4 citations · 8 across the 19 of their papers we have counts for
5 papers · 1 filter
MedMax: Mixed-Modal Instruction Tuning for Training Biomedical Assistants
Hritik Bansal, Daniel Israel, Siyan Zhao +3
Recent advancements in mixed-modal generative have opened new avenues for developing unified biomedical assistants capable of analyzing biomedical images, answering complex questio…
OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows
Shufan Li, Konstantinos Kallidromitis, Akash Gokul +4
We introduce OmniFlow, a novel generative model designed for any-to-any generation tasks such as text-to-image, text-to-audio, and audio-to-image synthesis. OmniFlow advances the r…
Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory
Eric Hanchen Jiang, Yasi Zhang, Zhi Zhang +4
Text-to-image (T2I) diffusion models have revolutionized generative modeling by producing high-fidelity, diverse, and visually realistic images from textual prompts. Despite these…
PopAlign: Population-Level Alignment for Fair Text-to-Image Generation
Shufan Li, Harkanwar Singh, Aditya Grover
Text-to-image (T2I) models achieve high-fidelity generation through extensive training on large datasets. However, these models may unintentionally pick up undesirable biases of th…
Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data
Shufan Li, Harkanwar Singh, Aditya Grover
In recent years, Transformers have become the de-facto architecture for sequence modeling on text and a variety of multi-dimensional data, such as images and video. However, the us…