1 citations · 1 across the 5 of their papers we have counts for
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GuidanceNFT: NFT-Based Reward Fine-Tuning of Guided Diffusion Models
Jiayin Liu, Zeyu Michael Li, Bingshuo Qian +1
DiffusionNFT (NFT) fine-tunes diffusion models toward task-specific rewards, but optimizing these rewards does not guarantee fidelity to the full prompt. Although classifier-free g…
Variational Trajectory Optimization of Anisotropic Diffusion Schedules
Pengxi Liu, Zeyu Michael Li, Xiang Cheng
We introduce a variational framework for diffusion models with anisotropic noise schedules parameterized by a matrix-valued path that allocates noise across subspaces. Cen…
Graph Transformers Dream of Electric Flow
Xiang Cheng, Lawrence Carin, Suvrit Sra
We show theoretically and empirically that the linear Transformer, when applied to graph data, can implement algorithms that solve canonical problems such as electric flow and eige…
Transformers Implement Functional Gradient Descent to Learn Non-Linear Functions In Context
Xiang Cheng, Yuxin Chen, Suvrit Sra
Many neural network architectures are known to be Turing Complete, and can thus, in principle implement arbitrary algorithms. However, Transformers are unique in that they can impl…
Linear attention is (maybe) all you need (to understand transformer optimization)
Kwangjun Ahn, Xiang Cheng, Minhak Song +3
Transformer training is notoriously difficult, requiring a careful design of optimizers and use of various heuristics. We make progress towards understanding the subtleties of trai…