1 citations · 1 across the 4 of their papers we have counts for
5 papers
CANDO: Cooperative Agentic Network for Layout Design Optimization
Athanasios Masouris, Zheng Jing, Benjamin Sam Chandler +1
Layout generation for real-world facilities is a challenging problem, requiring reasoning over irregular site boundaries, heterogeneous orientations, access-aware placements, and m…
InfSplign: Inference-Time Spatial Alignment of Text-to-Image Diffusion Models
Sarah Rastegar, Violeta Chatalbasheva, Sieger Falkena +5
Text-to-image (T2I) diffusion models generate high-quality images but often fail to capture the spatial relations specified in text prompts. This limitation can be traced to two fa…
Data-Efficient Challenges in Visual Inductive Priors: A Retrospective
Robert-Jan Bruintjes, Attila Lengyel, Osman Semih Kayhan +4
Deep Learning requires large amounts of data to train models that work well. In data-deficient settings, performance can be degraded. We investigate which Deep Learning methods ben…
Self-Attention Message Passing for Contrastive Few-Shot Learning
Ojas Kishorkumar Shirekar, Anuj Singh, Hadi Jamali-Rad
Humans have a unique ability to learn new representations from just a handful of examples with little to no supervision. Deep learning models, however, require an abundance of data…
Federated Learning with Taskonomy for Non-IID Data
Hadi Jamali-Rad, Mohammad Abdizadeh, Anuj Singh
Classical federated learning approaches incur significant performance degradation in the presence of non-IID client data. A possible direction to address this issue is forming clus…