4 papers
Spatial Priors via Space Filling Curves for Small and Limited Data Vision Transformers
Leyla Naz Candogan, Arshia Afzal, Pol Puigdemont +1
Though Vision Transformers (ViTs) have become the dominant backbone in many computer vision tasks, due to permutation equivariance, their attention mechanism lacks explicit spatial…
Quantum-PEFT: Ultra parameter-efficient fine-tuning
Toshiaki Koike-Akino, Francesco Tonin, Yongtao Wu +3
This paper introduces Quantum-PEFT that leverages quantum computations for parameter-efficient fine-tuning (PEFT). Unlike other additive PEFT methods, such as low-rank adaptation (…
Single-pass Detection of Jailbreaking Input in Large Language Models
Leyla Naz Candogan, Yongtao Wu, Elias Abad Rocamora +2
Defending aligned Large Language Models (LLMs) against jailbreaking attacks is a challenging problem, with existing approaches requiring multiple requests or even queries to auxili…
Linear Attention for Efficient Bidirectional Sequence Modeling
Arshia Afzal, Elias Abad Rocamora, Leyla Naz Candogan +5
Linear Transformers and State Space Models have emerged as efficient alternatives to softmax Transformers for causal sequence modeling, enabling parallel training via matrix multip…