Context: Recently deep learning based Natural Language Processing (NLP) models have shown great potential in the modeling of source code. However, a major limitation of these approaches is that they take source code as simple tokens of text and ignore its contextual, syntactical and structural ...
CVPR2017: Learning Deep Context-aware Features over Body and Latent Parts for,程序员大本营,技术文章内容聚合第一站。
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic...
① 数据集设置:Market1501、CUHK03、MARS; ② 参数设置:模型基于Caffe;图片大小resize为150*64;batch size = 64;learning rate = 0.01,并在1w次迭代后下降至0.01倍;momentum = 0.9,weight decay = 5*10-3;共迭代5w次。 (2)实验结果:
Deep learning-based human motion recognition for predictive context-aware human-robot collaboration,程序员大本营,技术文章内容聚合第一站。
Effective performance profiling and analysis are essential for optimizing training and inference of deep learning models, especially given the growing complexity of heterogeneous computing environments. However, existing tools often lack the capability to provide comprehensive program context information and perf...
深度学习(Deep Learning) 赞同8添加评论 分享喜欢收藏申请转载 写下你的评论... 还没有评论,发表第一个评论吧 推荐阅读 掌握学习方法——作为开发者最重要的能力 disco...发表于从零学习前... 软件开发探索之道:让自己成为知识的所有者 陈天发表于迷思 The fourth industrial re...
Wang, “Deep Learning for Acoustic Echo Cancellation in Noisy and Double-Talk Scenarios,” in INTERSPEECH, 2018, pp. 3239–3243. [9] H. Zhang, K. Tan, and D. Wang, “Deep Learning for Joint Acoustic Echo and Noise Cancellation with Nonlinear Distortions,” in INTERSPEECH, 2019, pp. ...
Context-Aware Sparse Deep Coordination Graphsarxiv.org/abs/2106.02886 背景介绍 这篇文章从标题就可以看出来,是利用图结构来解决MARL问题的。这类文章相比其他fully decentralized的MARL工作,主要的出发点是那些方法中每个智能体的utility function(就是Q函数)仅依赖自己的观测和动作,可能无法区分出其他智能体对自己...
【现有方案的不足】:① 现有的特征交叉:FM、Wide&Deep、DeepFM、XDeepFM只学习每个特征的固定表示,而不考虑在不同的语境下每个特征的不同重要性,导致表现不佳;② 学习特征的向量级权重:IFM、FiBiNET使用SENet学习特征权,仍是线性变换 【self-att+CIE的做法】:①self-att自行两两交叉,特征个数不变,但是特征表示...