for limited data learning in NLP
NLP has achieved great progress in the past decade through the use of neural models and
large labeled datasets. The dependence on abundant data prevents NLP models from being …
large labeled datasets. The dependence on abundant data prevents NLP models from being …
Neural discrete representation learning
Learning useful representations without supervision remains a key challenge in machine
learning. In this paper, we propose a simple yet powerful generative model that learns such …
learning. In this paper, we propose a simple yet powerful generative model that learns such …
Protein design and variant prediction using autoregressive generative models
The ability to design functional sequences and predict effects of variation is central to protein
engineering and biotherapeutics. State-of-art computational methods rely on models that …
engineering and biotherapeutics. State-of-art computational methods rely on models that …
Cyclical annealing schedule: A simple approach to mitigating kl vanishing
Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for
many natural language processing (NLP) tasks. The VAE objective consists of two terms,(i) …
many natural language processing (NLP) tasks. The VAE objective consists of two terms,(i) …