Jose Carlos Carrasco
Papers
1
Total Citations
3
H-Index
1
About
Dr. Jose Carlos Carrasco is a researcher whose work sits at the intersection of machine learning and robotic vision. His primary research areas include unsupervised learning, deep neural networks, and computational efficiency in perception systems. Carrasco’s most notable contribution is the introduction of a clustering learning technique for multi-layer feedforward deep neural networks, which dramatically reduces the time and parameter count required to compute network filters. This innovation, detailed in his 2013 paper "Clustering Learning for Robotic Vision," demonstrates that robust visual features can be learned in just minutes using a streamlined, unsupervised approach. While his work has garnered modest citation counts—with his key paper cited three times—its significance lies in its practical implications for real-time robotic systems, where speed and resource constraints are critical. Carrasco’s research offers a compelling alternative to traditional, computationally expensive training methods, making him a valuable voice in the ongoing effort to build more efficient, autonomous vision systems.
Research Focus
Key Achievements
Top Papers
- 1Clustering Learning for Robotic Vision3 citations · 2013