3 citations · 5 across the 9 of their papers we have counts for
9 papers
Spatial-ViLT: Enhancing Visual Spatial Reasoning through Multi-Task Learning
Chashi Mahiul Islam, Oteo Mamo, Samuel Jacob Chacko +2
Vision-language models (VLMs) have advanced multimodal reasoning but still face challenges in spatial reasoning for 3D scenes and complex object configurations. To address this, we…
Universal and Transferable Adversarial Attack on Large Language Models Using Exponentiated Gradient Descent
Sajib Biswas, Mao Nishino, Samuel Jacob Chacko +1
As large language models (LLMs) are increasingly deployed in critical applications, ensuring their robustness and safety alignment remains a major challenge. Despite the overall su…
Adversarial Attack on Large Language Models using Exponentiated Gradient Descent
Sajib Biswas, Mao Nishino, Samuel Jacob Chacko +1
As Large Language Models (LLMs) are widely used, understanding them systematically is key to improving their safety and realizing their full potential. Although many models are ali…
A Theory of Machine Understanding via the Minimum Description Length Principle
Canlin Zhang, Xiuwen Liu
Deep neural networks trained through end-to-end learning have achieved remarkable success across various domains in the past decade. However, the end-to-end learning strategy, orig…
Unaligning Everything: Or Aligning Any Text to Any Image in Multimodal Models
Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu
Utilizing a shared embedding space, emerging multimodal models exhibit unprecedented zero-shot capabilities. However, the shared embedding space could lead to new vulnerabilities i…
Learning Regularities from Data using Spiking Functions: A Theory
Canlin Zhang, Xiuwen Liu
Deep neural networks trained in an end-to-end manner are proven to be efficient in a wide range of machine learning tasks. However, there is one drawback of end-to-end learning: Th…