Bradley Brown

Hello! I am an incoming CS PhD student at the University of Oxford, supported by the Clarendon Fund. I obtained my undergraduate degree at the University of Waterloo studying Software Engineering with a joint major in Combinatorics and Optimization. Previously, I was a Research Scientist intern at NVIDIA’s Toronto AI lab, Layer 6 AI, and Akasha Imaging.

[ Email / Github / Twitter / Google Scholar / LinkedIn ]

News

April 2023: Paper @ CVPR 2023 - Our work creating a generative model of open-world 3D scenes, NF-LDM, was accepted to CVPR 2023!
April 2023: Tiny Paper @ ICLR 2023 - Our work examing a limitation of increased scale in LLMs was accepted to the ICLR 2023 tiny paper track.
January 2023: Paper @ ICLR 2023 - Our work Verifying the Union of Manifolds Hypothesis for Image Data was accepted to ICLR 2023!
October 2022: Workshop Paper @ NeurIPS 2022 - Our work Relating Regularization and Generalization through the Intrinsic Dimension of Activations was accepted to OPT 2022 and HITY 2022.
September 2022: Internship - Returning to NVIDIA as a Research Scientist intern in the Toronto AI lab
September 2022: RecSys Competion - Our team placed 2 / 303 teams in the 2022 RecSys Challenge, read about our approach here!
February 2022: Internship - Joined Layer 6 AI as a Machine Learning Researcher in their generative modelling team.
May 2021: Internship - Joined NVIDIA as a Research Scientist intern in the Toronto AI lab.
July 2021: Paper @ ICCV 2021 - Our work Towards Rotation Invariance in Object Detection was accepted at ICCV 2021.
July 2020: Internship - Joined Akasha Imaging (now part of Intrinsic) as a Computer Vision and Robotics researcher.

Research

	NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models Seung Wook Kim^, Bradley Brown^, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, Sanja Fidler IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2023. [ Project Page / Paper ] Building a generative model of open-world 3D scenes trained on real-world in-the-wild data.
	Verifying the Union of Manifolds Hypothesis for Image Data Bradley C.A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem International Conference on Learning Representations (ICLR) 2023. [Paper / Video / Code] Extending the manifold hypothesis to support natural image data lying on a union of manifolds with varying intrinsic dimension. Show increased performance in generative modelling and image classification tasks by designing models with an inductive bias for this structure.
	Language Models Inversely Scale on Piecewise Function Evaluation with Biased Examples Jordan Juravsky^, Bradley Brown^, Atif Mahmud^, Ryan Ehrlich^, Wais Shahbaz^* Tiny Paper at the International Conference on Learning Representations (ICLR) 2023. [Paper] Demonstrating that large language models (LLMs) can be misled by providing them with factually correct, but unrepresentative/biased examples, in the context of integer-to-integer piecewise functions.
	Relating Regularization and Generalization through the Intrinsic Dimension of Activations Bradley C.A. Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem NeurIPS 2022 workshops: OPT 2022 and HITY 2022. [ Paper / Code ] Investigating how the intrinsic dimension of activations in deep neural networks are affected by regularization, correlated with improved validation performance and are coupled with the effects of sudden generalization (grokking).
	Session-based Recommendation with Transformers Yichao Lu, Zhaolin Gao, Zhaoyue Cheng, Jianing Sun, Bradley Brown, Guangwei Yu, Anson Wong, Felipe Pérez, Maksims Volkovs Proceedings of the Recommender Systems Challenge 2022. [Paper] Leveraging transformers and self-supervised learning techniques to achieve 2/300+ teams on the RecSys session-based recommendation system challenge.
	Towards Rotation Invariance in Object Detection Agastya Kalra, Guy Stoppi, Bradley Brown, Rishav Agarwal, Achuta Kadambi International Conference on Computer Vision (ICCV) 2021. [ Paper / Video / Code ] Proposing a mathematically sound rotation augmentation scheme and loss modification for object detection models that leads to better rotation invariance/equivariance.

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