Search Results for author:
Found 7 papers, 4 papers with code
GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors
Constantly locating moving objects, i. e., geospatial tracking, is essential for autonomous building infrastructure.
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks
Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks.
Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications
The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions.
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series
This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts.
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
We present a general framework for classification of sparse and irregularly-sampled time series.
The Shape-Time Random Field for Semantic Video Labeling
In this work, we incorporate a CRBM prior into a CRF framework and present a new state-of-the-art model for the task of semantic labeling in videos.
Collaborative Filtering and the Missing at Random Assumption
Rating prediction is an important application, and a popular research topic in collaborative filtering.
