Award For Technical Clarity and Ease of Understanding


Animashree (Anima) Anandkumar, Bren Professor of Computing and Mathematical Sciences, and colleagues have won a Best Poster Award at the Neural Information Processing Systems (NIPS) MLtrain workshop. The submission was called “Tensor Regression Networks with TensorLy and MXNet” and the work showed that tensor contractions and regression layers are an effective replacement for fully connected layers in deep learning architectures. The MLtrain workshop focuses on making research more accessible through ipython notebooks and the submissions are judged based on the technical clarity and ease of understanding of the poster and the code. [View the poster]

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Best Poster Award At Neural Information Processing Systems Conference


CMS postdoctoral scholar Qi (Rose) Yu, working with Professor Anandkumar, and graduate student Stephan Zheng, working with Professor Yue, have won the Best Poster Presentation Award at the 2017 Neural Information Processing Systems (NIPS) Time Series Workshop. Dr. Yu works on the challenge of long-term forecasting in environments with nonlinear dynamics such as those involving climate and traffic data. She is tackling this challenge uses Tensor-Train RNN which are a novel family of neural sequence models that learn nonlinear dynamics directly using higher order moments and high-order state transition functions. [View her poster]

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AWS and Caltech Partner to Accelerate AI and Machine Learning


From autonomous robotics to state of-the-art computer vision, Caltech and Amazon have a lot in common, including the belief that pushing the boundaries of artificial intelligence (AI) and machine learning (ML) will not only disrupt industries, but it will fundamentally change the nature of scientific research. As part of this two-year renewable research collaboration, Amazon will provide both financial support, in the form of funding for graduate fellowships, and computing resources, in the form of AWS Cloud credits, to accelerate the work of faculty and students at Caltech in these areas. [AWS AI Blog]

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Teaching Machines How to Learn


Animashree (Anima) Anandkumar, Bren Professor of Computing and Mathematical Sciences, develops efficient techniques to speed up optimization algorithms that underpin machine-learning systems. Speaking about the connections between industry and academia she explains,“bridging the gap between industry and academia is really important. It is a big part of what brought me to Caltech. The sooner we can take theory and deploy it practically, the faster innovation moves and the more impact it can have.” [Interview with Professor Anandkumar]

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