5 citations · 19 across the 14 of their papers we have counts for
14 papers
Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity
Dang Nguyen, Ali Payani, Baharan Mirzasoleiman
Hallucination in large language models (LLMs) can be detected by assessing the uncertainty of model outputs, typically measured using entropy. Semantic entropy (SE) enhances tradit…
Bootstrapping LLM Robustness for VLM Safety via Reducing the Pretraining Modality Gap
Wenhan Yang, Spencer Stice, Ali Payani +1
Ensuring Vision-Language Models (VLMs) generate safe outputs is crucial for their reliable deployment. However, LVLMs suffer from drastic safety degradation compared to their LLM b…
Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity
Siddharth Joshi, Arnav Jain, Ali Payani +1
Contrastive Language-Image Pre-training (CLIP) on large-scale image-caption datasets learns representations that can achieve remarkable zero-shot generalization. However, such mode…
Investigating the Benefits of Projection Head for Representation Learning
Yihao Xue, Eric Gan, Jiayi Ni +2
An effective technique for obtaining high-quality representations is adding a projection head on top of the encoder during training, then discarding it and using the pre-projection…
Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent
Lauren Watson, Eric Gan, Mohan Dantam +2
Differentially private stochastic gradient descent (DP-SGD) is known to have poorer training and test performance on large neural networks, compared to ordinary stochastic gradient…
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
Xuxi Chen, Yu Yang, Zhangyang Wang +1
Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar genera…