二、Highlights Paper 带着Questions来看Paper对实际业务问题的解决,Reference来源 Q:一、文中把推荐问题转换成多分类问题,在next watch的场景下,每一个备选video都会是一个分类,因此总共的分类有数百万之巨,这在使用softmax训练时无疑是低效的,这个问题Youtube是如何解决的? Q:二、在candidate generation model的ser...
蹚入Deep Learning的人越来越多了,直接上手写Image Classification、Speech Recognition甚至搭一个完整的Machine Translation System也不再是一个难事了,但也因为嵌套的非线性结构使得Neural Network框架更像是一个黑盒子,我们该如何解释究竟是什么因素使得有这样的预测结果。 为什么要使得AI System具备可解释性呢? 如在...
Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the
in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem cell type (H1) and a differentiated lung cell type (IMR90). We train EP-DNN using p300 binding sites, as enhancers, and TSS an...
Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interfa
proteinnetworkresearch,miningproteincomplexesfrom protein-proteininteractionnetworksplaysanimportantrole inthedescriptionoffunctionaldiversityofhomologouspro- teinsandservesvaluableevolutionaryinsights,whichises- sentiallyaclusteringproblem.Itisthereforecrucialtode- ...
DNN (Deep neural network) has emerged as one of the standard methods to create a classification model. The most common issue affecting DNN performance is the class-imbalanced distribution dataset. This research designed two workflows for generating synthetic dataset using SMOTE algorithm, SDS-1, and...
个人阅读的Deep Learning方向的paper整理,分了几部分吧,但有些部分是有交叉或者内容重叠,也不必纠结于这属于DNN还是CNN之类,个人只是大致分了个类。目前只整理了部分,剩余部分还会持续更新。 一RNN 1 Recurrent neural network based language model RNN用在语言模型上的开山之作 ...
In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each ...
This paper shows the application of Deep Neural Network algorithms for Fluid-Structure Topology Optimization. The strategy offered is a new concept which can be added to the current process used to study Topology Optimization with Cellular Automata, Adjoint and Level-Set methods. The design space is...