M. Niranjan

We propose FMix, an MSDA that uses binary masks obtained by applying a threshold to low frequency images sampled from Fourier space, obtaining new state-of-the-art results on CIFAR-10 and Fashion-MNIST. Expand

We analyse MixUp, CutMix, and FMix from an information theoretic perspective, characterising learned models in terms of how they progressively compress the input with depth. Expand

We show that optical images obtained with a pair of stereo cameras may be used to extract depth information in the form of disparity values, and thereby significantly enhance the performance of a face recognition system. Expand

Correlation networks have been a popular way of inferring a financial network due to the simplicity of construction and the ease of interpretability. Expand

We provide a method of integrating spatial proteomics data together with protein-protein interaction (PPI) networks to enable the extraction of more information. Expand

We infer partial correlation networks from daily S&P500 returns, study how these networks vary over time and draw parallels to the macroeconomic events that may explain the changes. Expand

Saliency maps for protein secondary structure prediction using the techniques of saliency maps. Expand

An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction. Expand

We demonstrate a trade-off between fidelity of representation of amino acids of proteins and the resulting dimensionality of the embedding space. Expand