Genre
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting
Diffusion models have significantly advanced the field of generative modeling.
Q-VLM: Post-training Quantization for Large Vision-Language Models
In this paper, we propose a post-training quantization framework of large vision-language models (L VLMs) for efficient multi-modal inference. Conventional quantization methods sequentially search the layer-wise rounding functions by minimizing activation discretization errors, which fails to acquire optimal quantization strategy without considering cross-layer dependency.
Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way Ming Chen
These features are then fed into a V alue Classification Network (VCN) which directly generates classification solutions.
TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic Scene
Despite advancements in Neural Implicit models for 3D surface reconstruction, handling dynamic environments with interactions between arbitrary rigid, nonrigid, or deformable entities remains challenging.
FIDE: F requency-I nflated Conditional Diffusion Model for E xtreme-A ware Time Series Generation
It also extends the traditional diffusion-based model, enabling the generation of samples conditioned on the block maxima, thereby enhancing the model's
Low-Rank Optimal Transport through Factor Relaxation with Latent Coupling Peter Halmos
We leverage these advantages to derive a new algorithm Factor Relaxation with Latent Coupling (FRLC), which uses coordinate mirror descent to compute the LC factorization.
