deep machine learning - machine learning. outline ... enablers of current success cases 1. more...
TRANSCRIPT
John Ardelius, PhD Senior ResearcherSwedish Institute of Computer Science
Deep Machine Learning
Outline
● What is deep machine learning?– Enablers and success cases
– Potential for automotive
● How to do it?– Architechture and complexity
– CPU and memory requirements
● What are SICS doing?– Current research direction and trends
What is Deep Learning?
What is machine learning?
● The ability to learn and predict from data without explicit programs
What is machine learning?
● The ability to learn and predict from data without explicit programs
”SICS”
What is machine learning?
● The ability to learn and predict from data without explicit programs
”SICS”
?
Machine learning – two approaches
1) Feature detectors, ”rule based”
”SICS”
Machine learning – two approaches
1) Feature detectors, ”rule based”
2) Provide examples, ”data driven”
”SICS”
Neural networks
Learns correlation in input relevant to generate correct output
”SICS”
www.sics.se
Enablers of current success cases
1. More data → better modelsAbility to generalize without overfittnig depends on data availability
www.sics.se
Enablers of current success cases
2. More CPU/memory → larger models.Ability to find complex patterns and structure in data depends on number of model parameters.
www.sics.se
Andrew Ng's philosophy
www.sics.se
Application example : Sentence descriptions *
http://cs.stanford.edu/people/karpathy/deepimagesent/
www.sics.se
Application example : Sentence descriptions *
http://cs.stanford.edu/people/karpathy/deepimagesent/
www.sics.se
Application example : Sentence descriptions *
http://cs.stanford.edu/people/karpathy/deepimagesent/
www.sics.se
Application example : Sentence descriptions *
http://cs.stanford.edu/people/karpathy/deepimagesent/
www.sics.se
Application example : Sentence descriptions *
http://cs.stanford.edu/people/karpathy/deepimagesent/
www.sics.se
Application example : Sentence descriptions *
http://cs.stanford.edu/people/karpathy/deepimagesent/
www.sics.se
EXAMPLE 2 : Age detection
”How-old.net”
• Better-than-human computer vision
• Due to Big Data from social media + vast computational resources
• Trend: domain specific object/pattern recognition libraries
www.sics.se
Potentials for automotive
1) Situation detection
www.sics.se
Potentials for automotive
1) ADAS and external services
Reschedule my next meeting...
www.sics.se
Potentials for automotive
3) Monitoring and diagnostics
[slide from G.Hinton 2014]
3. How it works
Architecture in 2012
● 6 days to train on two GTX 580 3GB GPUs.
www.sics.se
Train offline – run online
”field of poppy flowers”
From CPU to GPU processing
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Deep Learning @ SICS
● Temporal learning– Identifying sequential patterns
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Deep Learning @ SICS
● Transfer learning– Learning from others mistakes
www.sics.se
Deep Learning @ SICS
● Contextual learning– Data does not live in isolation
www.sics.se
Thank you!