no code implementations • 21 Nov 2023 • Jiahui Liu, Xiaohao Cai, Mahesan Niranjan
There is growing concern that the potential of black box AI may exacerbate health-related disparities and biases such as gender and ethnicity in clinical decision-making.
no code implementations • 16 Nov 2023 • Yihong Wu, Yuwen Heng, Mahesan Niranjan, Hansung Kim
To this end, in this work, we quantify the relative contributions of the known cues of depth in a monocular depth estimation setting using an indoor scene data set.
no code implementations • 10 Aug 2023 • Keqiang Fan, Xiaohao Cai, Mahesan Niranjan
The classifier module first extracts image features from the input medical images and produces disease-related indicators with their corresponding states.
no code implementations • 23 May 2023 • Jiahui Liu, Xiaohao Cai, Mahesan Niranjan
Linear discriminant analysis (LDA) has been a useful tool in pattern recognition and data analysis research and practice.
no code implementations • 23 May 2023 • Junyu Mao, Stuart E. Middleton, Mahesan Niranjan
Prompt-based models have gathered a lot of attention from researchers due to their remarkable advancements in the fields of zero-shot and few-shot learning.
1 code implementation • 8 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
no code implementations • 5 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.
5 code implementations • 27 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.
Ranked #3 on Image Classification on Fashion-MNIST
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.
1 code implementation • 4 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.
no code implementations • 5 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.
2 code implementations • ICLR 2019 • Ethan Harris, Mahesan Niranjan, Jonathon Hare
The state of our Hebb-Rosenblatt memory is embedded in STAWM as the weights space of a layer.
1 code implementation • 28 Apr 2017 • Xin Liu, Mahesan Niranjan
We address the problem of parameter estimation in models of systems biology from noisy observations.
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.