15 citations · 37 across the 13 of their papers we have counts for
14 papers
Decoding Insect Song: A Multitask Semisupervised Orthoptera Bioacoustic Classifier
Olga Isupova, Danil Kuzin, Ella Browning +2
Passive acoustic monitoring holds great promise for ecological inference, yet existing automated tools are typically narrowly trained and non-transferable. We address these limitat…
Improving Deep Ensembles by Estimating Confusion Matrices
Danil Kuzin, Olga Isupova, Steven Reece +1
Ensembling in deep learning improves accuracy and calibration over single networks. The traditional aggregation approach, ensemble averaging, treats all individual networks equally…
Disaster mapping from satellites: damage detection with crowdsourced point labels
Danil Kuzin, Olga Isupova, Brooke D. Simmons +1
High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastruct…
Using Echo State Networks to Approximate Value Functions for Control
Allen G. Hart, Kevin R. Olding, A. M. G. Cox +2
An Echo State Network (ESN) is a type of single-layer recurrent neural network with randomly-chosen internal weights and a trainable output layer. We prove under mild conditions th…
Mining and Tailings Dam Detection In Satellite Imagery Using Deep Learning
Remis Balaniuk, Olga Isupova, Steven Reece
This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyse a real, large-scale problem: the automatic country-wide…
Uncertainty propagation in neural networks for sparse coding
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
A novel method to propagate uncertainty through the soft-thresholding nonlinearity is proposed in this paper. At every layer the current distribution of the target vector is repres…
BCCNet: Bayesian classifier combination neural network
Olga Isupova, Yunpeng Li, Danil Kuzin +3
Machine learning research for developing countries can demonstrate clear sustainable impact by delivering actionable and timely information to in-country government organisations (…
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that give…
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning
Danil Kuzin, Le Yang, Olga Isupova +1
Gaussian process regression is a machine learning approach which has been shown its power for estimation of unknown functions. However, Gaussian processes suffer from high computat…
Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
Video analytics requires operating with large amounts of data. Compressive sensing allows to reduce the number of measurements required to represent the video using the prior knowl…
Structured Sparse Modelling with Hierarchical GP
Danil Kuzin, Olga Isupova, Lyudmila Mihaylova
In this paper a new Bayesian model for sparse linear regression with a spatio-temporal structure is proposed. It incorporates the structural assumptions based on a hierarchical Gau…
Learning Methods for Dynamic Topic Modeling in Automated Behaviour Analysis
Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
Semi-supervised and unsupervised systems provide operators with invaluable support and can tremendously reduce the operators load. In the light of the necessity to process large vo…
Dynamic Hierarchical Dirichlet Process for Abnormal Behaviour Detection in Video
Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
This paper proposes a novel dynamic Hierarchical Dirichlet Process topic model that considers the dependence between successive observations. Conventional posterior inference algor…
Anomaly detection in video with Bayesian nonparametrics
Olga Isupova, Danil Kuzin, Lyudmila Mihaylova
A novel dynamic Bayesian nonparametric topic model for anomaly detection in video is proposed in this paper. Batch and online Gibbs samplers are developed for inference. The paper…