Search Results for author: Mahesan Niranjan

Found 12 papers, 5 papers with code

Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation

1 code implementation8 May 2020 Tristan Millington, Mahesan Niranjan

MSTs constructed using these rank methods tend to be more stable and maintain more edges over the dataset than those constructed using Pearson correlation.

Computational Engineering, Finance, and Science Statistical Finance

Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box

no code implementations5 May 2020 Manuel Nunes, Enrico Gerding, Frank McGroarty, Mahesan Niranjan

Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons.

Decision Making

FMix: Enhancing Mixed Sample Data Augmentation

4 code implementations27 Feb 2020 Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon Hare

Finally, we show that a consequence of the difference between interpolating MSDA such as MixUp and masking MSDA such as FMix is that the two can be combined to improve performance even further.

Data Augmentation Image Classification

A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation

no code implementations ICLR 2019 Steven Squires, Adam Prügel Bennett, Mahesan Niranjan

We design a network which can perform non-negative matrix factorisation (NMF) and add in aspects of a VAE to make the coefficients of the latent space probabilistic.

Time Series

A numerical measure of the instability of Mapper-type algorithms

1 code implementation4 Jun 2019 Francisco Belchí, Jacek Brodzki, Matthew Burfitt, Mahesan Niranjan

We define an intrinsic notion of Mapper instability that measures the variability of the output as a function of the choice of parameters required to construct a Mapper output.

Minimum description length as an objective function for non-negative matrix factorization

no code implementations5 Feb 2019 Steven Squires, Adam Prugel Bennett, Mahesan Niranjan

Non-negative matrix factorization (NMF) is a dimensionality reduction technique which tends to produce a sparse representation of data.

Dimensionality Reduction

Parameter Estimation in Computational Biology by Approximate Bayesian Computation coupled with Sensitivity Analysis

1 code implementation28 Apr 2017 Xin Liu, Mahesan Niranjan

We address the problem of parameter estimation in models of systems biology from noisy observations.

Enriching Texture Analysis with Semantic Data

no code implementations CVPR 2013 Tim Matthews, Mark S. Nixon, Mahesan Niranjan

Low-level visual features used by existing texture descriptors are then assessed in terms of their correspondence to the semantic space.

Texture Classification Zero-Shot Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.