Machine learningDeep learningThe analytics of lifelogging has generated great interest for data scientists because big and multi-dimensional data are generated as a result of lifelogging activities. In this paper, the NTCIR...doi:10.1007/978-3-030-20915-5_30Kader Belli...
Learning activity: Levels of racismCompleted 100 XP 10 minutes Racism, much like the concept of race, is also widely misunderstood. Often, when people think of racism, they conjure images of interpersonal interactions in which overt acts of discrimination are directed against a perso...
Video-based human activity recognition systems have potential contributions to various applications such as smart homes and healthcare services. In this work, we present a novel depth video-based translation and scaling invariant human activity recognition (HAR) system utilizing R transformation of depth...
DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection ArticleOpen access29 January 2021 SLEAP: A deep learning system for multi-animal pose tracking ArticleOpen access Automatic mapping of multiplexed social receptive fields by deep learni...
Benchmarking Continual Learning in Sensor-based Human Activity Recognition: an Empirical Analysis[Accepted in theInformation Sciences(April 2021)] Continual Learning in Human Activity Recognition (HAR): An Emperical Analysis of Regularization[ICML workshop on Continual Learning (July 2020)] ...
HAKE-Image (CVPR'18/20): Human body part state labels in images. HAKE-HICO, HAKE-HICO-DET, HAKE-Large, Extra-40-verbs. HAKE-AVA: Human body part state labels in videos from AVA dataset. CLIP-A2V: CLIP-based part states & verb recognizer. HAKE-A2V (CVPR'20): Activity2Vec, a gen...
Importantly, sequence classes can be used to both classify and quantify the regulatory activities of any sequence based on predictions made by the deep learning sequence model, thereby allowing any mutation to be quantified by its impact (for example, increase, decrease or no change) on cell ...
If you use Facemap, please cite the Facemappaper: Syeda, A., Zhong, L., Tung, R., Long, W., Pachitariu, M.*, & Stringer, C.* (2024). Facemap: a framework for modeling neural activity based on orofacial tracking.Nature Neuroscience, 27(1), 187-195. [bibtex] ...
We collected a large number of images at sites representative of, and throughout, the entire city, with fine temporal granularity, which allowed us to assess the dynamics of urban environment and activity over space and time. We adapted deep learning models and training datasets to the specific...
- `Part_II--Machine-Learning-Part.ipynb`: This is the notebook file which related to machine learning, visualizations and detailed analysis of each step performed to build the final model. - `README.md` : It contains a short description of this project and necessary steps to run it ...