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cs.LG2023
Disentangling Learning Representations with Density Estimation
Eric Yeats, Frank Liu, Hai Li
Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues. We present Gaussian Channel Autoencode…
cs.LG2023★ 5 cited
GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning
Xin Dong, Ruize Wu, Chao Xiong +6
Multi-task learning (MTL) aims at solving multiple related tasks simultaneously and has experienced rapid growth in recent years. However, MTL models often suffer from performance…
cs.LG2023
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble
Jingchi Zhang, Huanrui Yang, Hai Li
Ensemble learning has gain attention in resent deep learning research as a way to further boost the accuracy and generalizability of deep neural network (DNN) models. Recent ensemb…