Masked language modeling in BERT The BERT model is an example of a pretrained MLM that consists of multiple layers of transformer encoders stacked on top of each other. Various large language models, such as BERT, use a fill-in-the-blank approach in which the model uses the context words ...
论文| githubBERT使用了‘完形填空’(masked language model)这样的自监督的训练机制,不需要使用标注,通过预测一个句子里面不见(masked)的词,从而获取对文本特征抽取的能力。ViT就是将transformer用到CV上…
language modeling (vs. MLM in BERT), and takes auxiliary position information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a ...
In generic distil- lation, decoder of the small model is encouraged to align feature predictions with hidden representations of the large model, so that task-agnostic knowledge can be transferred. In specific distillation, predictions of the small model are constrained to be consisten...
Previous research has shown that face masks impair the ability to perceive social information and the readability of emotions. These studies mostly explored the effect of standard medical, often white, masks on emotion recognition. However, in reality, m
Notice that EVA CLIP's vision branch learns from Ope- nAI CLIP-L, while language branch initialized from the same CLIP-L model. Therefore, starting from a CLIP-L with only 430M parameters, we progressively scale up a 1.1B EVA CLIP-g with large performance i...
OpenAI是一个想把一切GPT化的公司，到了图像这里，自然的想法就是用GPT来训一个图像模型。但是图像是个...
Ketamine assisted psychotherapy (KAP): patient demographics, clinical data and outcomes in three large practices administering ketamine with psychotherapy. J. Psychoactive Drugs 51, 189–198 (2019). Article PubMed Google Scholar Joneborg, I. et al. Active mechanisms of ketamine-assisted psychotherapy...
256 or 1024, is typically much larger than that of language. We introduce a novel decoding method where all tokens in the image are generated simulta- neously in parallel. This is feasible due to the bi-directional self-attention of MTVM. In theory, our model is able to infer all ...
Coupling these two de- signs enables us to train large models efficiently and ef- fectively: we accelerate training (by 3× or more) and im- prove accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the ...