我们将预训练模型分为了Auto-encoding models,Auto-regressive models和Sequence-to-sequence models这三个方向去阐述大模型。 第一篇要将Auto-encoding models最具代表性的模型 Bert。 大家好,我叫BERT 因为和BERT正好是芝麻街(美国儿童动画片)中一个角色同名,后面的各个角色被后面的预训练模型也给硬凑出来了。甚至芝...
AR模型在预测下一个单词时，只能利用上文信息，形成单向预测。这种方式适用于文本生成和机器翻译任务，如Transformer和OpenAI的GPT系列论文中的GPT模型。GPT系列模型的论文标题分别为：Improving Language Understanding by Generative Pre-Training, Language Models are Unsupervised Multitask Learners和Language Mo...
Navab, Deep autoencoding models for unsupervised anomaly segmentation in brain mr images, arXiv preprint arXiv:1804.04488.C. Baur, B. Wiestler, S. Albarqouni, and N. Navab, "Deep Autoen- coding Models for Unsupervised Anomaly Segmentation in Brain MR Images," arXiv preprint arXiv:...
Understanding other autoencoding modelsIn this part, we will review autoencoding model alternatives that slightly modify the original BERT. These alternative re-implementations have led to better downstream tasks by exploiting many sources: optimizing the pre-training process and the number of layers or...
2、https://aman.ai/primers/ai/autoregressive-vs-autoencoder-models/ 3、https://discuss....
Variational Auto-Encoders Variational Auto-Encoders又称为变分自编码器,要想学习它,首先需要弄清楚Autoencoder自编码器到底是个什么东西。我从MIT 6.S191中的Deep Generative Models课程中大受启发,感兴趣的朋友可以去看看,这里就附上课程链接:http://introtodeeplearning.com/,另外有篇...Text...
(2)式最后的推导结果也叫做ELBO,论文中作者令其为L(γ,ϕ∣α,β)L(γ,ϕ∣α,β),下面开始化简ELBO。 ELBO=L(γ,ϕ∣α,β)=Eqlogp(θ,z,W∣α,β)q(θ,z∣γ,ϕ)=Eqlogp(θ,z∣α,β)p(W∣θ,z,α,β)q(θ,z∣γ,ϕ)=−DKL[q(θ,z∣γ,ϕ)∣∣p(θ,z∣α,β...
BAYESFLOW: LEARNING COMPLEX STOCHASTIC MODELS WITH INVERTIBLE NEURAL NETWORKS BAYESFLOW:使用可逆神经网络学习复杂随机模型 https://arxiv.org/pdf/2003.06281 CreateAMind 2024/05/22 2750 VITS 论文笔记 笔记论文算法音频语音 这篇文章发表在 ICML 2021 会议上,当时的 TTS(test-to-speech)工作效果好的都以两阶...
We present an efficient method of pretraining large-scale autoencoding language models using training signals generated by an auxiliary model. Originated in ELECTRA, this training strategy has demonstrated sample-efficiency to pretrain models at the scale of hundreds of millions of parameters. In this...
This is a tensorflow implementation for both of the Autoencoded Topic Models mentioned in the paper. To run theprodLDAmodel in the20Newgroupdataset: CUDA_VISIBLE_DEVICES=0 python run.py -m prodlda -f 100 -s 100 -t 50 -b 200 -r 0.002 -e 200 ...