Hardness of Approximation Between P and NP
- Aviad Rubinstein | UC Berkeley
The first question we computer scientists ask when facing a new algorithmic challenge is: is it NP-hard, or is it in P? Surprisingly, for many important problems, the answer is “neither!” I will discuss recent progress towards understanding the complexity of those problems.
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Casey Anderson
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Series: Microsoft Research Talks
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Decoding the Human Brain – A Neurosurgeon’s Experience
- Dr. Pascal O. Zinn
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Challenges in Evolving a Successful Database Product (SQL Server) to a Cloud Service (SQL Azure)
- Hanuma Kodavalla,
- Phil Bernstein
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Improving text prediction accuracy using neurophysiology
- Sophia Mehdizadeh
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Tongue-Gesture Recognition in Head-Mounted Displays
- Tan Gemicioglu
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DIABLo: a Deep Individual-Agnostic Binaural Localizer
- Shoken Kaneko
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Audio-based Toxic Language Detection
- Midia Yousefi
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From SqueezeNet to SqueezeBERT: Developing Efficient Deep Neural Networks
- Forrest Iandola,
- Sujeeth Bharadwaj
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Hope Speech and Help Speech: Surfacing Positivity Amidst Hate
- Ashique Khudabukhsh
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Towards Mainstream Brain-Computer Interfaces (BCIs)
- Brendan Allison
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Learning Structured Models for Safe Robot Control
- Subramanian Ramamoorthy
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