Search Results for author: Tahseen Rabbani

Found 6 papers, 2 papers with code

Benchmarking the Robustness of Image Watermarks

1 code implementation16 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.

Benchmarking

conv_einsum: A Framework for Representation and Fast Evaluation of Multilinear Operations in Convolutional Tensorial Neural Networks

no code implementations7 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.

Image Classification

Large-Scale Distributed Learning via Private On-Device Locality-Sensitive Hashing

no code implementations5 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.

SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication

1 code implementation25 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.

Federated Learning Image Classification

Practical and Fast Momentum-Based Power Methods

no code implementations20 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.

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