2 citations · 4 across the 5 of their papers we have counts for
5 papers
Labels, Information, and Computation: Efficient Learning Using Sufficient Labels
Shiyu Duan, Spencer Chang, Jose C. Principe
In supervised learning, obtaining a large set of fully-labeled training data is expensive. We show that we do not always need full label information on every single training exampl…
Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods
Shiyu Duan, Jose C. Principe
This tutorial paper surveys provably optimal alternatives to end-to-end backpropagation (E2EBP) -- the de facto standard for training deep architectures. Modular training refers to…
JPD-SE: High-Level Semantics for Joint Perception-Distortion Enhancement in Image Compression
Shiyu Duan, Huaijin Chen, Jinwei Gu
While humans can effortlessly transform complex visual scenes into simple words and the other way around by leveraging their high-level understanding of the content, conventional o…
Modularizing Deep Learning via Pairwise Learning With Kernels
Shiyu Duan, Shujian Yu, Jose Principe
By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces,…
On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation
Shiyu Duan, Shujian Yu, Yunmei Chen +1
We propose a novel family of connectionist models based on kernel machines and consider the problem of learning layer-by-layer a compositional hypothesis class, i.e., a feedforward…