Search Results for author:
Found 8 papers, 6 papers with code
Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data
Factor analysis methods have been widely used in neuroimaging to transfer high dimensional imaging data into low dimensional, ideally interpretable representations.
Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion
In this paper, we propose a Bayesian switching dynamical model for segmentation of 3D pose data over time that uncovers interpretable patterns in the data and is generative.
Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting
We introduce deep switching auto-regressive factorization (DSARF), a deep generative model for spatio-temporal data with the capability to unravel recurring patterns in the data and perform robust short- and long-term predictions.
Deep Markov Spatio-Temporal Factorization
This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in the presence of a control signal.
G-LBM:Generative Low-dimensional Background Model Estimation from Video Sequences
Our model, called generative low-dimensional background model (G-LBM) admits variational operations on the distribution of the manifold coordinates and simultaneously generates a low-rank structure of the latent manifold given the data.
Development of Use-specific High Performance Cyber-Nanomaterial Optical Detectors by Effective Choice of Machine Learning Algorithms
Due to their inherent variabilities, nanomaterial-based sensors are challenging to translate into real-world applications, where reliability/reproducibility is key. Recently we showed Bayesian inference can be employed on engineered variability in layered nanomaterial-based optical transmission filters to determine optical wavelengths with high accuracy/precision. In many practical applications the sensing cost/speed and long-term reliability can be equal or more important considerations. Though various machine learning tools are frequently used on sensor/detector networks to address these, nonetheless their effectiveness on nanomaterial-based sensors has not been explored. Here we show the best choice of ML algorithm in a cyber-nanomaterial detector is mainly determined by specific use considerations, e. g., accuracy, computational cost, speed, and resilience against drifts/ageing effects. When sufficient data/computing resources are provided, highest sensing accuracy can be achieved by the kNN and Bayesian inference algorithms, but but can be computationally expensive for real-time applications. In contrast, artificial neural networks are computationally expensive to train, but provide the fastest result under testing conditions and remain reasonably accurate. When data is limited, SVMs perform well even with small training sets, while other algorithms show considerable reduction in accuracy if data is scarce, hence, setting a lower limit on the size of required training data. We show by tracking/modeling the long-term drifts of the detector performance over large (1year) period, it is possible to improve the predictive accuracy with no need for recalibration. Our research shows for the first time if the ML algorithm is chosen specific to use-case, low-cost solution-processed cyber-nanomaterial detectors can be practically implemented under diverse operational requirements, despite their inherent variabilities.
DeepPBM: Deep Probabilistic Background Model Estimation from Video Sequences
This paper presents a novel unsupervised probabilistic model estimation of visual background in video sequences using a variational autoencoder framework.
Indoor GeoNet: Weakly Supervised Hybrid Learning for Depth and Pose Estimation
Humans naturally perceive a 3D scene in front of them through accumulation of information obtained from multiple interconnected projections of the scene and by interpreting their correspondence.
