1 code implementation • 22 Apr 2024 • Anthony Baptista, Alessandro Barp, Tapabrata Chakraborti, Chris Harbron, Ben D. MacArthur, Christopher R. S. Banerji
To illustrate this idea, we present a computational framework to quantify the geometric changes that occur as data passes through successive layers of a DNN, and use this framework to motivate a notion of `global Ricci network flow' that can be used to assess a DNN's ability to disentangle complex data geometries to solve classification problems.
no code implementations • 4 Apr 2023 • Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti, Chris Holmes, Ryan Copping, Niels Hagenbuch, Stefanie Biedermann, Jack Noonan, Brieuc Lehmann, Aditi Shenvi, Xuan Vinh Doan, David Leslie, Ginestra Bianconi, Ruben Sanchez-Garcia, Alisha Davies, Maxine Mackintosh, Eleni-Rosalina Andrinopoulou, Anahid Basiri, Chris Harbron, Ben D. MacArthur
Missing data are an unavoidable complication in many machine learning tasks.
no code implementations • 17 May 2019 • Tapabrata Chakraborti, Arijit Patra, Alison Noble
How can one ensure that the decisions were taken based on merit and not on protected attributes like race or sex?
no code implementations • 3 May 2019 • Tapabrata Chakraborti, Brendan McCane, Steven Mills, Umapada Pal
We present a simple effective way of achieving this by learning a generic Mahalanabis distance in a collaborative loss function in an end-to-end fashion with any standard convolutional network as the feature learner.
no code implementations • 21 Mar 2019 • Tapabrata Chakraborti, Brendan McCane, Steven Mills, Umapada Pal
We present a conditional probabilistic framework for collaborative representation of image patches.
no code implementations • 28 Jan 2019 • Tapabrata Chakraborti, Brendan McCane, Steven Mills, Umapada Pal
We present an end-to-end deep network for fine-grained visual categorization called Collaborative Convolutional Network (CoCoNet).
Fine-Grained Visual Categorization Fine-Grained Visual Recognition +1
no code implementations • 25 Oct 2017 • Tapabrata Chakraborti, Brendan McCane, Steven Mills, Umapada Pal
This letter introduces the LOOP binary descriptor (local optimal oriented pattern) that encodes rotation invariance into the main formulation itself.