28 citations · 128 across the 19 of their papers we have counts for
6 papers · 1 filter
SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices
Zhengang Li, Geng Yuan, Tomoharu Yamauchi +8
Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high energy efficiency. By employing the distinct polarity of current to denote logic `0' and `1'…
SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models
Shuchen Xue, Mingyang Yi, Weijian Luo +4
Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE or ODE which is time-cons…
A Life-Cycle Energy and Inventory Analysis of Adiabatic Quantum-Flux-Parametron Circuits
Masoud Zabihi, Yanyue Xie, Zhengang Li +5
The production process of superconductive integrated circuits is complex and consumes significant amounts of resources and energy. Therefore, it is crucial to evaluate the environm…
A Causal Framework to Unify Common Domain Generalization Approaches
Nevin L. Zhang, Kaican Li, Han Gao +5
Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s). It is a fundamental problem…
Training Energy-Based Models with Diffusion Contrastive Divergences
Weijian Luo, Hao Jiang, Tianyang Hu +3
Energy-Based Models (EBMs) have been widely used for generative modeling. Contrastive Divergence (CD), a prevailing training objective for EBMs, requires sampling from the EBM with…
Complexity Matters: Rethinking the Latent Space for Generative Modeling
Tianyang Hu, Fei Chen, Haonan Wang +4
In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates i…