The past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers, leading some to herald the emergence of a new field: “computational social science.” In this talk I highlight two areas of research that would not have been p
Multiplicative updates multiply the parameters by nonnegative factors. These updates are motivated by a Maximum Entropy Principle and they are prevalent in evolutionary processes where the parameters are for example concentrations of species and the factors are survival rates.
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Large-scale distributed storage and caching systems form the foundation of big-data systems. A key scalability challenge in distributed storage systems is achieving fault tolerance in a resource-efficient manner.
Sleep occupies nearly a third of human’s life and acts as a critical daily function, helping our body to balance and regulate vital systems. However, in the United States, more than 35% of the population is sleep deprived.
Deep learning has spawned a wide range of AI applications that are changing our lives. However, deep neural networks are both computationally and memory intensive, thus they are power hungry when deployed on embedded systems and data centers with a limited power budget.
Natural language processing (NLP) aims to teach computers to understand human language. NLP has enabled some of the most visible applications of artificial intelligence, including Google search, IBM Watson, and Apple’s Siri.
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