1 code implementation • 14 Sep 2022 • Jacob Tyo, Bhuwan Dhingra, Zachary C. Lipton
Despite decades of research on authorship attribution (AA) and authorship verification (AV), inconsistent dataset splits/filtering and mismatched evaluation methods make it difficult to assess the state of the art.
1 code implementation • 14 Nov 2023 • Jacob Tyo, Motolani Olarinre, Youngseog Chung, Zachary C. Lipton
We analyze the impact of real-world factors including mud, pose, lighting, and more.
no code implementations • 24 Feb 2020 • Jacob Tyo, Zachary Lipton
In this paper, we consider the source of Deep Reinforcement Learning (DRL)'s sample complexity, asking how much derives from the requirement of learning useful representations of environment states and how much is due to the sample complexity of learning a policy.
no code implementations • 27 Jan 2023 • Jacob Tyo, Zachary C. Lipton
Supervised learning typically optimizes the expected value risk functional of the loss, but in many cases, we want to optimize for other risk functionals.
1 code implementation • 14 Nov 2023 • Jacob Tyo, Youngseog Chung, Motolani Olarinre, Zachary C. Lipton
RnD represents a valuable new benchmark to drive innovation in real-world OCR capabilities.
Optical Character Recognition Optical Character Recognition (OCR)
no code implementations • 12 Feb 2024 • Jacob Tyo, Zachary C. Lipton
Through extensive experiments and analysis across three datasets, CMIL not only matches state-of-the-art performance on the large-scale SYSU-30k dataset with fewer assumptions but also consistently outperforms all baselines on the WL-market1501 and Weakly Labeled MUddy racer re-iDentification dataset (WL-MUDD) datasets.
no code implementations • 12 Feb 2024 • Jacob Tyo, Motolani Olarinre, Youngseog Chung, Zachary C. Lipton
With these datasets and analysis of model limitations, we aim to foster innovations in handling real-world conditions like mud and complex poses to drive progress in robust computer vision.
Optical Character Recognition Optical Character Recognition (OCR) +2