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Robust Physical Adversarial Patches Using Dynamically Optimized Clusters
Harrison Bagley, Will Meakin, Simon Lucey +2
Physical adversarial attacks on deep learning systems is concerning due to the ease of deploying such attacks, usually by placing an adversarial patch in a scene to manipulate the…
From Tables to Signals: Revealing Spectral Adaptivity in TabPFN
Jianqiao Zheng, Cameron Gordon, Yiping Ji +2
Task-agnostic tabular foundation models such as TabPFN have achieved impressive performance on tabular learning tasks, yet the origins of their inductive biases remain poorly under…
SineProject: Machine Unlearning for Stable Vision Language Alignment
Arpit Garg, Hemanth Saratchandran, Simon Lucey
Multimodal Large Language Models (MLLMs) increasingly need to forget specific knowledge such as unsafe or private information without requiring full retraining. However, existing u…
Cutting the Skip: Training Residual-Free Transformers
Yiping Ji, James Martens, Jianqiao Zheng +5
Transformers have achieved remarkable success across a wide range of applications, a feat often attributed to their scalability. Yet training them without skip (residual) connectio…
Stable Forgetting: Bounded Parameter-Efficient Unlearning in Foundation Models
Arpit Garg, Hemanth Saratchandran, Ravi Garg +1
Machine unlearning in foundation models (e.g., language and vision transformers) is essential for privacy and safety; however, existing approaches are unstable and unreliable. A wi…
Leaner Transformers: More Heads, Less Depth
Hemanth Saratchandran, Damien Teney, Simon Lucey
Transformers have reshaped machine learning by utilizing attention mechanisms to capture complex patterns in large datasets, leading to significant improvements in performance. Thi…
SineLoRA: Sine-Activated Delta Compression
Cameron Gordon, Yiping Ji, Hemanth Saratchandran +2
Resource-constrained weight deployment is a task of immense practical importance. Recently, there has been interest in the specific task of \textit{Delta Compression}, where partie…
Structured Initialization for Vision Transformers
Jianqiao Zheng, Xueqian Li, Hemanth Saratchandran +1
Convolutional Neural Networks (CNNs) inherently encode strong inductive biases, enabling effective generalization on small-scale datasets. In this paper, we propose integrating thi…
Enhancing Transformers Through Conditioned Embedded Tokens
Hemanth Saratchandran, Simon Lucey
Transformers have transformed modern machine learning, driving breakthroughs in computer vision, natural language processing, and robotics. At the core of their success lies the at…
Always Skip Attention
Yiping Ji, Hemanth Saratchandran, Peyman Moghadam +1
We highlight a curious empirical result within modern Vision Transformers (ViTs). Specifically, self-attention catastrophically fails to train unless it is used in conjunction with…