A. Mousavi, G. Dasarathy, R. G. Baraniuk, “DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks,” arXiv:1707.03386, July 2017.
We develop a novel computational sensing framework for sensing and recovering structured signals called DeepCodec. When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from them using a deep convolutional neural network. In other words, it learns a transformation from the original signals to a near-optimal number of undersampled measurements and the inverse transformation from measurements to signals. This is in contrast to conventional compressive sensing (CS) systems that use random linear measurements and convex optimization or iterative algorithms for signal recovery. We compare our new framework with ℓ1-minimization from the phase transition point of view and demonstrate that it outperforms ℓ1-minimization in the regions of phase transition plot where ℓ1-minimization cannot recover the exact solution. In addition, we experimentally demonstrate how learning measurements enhances recovery performance, speeds up training, and reduces the number of parameters to learn.
DeepCodec learns a transformation from signals x to measurement vectors y and an approximate inverse transformation from measurement vectors y to signals x using a deep convolutional network that consists of convolutional and sub-pixel convolution layers.
Recovery comparison of DeepCodec vs. LASSO (with optimal regularization parameter).
Rice University-based nonprofit OpenStax, which is already changing the economics of higher education by providing free textbooks to more than 1 million college students per year, today launched a low-cost, personalized learning system called OpenStax Tutor Beta that analyzes how students learn to offer them individualized homework and tutoring. In development for three years, the system will be available this fall for three courses: college physics, biology and sociology. While students study using OpenStax Tutor, it learns how they learn — what they struggle with, what helps them most — and it uses that information to offer just-in-time remediation and enrichment. The system provides personalized assessment and spaced practice, helping students focus their studying efforts on their weak areas and remember what they learned earlier in the course.
DSP alum Marco Duarte (PhD, 2009) has been promoted to the position of Associate Professor with Tenure at the University of Massachusetts at Amherst effective September 2017. Marco is an expert in sparse signal processing, sensor networks, and pattern recognition. He has received the IEEE Signal Processing Society Overview Paper Award, an NSF/IPAM Mathematical Sciences Research Institutes Postdoctoral Fellowship, and the SPARS Best Student Paper Award. Congratulations!
The National Academy of Sciences announced today the election of 84 new members and 21 foreign associates in recognition of their distinguished and continuing achievements in original research. Ron DeVore, the Walter E. Koss Professor in the Department of Mathematics at Texas A&M University, is a pioneer of approximation theory and sparse representations. His work at Rice as the TI Visiting Professor in 2005-2006 focused on compressive sensing.
Prof. Richard Baraniuk of the Rice DSP/Machine Learning group has two open postdoc positions for research in machine learning theory and methods, computational sensing/imaging, and sparse signal processing. The group offers an energizing environment for research; 25 recent postdocs and PhD students have been placed in top faculty positions. Apply here!
The KDD 2017 Workshop on Advancing Education with Data will be held in conjunction with the Knowledge Discovery and Data Mining conference on the morning of 14 August 2017 in beautiful Halifax, Nova Scotia, Canada. The workshop will bring together data scientists and educators together to stimulate research in the interdisciplinary field of data science for education. At this year’s workshop, we are highlighting the following areas of interest: (1) Lifelong learning, (2) Assessments, (3) Learning Analytics and Personalization, and (4) Infrastructure.
KDD is a premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.
- Paper submission deadline: May 26, 2017
- Author notification: June 16, 2017
- Final version of accepted submissions: June 30, 2017
- Final workshop schedule: June 30, 2017
- Workshop: August 14, 2017
More details can be found on the workshop website.
Rice DSP PhD Eva Dyer (PhD, 2014) has accepted an assistant professor position at Georgia Tech in the Department of Biomedical Engineering. She has spent the past two years as a postdoc and research scientist at the Rehabilitation Institute of Chicago at Northwestern University. Eva joins DSP PhD alums Jim McClellan, Doug Williams, Justin Romberg, Chris Rozell, and Mark Davenport and ECE PhD alum Rob Butera.
Rice University DSP faculty member Richard Baraniuk has been elected to the American Academy of Arts and Sciences. He is one of 228 new members announced today by the academy, which honors some of the world’s most accomplished scholars, scientists, writers, artists and civic, business and philanthropic leaders. The academy is one of the country’s oldest learned societies and independent policy research centers. It convenes leaders from the academic, business and government sectors to respond to the challenges facing - and opportunities available to - the nation and the world.
Rice press release
DSP PhD student AmirAli Aghazadeh, DSP PhD alum Mona Sheikh, Bioengineering professor Rebekah Drezek, Bioengineering alums Allen Chen and Adam Yuh Lin, and DSP professor Richard Baraniuk have been awarded the 2017 Hershel M. Rich Invention Award for their development of Universal Microbial Diagnostics (UMD). UMD is a radically new way to detect and classify bacteria and other microbials that is fast (minutes rather than days) and efficient. (Double congratulations to Dr. Aghazadeh, who defended his PhD thesis today!)
Rice and Baylor College of Medicine Assistant Professor Xaq Pitkow has received an NSF CAREER award for his project "Distributed Nonlinear Neural Computation." The project will develop new quantitative theories that explain brain function as distributed nonlinear neural computations that implement principles of statistical reasoning. To test these theories, he is collaborating with experimentalists to design and interpret neuroscience experiments that quantify both what information about naturalistic tasks is encoded in neural populations and what aspects of that information are actually decoded.