849 citations · 1.1k across the 20 of their papers we have counts for
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SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers
KL Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman +1
Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of de…
NOLA: Compressing LoRA using Linear Combination of Random Basis
Soroush Abbasi Koohpayegani, KL Navaneet, Parsa Nooralinejad +2
Fine-tuning Large Language Models (LLMs) and storing them for each downstream task or domain is impractical because of the massive model size (e.g., 350GB in GPT-3). Current litera…
Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning
Ajinkya Tejankar, Maziar Sanjabi, Qifan Wang +4
Recently, self-supervised learning (SSL) was shown to be vulnerable to patch-based data poisoning backdoor attacks. It was shown that an adversary can poison a small part of the un…
A Cookbook of Self-Supervised Learning
Randall Balestriero, Mark Ibrahim, Vlad Sobal +16
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art wi…