Machine learning (ML) enabled classification models are becoming increasingly popular for tackling the sheer volume and speed of online misinformation.
We first propose the Fusion Transformer, an attention-based model for multimodal and multi-sensor fusion.
We use data collected from 10 people with Parkinson's, and 10 controls, each of whom lived for five days in a smart home with various sensors.
Hybrid closed loop systems represent the future of care for people with type 1 diabetes (T1D).
Training these machine learning models require datasets of sufficient scale, diversity and quality.
Ranked #1 on Node Classification on MuMiN-small
We empirically show that our approach can accurately learn the reliability of each trainer correctly and use it to maximise the information gained from the multiple trainers' feedback, even if some of the sources are adversarial.
1 code implementation • 8 Oct 2021 • Mohammud J. Bocus, Wenda Li, Shelly Vishwakarma, Roget Kou, Chong Tang, Karl Woodbridge, Ian Craddock, Ryan McConville, Raul Santos-Rodriguez, Kevin Chetty, Robert Piechocki
This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities.
Reciprocal Recommenders are a subset of recommender systems, where the items in question are people, and the objective is therefore to predict a bidirectional preference relation.
Traditional approaches to activity recognition involve the use of wearable sensors or cameras in order to recognise human activities.
Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations.
However, few studies have considered the balance between wearable power consumption and activity recognition accuracy.
We study a number of local and global manifold learning methods on both the raw data and autoencoded embedding, concluding that UMAP in our framework is best able to find the most clusterable manifold in the embedding, suggesting local manifold learning on an autoencoded embedding is effective for discovering higher quality discovering clusters.
Ranked #1 on Image Clustering on HAR
While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked.
In this paper we study the prediction of heart rate from acceleration using a wrist worn wearable.