#deep neural networks
8 papers · 1 filter
Error Analysis of Neural-Network-Based Engression
Juntong Chen, Zijian Guo, Xinwei Shen
The paper analyzes the theoretical error of neural‑network‑based engression, a method for learning conditional distributions via an energy score, and derives convergence rates by d…
Mixed-Frequency Time Series Forecasting via Depth-Separable Neural Networks
Yize Wang, Qianqian Zhu, Guodong Li
The paper proposes a deep neural network architecture that aligns mixed-frequency time series using nonlinear transformations, with parameter sharing across stages to improve forec…
Minimax Theory of Likelihood-Based Deep Learning for Speckle Regression
Soham Jana
The paper develops a minimax theory for likelihood‑based deep neural network estimators in speckle regression, showing they achieve optimal nonparametric rates despite multiplicati…
Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation
Yizhou Zhang, Wangjin Zhou, Yi Zhao +3
The paper uncovers that music aesthetic scoring models often rely on genre cues as shortcuts, leading to biased evaluations, and introduces a training objective that reweights hard…
Weight Feedback Computes the Jacobian Transpose Locally in Modern Deep Networks
Junlong Shen, Xingyu Li
The paper shows that for common deep network layers with activation, normalization, and linear maps, the Jacobian‑transpose needed for predictive coding can be expressed using only…
Variational Inference for Evidential Deep Learning
Jiawei Tang, Xinyan Du, Hui Liu +2
The paper introduces VI-EDL, a variational inference framework for evidential deep learning that controls evidence growth and provides theoretical guarantees for uncertainty estima…