7 papers
Constrained Tabular Diffusion for Finance
Michael Cardei, Jose M Munoz, Oscar Barrera +2
Generative models in finance face the dual challenge of producing realistic data while satisfying strict regulatory and economic objectives, a requirement that standard tabular dif…
Simple Self-Conditioning Adaptation for Masked Diffusion Models
Michael Cardei, Huu Binh Ta, Ferdinando Fioretto
Masked diffusion models (MDMs) generate discrete sequences by iterative denoising under an absorbing masking process. In standard masked diffusion, if a token remains masked after…
Constrained Code Generation with Discrete Diffusion
Lize Shao, Michael Cardei, Zichen Xie +2
Discrete diffusion models are a powerful, emerging paradigm for code generation. They construct programs through iterative refinement of partially corrupted token sequences and ena…
Search-Augmented Masked Diffusion Models for Constrained Generation
Huu Binh Ta, Michael Cardei, Alvaro Velasquez +1
Discrete diffusion models generate sequences by iteratively denoising samples corrupted by categorical noise, offering an appealing alternative to autoregressive decoding for struc…
Constrained Discrete Diffusion
Michael Cardei, Jacob K Christopher, Thomas Hartvigsen +2
Discrete diffusion models are a class of generative models that construct sequences by progressively denoising samples from a categorical noise distribution. Beyond their rapidly g…
ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones
Anurag Ghosh, Shen Zheng, Robert Tamburo +7
Perceiving and autonomously navigating through work zones is a challenging and underexplored problem. Open datasets for this long-tailed scenario are scarce. We propose the ROADWor…