1 code implementation • 2 Apr 2024 • Dehao Yuan, Cornelia Fermüller, Tahseen Rabbani, Furong Huang, Yiannis Aloimonos
We propose VecKM, a novel local point cloud geometry encoder that is descriptive, efficient and robust to noise.
1 code implementation • 16 Jan 2024 • Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, ChengHao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang
We present WAVES (Watermark Analysis Via Enhanced Stress-testing), a novel benchmark for assessing watermark robustness, overcoming the limitations of current evaluation methods. WAVES integrates detection and identification tasks, and establishes a standardized evaluation protocol comprised of a diverse range of stress tests.
no code implementations • 7 Jan 2024 • Tahseen Rabbani, Jiahao Su, Xiaoyu Liu, David Chan, Geoffrey Sangston, Furong Huang
Modern ConvNets continue to achieve state-of-the-art results over a vast array of vision and image classification tasks, but at the cost of increasing parameters.
no code implementations • 5 Jun 2023 • Tahseen Rabbani, Marco Bornstein, Furong Huang
This allows devices to avoid maintaining (i) the fully-sized model and (ii) large amounts of hash tables in local memory for LSH analysis.
1 code implementation • 25 Oct 2022 • Marco Bornstein, Tahseen Rabbani, Evan Wang, Amrit Singh Bedi, Furong Huang
Furthermore, we provide theoretical results for IID and non-IID settings without any bounded-delay assumption for slow clients which is required by other asynchronous decentralized FL algorithms.
no code implementations • 17 Sep 2021 • Tahseen Rabbani, Brandon Feng, Marco Bornstein, Kyle Rui Sang, Yifan Yang, Arjun Rajkumar, Amitabh Varshney, Furong Huang
Federated learning (FL) is a popular paradigm for private and collaborative model training on the edge.
no code implementations • 20 Aug 2021 • Tahseen Rabbani, Apollo Jain, Arjun Rajkumar, Furong Huang
The power method is a classical algorithm with broad applications in machine learning tasks, including streaming PCA, spectral clustering, and low-rank matrix approximation.