270 citations · 1.4k across the 97 of their papers we have counts for
6 papers · 1 filter
Learning Morphisms with Gauss-Newton Approximation for Growing Networks
Neal Lawton, Aram Galstyan, Greg Ver Steeg
A popular method for Neural Architecture Search (NAS) is based on growing networks via small local changes to the network's architecture called network morphisms. These methods sta…
QuAILoRA: Quantization-Aware Initialization for LoRA
Neal Lawton, Aishwarya Padmakumar, Judith Gaspers +4
QLoRA reduces the memory-cost of fine-tuning a large language model (LLM) with LoRA by quantizing the base LLM. However, quantization introduces quantization errors that negatively…
Exploring the Design Space of Diffusion Bridge Models
Shaorong Zhang, Yuanbin Cheng, Greg Ver Steeg
Diffusion bridge models and stochastic interpolants enable high-quality image-to-image (I2I) translation by creating paths between distributions in pixel space. However, the prolif…
Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training
Yunshu Wu, Yingtao Luo, Xianghao Kong +2
Diffusion models learn to denoise data and the trained denoiser is then used to generate new samples from the data distribution. In this paper, we revisit the diffusion sampling pr…
Prompt Perturbation Consistency Learning for Robust Language Models
Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi +4
Large language models (LLMs) have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. However,…
Interpretable Measures of Conceptual Similarity by Complexity-Constrained Descriptive Auto-Encoding
Alessandro Achille, Greg Ver Steeg, Tian Yu Liu +3
Quantifying the degree of similarity between images is a key copyright issue for image-based machine learning. In legal doctrine however, determining the degree of similarity betwe…