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Found 8 papers, 3 papers with code
One-Step Diffusion Distillation via Deep Equilibrium Models
We demonstrate that the DEQ architecture is crucial to this capability, as GET matches a $5\times$ larger ViT in terms of FID scores while striking a critical balance of computational cost and image quality.
Deep Equilibrium Based Neural Operators for Steady-State PDEs
Motivated by this observation, we propose FNO-DEQ, a deep equilibrium variant of the FNO architecture that directly solves for the solution of a steady-state PDE as the infinite-depth fixed point of an implicit operator layer using a black-box root solver and differentiates analytically through this fixed point resulting in $\mathcal{O}(1)$ training memory.
Training-free Linear Image Inverses via Flows
Solving inverse problems without any training involves using a pretrained generative model and making appropriate modifications to the generation process to avoid finetuning of the generative model.
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation
Designing networks capable of attaining better performance with an increased inference budget is important to facilitate generalization to harder problem instances.
Deep Equilibrium Approaches to Diffusion Models
In this paper, we look at diffusion models through a different perspective, that of a (deep) equilibrium (DEQ) fixed point model.
Contrasting the landscape of contrastive and non-contrastive learning
Some recent works however have shown promising results for non-contrastive learning, which does not require negative samples.
Deep Local Trajectory Replanning and Control for Robot Navigation
We present a navigation system that combines ideas from hierarchical planning and machine learning.
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation.
