I am a research assistant at the RLAI lab working with Martha White and Rich Sutton. I'm interested in building computational intelligence that learns online. Currently, I'm working on scalable online representation learning. Previously, I briefly worked at MILA, Montreal with Prof. Yoshua Bengio on incorporating causality and online learning, and at TUKL-SEECS with Dr. Faisal Shafait on various Computer Vision and Machine Learning R&D projects during most of my undergraduate. I also represented my country, Pakistan, at 55th International Mathematical Olympiad , and XXVI Asian Pacific Mathematical Olympiad before college, receiving an honourable-mention and a bronze medal respectively.
We propose a method for learning models that do not rely on spurious correlations. Our work builds on IRM (M Arjovsky, 2019) except unlike IRM, it can be implemented online to (1) detect spurious features for a set of given features and (2) learn non-spurious features from sensory data.
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We propose OML, an objective for learning representations by using catastrophic interference as a training
signal. Resultant representations are naturally sparse, accelerate future learning and are robust to forgetting
under online updates in continual learning.
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We isolate the truly effective existing ideas for incremental classifier learning from those that only work under certain conditions. Moreover, we propose a dynamic threshold moving algorithm that can successfully remove bias from an incrementally learned classifier when learning by knowledge distillation.
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We propose a computationally efficient document segmentation algorithm that recursively uses
convolutional neural networks to precisely
localize a document in a natural image in real-time.
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