Visualization of learning in multilayer perceptron networks using principal component analysis

By Marcus R. Gallagher, T. Downs. 2003

41 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zheng Xu
2. Tom Goldstein
3. Gavin Taylor
4. Hao Li
5. Sarvesh Vishwakarma
6. Anupam Agrawal
7. Diganta Misra
8. Martin Jaggi

In Control and Systems Engineering

1. Zheng Xu
2. Hao Li
3. Tom Goldstein
4. Gavin Taylor
5. Sarvesh Vishwakarma
6. Anupam Agrawal
7. Csaba Szepesvari
8. Jan Peters

In Signal Processing

1. Zheng Xu
2. Hao Li
3. Tom Goldstein
4. Sarvesh Vishwakarma
5. Anupam Agrawal

Citations in the corpus

• Visualizing the Loss Landscape of Neural Nets (2017, background): “Several authors have observed that random directions fail to capture the variation in optimization trajectories, including [10, 29, 28, 27].”
• A survey on activity recognition and behavior understanding in video surveillance (2013, methodology): “However, when there is a need for a more complete description, other approaches are convenient: dynamic time warping [72], hidden Markov models [6] or neural networks [47], followed by an action recognition step and semantic description phase.”