8 papers
Regularizing cross entropy loss via minimum entropy and K-L divergence
Abdulrahman Oladipupo Ibraheem
I introduce two novel loss functions for classification in deep learning. The two loss functions extend standard cross entropy loss by regularizing it with minimum entropy and Kull…
T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction
Abdulrahman Ibraheem, Narsis Kiani, Jesper Tegner
This paper studies the usefulness of incorporating path information in predicting chemical properties from molecular graphs, in the domain of QSAR (Quantitative Structure-Activity…
T- Hop: Tensor representation of paths in graph convolutional networks
Abdulrahman Ibraheem
We describe a method for encoding path information in graphs into a 3-d tensor. We show a connection between the introduced path representation scheme and powered adjacency matrice…
HBert + BiasCorp -- Fighting Racism on the Web
Olawale Onabola, Zhuang Ma, Yang Xie +5
Subtle and overt racism is still present both in physical and online communities today and has impacted many lives in different segments of the society. In this short piece of work…
Compact Shape Trees: A Contribution to the Forest of Shape Correspondences and Matching Methods
Abdulrahman Oladipupo Ibraheem
We propose a novel technique, termed compact shape trees, for computing correspondences of single-boundary 2-D shapes in O(n2) time. Together with zero or more features defined at…
Bi-directional Shape Correspondences (BSC): A Novel Technique for 2-d Shape Warping in Quadratic Time?
Abdulrahman Oladipupo Ibraheem
We propose Bidirectional Shape Correspondence (BSC) as a possible improvement on the famous shape contexts (SC) framework. Our proposals derive from the observation that the SC fra…
Correlation of Data Reconstruction Error and Shrinkages in Pair-wise Distances under Principal Component Analysis (PCA)
Abdulrahman Oladipupo Ibraheem
In this on-going work, I explore certain theoretical and empirical implications of data transformations under the PCA. In particular, I state and prove three theorems about PCA, wh…
SENNS: Sparse Extraction Neural NetworkS for Feature Extraction
Abdulrahman Oladipupo Ibraheem
By drawing on ideas from optimisation theory, artificial neural networks (ANN), graph embeddings and sparse representations, I develop a novel technique, termed SENNS (Sparse Extra…