In this paper, we propose an improved NIDS using word embedding-based deep learning (WEDL-NIDS), which has the ability of dimension reduction and learning features from data with sophisticated structure. The experimental results show that the proposed method outperforms previous methods in terms of...
刚好在做preference learning 很有启发 03-18· 美国 回复1 黑风咧 佬,deepseek明确说不用rm的情况下,现在还建议研究llm+rm吗 03-20· 浙江 回复喜欢 黑风咧 孙浩 围棋错一步影响很大,llm错一步,下一步就可以通过反思完全消除影响,感觉搜不搜不是起决定作用啊 顶多效率有点区别,但是deepsee...
京东的《Towards personalized and semantic retrieval: An end-to-end solution for e-commerce search via embedding learning》后面简称 DPSR (Deep Personalized and Semantic Retrieval) 是2019 年在京东已经全量的版本。 传统双塔就跟FaceBook 的一样,DPSR 就搞得“以量取胜”。Item 塔还是和从前一样,将特征拼...
其中的嵌入(embedding),也被称为表征学习(representation learning),已被证明是成功的技术,有助于[2]的成功。本质上,嵌入是一种将ids的稀疏向量表示为密集特征向量的方法,也称为语义嵌入,因为它通常可以学习语义。一旦了解了嵌入,它就可以作为查询和文档的表征,应用于搜索引擎的各个阶段。由于该技术在计算机视觉和推荐...
Jean, N., Xie, S. M., & Ermon, S. (2018). Semi-supervised deep kernel learning: Regression with unlabeled data by minimizing predictive variance. InAdvances in Neural Information Processing Systems, pp 5322–5333. Jerrett, M., Burnett, R. T., Ma, R., Pope, C. A., III., Krewsk...
Model Architecture The overall model structure is divided into three parts: the input layer, the representation layer, and the output layer. Firstly, the input layer manages the data and features for the deep learning models. In two-tower models, user-story interaction logs are separated into us...
However, unlike deep learning, RL trains a policy model by receiving rewards through interactions with the environment without training the label data. In recent years, several attempts have been made to solve the VNE problems using RL. When RL-based algorithms are applied to solve VNE problems,...
Therefore, this paper proposes a block-based key embedding method and a color image encryption scheme based on deep learning with this method. By using a neural network model to predict the initial chaotic sequence, the key data generated by prediction are encrypted to the color image in layers...
为了在 Facebook 搜索中部署基于嵌入的检索,我们开发了一些方法来应对建模(modeling)、服务(serving)和全栈优化(full-stack optimization)方面的挑战。 在建模方面,我们提出了统一嵌入(unified embedding),这是一个双面模型,一侧是搜索请求,包括查询文本、搜索者和上下文,另一侧是文档。 为了有效地训练模型,我们开发了从...
Source: Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search TL;DR: 不同于NLP中的文本相关性任务,搜索广告模型的训练有着多个优化目标:给定一个用户查询,模型…