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Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference., , , , , , , и . IEEE J. Sel. Top. Signal Process., 14 (4): 623-633 (2020)From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach., , , , , и . CoRR, (2023)Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning., , , , , и . CoRR, (2018)Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees., , и . CoRR, (2019)ARist: An effective API argument recommendation approach., , , , , , и . J. Syst. Softw., (октября 2023)Improving Generative Flow Networks with Path Regularization., , , , , и . CoRR, (2022)p-Laplacian Transformer., , , и . CoRR, (2023)Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals., , и . CoRR, (2023)DeepGRAND: Deep Graph Neural Diffusion., , , , и . ACSSC, стр. 1044-1048. IEEE, (2023)A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys., , , , , , , , , и . ICASSP, стр. 1-5. IEEE, (2023)