9 citations · 14 across the 7 of their papers we have counts for
7 papers
Semantic Self-Distillation for Language Model Uncertainty
Edward Phillips, Sean Wu, Fredrik K. Gustafsson +2
Large language models present challenges for principled uncertainty quantification, in part due to their complexity and the diversity of their outputs. Semantic dispersion, or the…
Exploring the latent space of diffusion models directly through singular value decomposition
Li Wang, Boyan Gao, Yanran Li +4
Despite the groundbreaking success of diffusion models in generating high-fidelity images, their latent space remains relatively under-explored, even though it holds significant pr…
Enhancing Generalization via Sharpness-Aware Trajectory Matching for Dataset Condensation
Boyan Gao, Bo Zhao, Shreyank N Gowda +4
Dataset condensation aims to synthesize datasets with a few representative samples that can effectively represent the original datasets. This enables efficient training and produce…
FE-Adapter: Adapting Image-based Emotion Classifiers to Videos
Shreyank N Gowda, Boyan Gao, David A. Clifton
Utilizing large pre-trained models for specific tasks has yielded impressive results. However, fully fine-tuning these increasingly large models is becoming prohibitively resource-…
CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation
Shreyank N Gowda, David A. Clifton
The Segment Anything Model (SAM) has achieved remarkable successes in the realm of natural image segmentation, but its deployment in the medical imaging sphere has encountered chal…
Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring
Shreyank N Gowda, David A. Clifton
Contemporary medical contrastive learning faces challenges from inconsistent semantics and sample pair morphology, leading to dispersed and converging semantic shifts. The variabil…
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
Xingrun Xing, Boyan Gao, Zheng Zhang +5
Recent advancements in large language models (LLMs) with billions of parameters have improved performance in various applications, but their inference processes demand significant…