Denis Rakitin

🚀COSMOS is OUT! @NeurIPSconf 2025! 📈COSMOS achieves up to 2x faster text generation compared to other diffusion models, utilizing up to 8x compression in text representations for superior efficiency. 📄 Paper: arxiv.org/pdf/2506.21170 💻 Code: github.com/MeshchaninovVi… (1/6)

1/ Can we efficiently learn the destruction process of diffusion samplers? Can we learn not just the drift, but also the variance for all transition kernels? – We answer YES in our recent paper “Adaptive Destruction Processes for Diffusion Samplers” (Oral at NeurIPS 2025 FPI Workshop).

We will be presenting “Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces” during #NeurIPS2021 poster session 8! We iteratively apply the Gumbel-Max trick to obtain structured variables instead of categorical. Poster: neurips.cc/virtual/2021/p…