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ARCHITECTURE: TREE LSTM
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Classroom Contents
The AI Trinity - Data + Algorithms + Infrastructure
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- 1 Intro
- 2 TRINITY FUELING ARTIFICIAL INTELLIGENCE
- 3 TASK: NAMED ENTITY RECOGNITION
- 4 RESULTS NER task on largest open benchmark (Onto-notes)
- 5 ACTIVE LEARNING WITH PARTIAL FEEDBACK
- 6 RESULTS ON TINY IMAGENET (100K SAMPLES) Accuracy vs. Mof Questions
- 7 TWO TAKE-AWAYS
- 8 CROWDSOURCING: AGGREGATION OF CROWD ANNOTATIONS
- 9 PROPOSED CROWDSOURCING ALGORITHM
- 10 LABELING ONCE IS OPTIMAL: BOTH IN THEORY AND PRACTICE
- 11 DATA AUGMENTATION 1: GENERATIVE MODELING
- 12 PREDICTIVE VS GENERATIVE MODELS
- 13 STATISTICAL GUARANTEES FOR THE NRM
- 14 NEURAL RENDERING MODEL (NRM)
- 15 NEURAL DEEP RENDERING MODEL (NRM)
- 16 DATA AUGMENTATION 2: SYMBOLIC EXPRESSIONS
- 17 ARCHITECTURE: TREE LSTM
- 18 SOME RESEARCH LEADERS AT NVIDIA
- 19 CONCLUSION Al needs integration of data, algorithms and infrastructure
