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Igor Molybog
Assistant Professor
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Research Interests
Computational Techniques for Learning From Data That Are Highly Scalable With the Availability of the Compute
Working on Optimizers and Model Architecture Scaling Schemes That Would Allow Predictable and Efficient Training of Transformer Models Containing Hundreds of Billions of Parameters
Sparse and Compressive Sensing Techniques
Topic Modeling
Natural Language Processing Techniques
Multimodal Machine Learning Applications
Machine Learning and Algorithms
Academic Metrics
Links
Grants
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