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cs.LG2025
BI-DCGAN: A Theoretically Grounded Bayesian Framework for Efficient and Diverse GANs
Mahsa Valizadeh, Rui Tuo, James Caverlee
Generative Adversarial Networks (GANs) are proficient at generating synthetic data but continue to suffer from mode collapse, where the generator produces a narrow range of outputs…
cs.CL2025
Language Models as Semantic Augmenters for Sequential Recommenders
Mahsa Valizadeh, Xiangjue Dong, Rui Tuo +1
Large Language Models (LLMs) excel at capturing latent semantics and contextual relationships across diverse modalities. However, in modeling user behavior from sequential interact…
cs.LG2025
From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation
Wenyuan Zhao, Haoyuan Chen, Tie Liu +2
With the strengths of both deep learning and kernel methods like Gaussian Processes (GPs), Deep Kernel Learning (DKL) has gained considerable attention in recent years. From the co…