Our unsupervised approaches leverage long short-term memory (LSTM) encoder-decoder models to embed the graph sequences into a continuous vector space. We then represent a graph by aggregating its graph sequence representations. Our supervised architecture uses an attention mechanism to collect ...
其通过具有instruction简单的seq-to-seq学习框架统一了任务,包括理解和生成,例如:image generation、visual grounding、visual question answering、image captioning、image classification、language modeling。 实验结果显示OFA在多模态基准上实现了新的SOTA,包括:image captioning、text-to-image generation、VQA、SNLI-VE等;...
1. Language Modeling Loss:语言模型损失主要用于衡量模型生成一个文本序列的概率。通常，LM任务预测给定上...
where the goal is to reformulate the conversational query into a search engine friendly query in order to satisfy users’ information needs in conversational settings. Such context-aware query reformulation problem lends itself to sequence to sequence modeling. We present a large scale op...
《Residual Flows for Invertible Generative Modeling》(CoRR 2019) GitHub: O网页链接《VisualBERT: A Simple and Performant Baseline for Vision and Language》GitHub:O网页链接《SCARLET-NAS: Bridging the gap Between Scalability and Fairness in Neural Architecture Search》GitHub:O网页链接...
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Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers: ...
Modeling text in a numerical representation is a prime task for any Natural Language Processing downstream task such as text classification. This paper att... S Modha,P Majumder,T Mandl - 《Journal of Experimental & Theoretical Artificial Intelligence》 被引量: 0发表: 2022年 ...
--Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attnto define architecture. --saved_modelto provide a path to a pre-trained model. In case oftrain.pyit will be used as a starting point in fine-tuning and in the case oftest.pyit will be used for pre...
While sequence-to-sequence tasks are commonly solved with recurrent neural network architectures, Bai et al. [1] show that convolutional neural networks can match the performance of recurrent networks on typical sequence modeling tasks or even outperform them. Potential benefits of using convolutional...