Survey on the use of CNN and Deep Learning in Image ClassificationSiddhant DaniP. S. HanwateHrishikesh PanseKshitij ChaudhariShruti KotwalJETIR(www.jetir.org)
Deep learning is an emerging area in current scenario. Mostly, Convolutional Neural Network (CNN) and Deep Belief Network (DBN) are used as the model in deep learning. It is termed as Deep Neural Network (DNN). The use of DNN is widely spread in many applications, exclusively for detection...
Lee等人将RNN和CNN相结合来学习对话过程中引语的表示。“维基引号”和“牛津谚语简明词典”被认 为是推特对话过程引语的来源。 “Ask the GRU,” in Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’ 利用基于GRU的递归神经网络将项目文本转化为潜在特征,以提高协同过滤性能,特别是冷启动...
In this paper, we propose a combination of two powerful techniques, deep learning and parallel computing, to significantly reduce the complexity of the HEVC encoding engine. Our experimental results show that a combination of deep learning to reduce the CTU partitioning complexity with parallel ...
Furthermore, we used a CNN that is trained for object recognition to extract features relevant to the built environment because of the lack of a labeled data set for high- and low-obesity areas. This approach puts some restrictions on the interpretability of features used in our model. A CNN...
Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works(笔记),注意:论文中,很多的地方出现baseline,可以理解为参照物的意思,但是在论文中,我们还是直接将它称之为基线,也就是对照物,参照物.这片论文中,作者没有去做实际的实验,但是
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. Full multimodal LLM Android App:[MNN-LLM-Android](./project/android/apps/MnnLlmApp/README.md) - alibaba/MNN
We evaluate the use of different intermediate convolutional layers of this CNN to obtain holistic descriptors and use them to address the task of fine localization in different environments. We study the performance of the proposed deep learning approach to address the complete hierarchical localization...
In this paper, we propose a camera model identification method based on deep convolutional neural networks (CNNs). Unlike traditional methods, CNNs can automatically and simultaneously extract features and learn to classify during the learning process. A layer of preprocessing is added to the CNN ...
TensorRT-LLM is built on top of theTensorRTDeep Learning Inference library. It leverages much of TensorRT's deep learning optimizations and adds LLM-specific optimizations on top, as described above. TensorRT is an ahead-of-time compiler; it builds "Engines" which are optimized representations of...