1 citations · 1 across the 7 of their papers we have counts for
8 papers
Intermediate Representations are Strong AI-Generated Image Detectors
Zhenhan Huang, Pin-Yu Chen, Tejaswini Pedapati +1
The rapid advancement in generative AI models has enabled the creation of photorealistic images. At the same time, there are growing concerns about the potential misuse and dangers…
Unraveling the cognitive patterns of Large Language Models through module communities
Kushal Raj Bhandari, Pin-Yu Chen, Jianxi Gao
Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging from scientific discoveries and…
Forecasting Open-Weight AI Model Growth on HuggingFace
Kushal Raj Bhandari, Pin-Yu Chen, Jianxi Gao
As the open-weight AI landscape continues to proliferate-with model development, significant investment, and user interest-it becomes increasingly important to predict which models…
Modular Prompt Learning Improves Vision-Language Models
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +1
Pre-trained vision-language models are able to interpret visual concepts and language semantics. Prompt learning, a method of constructing prompts for text encoders or image encode…
Sparse Gradient Compression for Fine-Tuning Large Language Models
David H. Yang, Mohammad Mohammadi Amiri, Tejaswini Pedapati +2
Fine-tuning large language models (LLMs) for downstream tasks has become increasingly crucial due to their widespread use and the growing availability of open-source models. Howeve…
Differentiable Prompt Learning for Vision Language Models
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +1
Prompt learning is an effective way to exploit the potential of large-scale pre-trained foundational models. Continuous prompts parameterize context tokens in prompts by turning th…
Graph is all you need? Lightweight data-agnostic neural architecture search without training
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +2
Neural architecture search (NAS) enables the automatic design of neural network models. However, training the candidates generated by the search algorithm for performance evaluatio…
From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers
Bharat Runwal, Tejaswini Pedapati, Pin-Yu Chen
Pretrained Language Models (PLMs) have become the de facto starting point for fine-tuning on downstream tasks. However, as model sizes continue to increase, traditional fine-tuning…