Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects such as images and genes. However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an eff...
Multi-instance learningRemote sensing imagery is widely used in mapping thematic classes, such as, forests, crops, forests and other natural and man-made objects on the Earth. With the availability of very high-resolution satellite imagery, it is now possible to iden...
Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. Durin... M Zhang,Z Zhou - 《IEEE Transactions on Knowledge & Data Engineering》 被引量: 885发表: 2014年 Bayesian Reasoning and Machine Learning ...
In this blog post we demonstrate how to optimize AWS infrastructure to further minimize deep learning training times by using distributed/multi-node synchronous training. We use ResNet-50 with the ImageNet dataset and Amazon EC2 P3 instances with NVIDIA Tesla V100 GPUs to benchmark o...
A new Ubuntu 16.04 ECS instance: Lustre kernel driver and FS mount CUDA 10 with NVIDIA Tesla 410 driver Docker 18.09-ce including nvidia-docker2 A multi-node parallel batch–compatible TensorFlow container with the following stack: Ubuntu 18.04 container image ...
Earlier works on MI deep learning in computer vision include work by Wuet al.[10], where the concept of an MI pooling (MIPool) layer is introduced to aggregate representations for multi-label classification. Yanet al.leveraged MI deep learning for efficient body part recognition [11]. Unlike...
in the context of multi-label learning, whereas Yang et al. (2013) considered weakly labeled data in the context of multi-instance multi-label learning (Zhou et al., 2012). Unlike supervised learning where the training labels are complete, weak-label learning needs to infer the integer-valued...
Meanwhile, the revolution of artificial intelligence4,5 (for example, deep learning) in recent years has shown potential in various tasks in pathology that range from disease diagnosis, prognosis and integrative multi-omic analysis6,7,8,9,10,11,12,13,14,15,16,17. However, a majority of com...
Multi camera support: IP Cameras (H264 and H265), USB cameras and Raspberry Pi Camerasthrough a RTSP proxy. Single camera per instance (e.g. one container per camera). Low resolution streaming through MQTT and high resolution streaming through WebRTC (only supports H264/PCM). ...
Thus, alternative approaches for scalable machine learning and data analysis are required. Dask is highly flexible and versatile and can be used as standalone tool or to support other frameworks. It facilitates scalable data analysis with multi-core and out-of-core computation functionalities. By ...