As the data is not pre-classified, interpreting the output of an unsupervised learning model can be challenging. Unsupervised learning models often provide results in the form of clusters, associations, or patterns. Interpreting these results and understanding their real-world implications can be diffic...
Recently, many object detection meth- ods [4, 42, 28, 31, 41, 15, 9, 25, 14] have been proposed and achieved great progress, pushing the related applica- tions forward to the real world. However, most existing methods rely on a great volume of fine ...
We restrict users from posting their own media and from unfol- lowing a followed user to conduct a controlled real-time, real-world social experiment. This web application is released for public use to replicate or conduct similar online social experiments. The focus of the data collec- tion ...
cd pretrain_src outdir=../datasets/REVERIE/expr_duet/pretrain_hm3d_v1/test python train_hm3d_reverie.py --world_size 1 --vlnbert cmt \ --model_config config/hm3d_reverie_obj_model_config.json \ --config config/hm3d_reverie_obj_pretrain.json \ --output_dir $outdir ...
annotated by professionals. While the acquisition of labeled data can be a challenging and costly endeavor, we usually have access to large amounts of unlabeled datasets, especially image and text data. Therefore, we need to find a way to tap into these underused datasets and use them for ...
Specifically, we use Latent Dirichlet Allocation (LDA) to reduce the feature dimension of document vectors to a lower dimension of topic vectors. Then the procedure of discovering relevant documents using a PU learning method is conducted in the topic space. Using Mean Average Precision (MAP) and...
which are used to train a state-of-the-art hybrid Convolutional Neural Network/Long Short Term Memory (CNN-LSTM) model to classify aortic valve malformations. To assess the real-world relevance of our image classification model, we apply the CNN-LSTM to a cohort of 9230 new patients with lo...
6. When and why does PU data arise in real-world applications? 7. How does PU learning relate to other areas of machine learning? This survey is structured around giving a comprehensive overview about how the PU learning research community is tackling each of these questions. It concludes with...
belonging to the ground plane, which can be done with the semantic segmentation of the scene [6]. The frame to world transformation allows us to project the estimated trajectories in the LIDAR metric reference system and to compare them with the ground truth, obtaining an error in meters (...
In addition, a majority of current prompt learning methods do not make use of existing unlabeled data, thus often leading to unsatisfactory performance in real-world applications. To address the above limitations, we propose a novel Chinese FSTC method called CIPLUD that combines an improved prompt...