UCI and Disney Research scientists develop AI-enhanced video compression model

Standard
Assistant professor of computer Science Stephan Mandt photo: Steve Zylius/UCI

A new artificial intelligence-enhanced video compression model developed by computer scientists at the University of California, Irvine and Disney Research has demonstrated that deep learning can compete against established video compression technology.

Unveiling their work in December at the Conference on Neural Information Processing Systems in Vancouver, British Columbia, the UCI/Disney Research team members showed that their compressor – while still in an early phase – yielded less distortion and significantly smaller bits-per-pixel rates than classical coding-decoding algorithms such as H.265 when trained on specialized video content and achieved comparable results on downscaled, publicly available YouTube videos.

Read more: https://www.ics.uci.edu/community/news/view_news?id=1714

Upgrading the UCI ML Repository

Standard

The UCI Machine Learning Repository has been a tremendous resource for empirical and methodological research in machine learning for decades. Yet with the growing number of machine learning (ML) research papers, algorithms and datasets, it is becoming increasingly difficult to track the latest performance numbers for a particular dataset, identify suitable datasets for a given task, or replicate the results of an algorithm run on a particular dataset. To address this issue, CML Professors Sameer Singh and Padhraic Smyth along with Philip Papadopoulos, Director of UCI’s Research Cyberinfrastructure Center (RCIC), have planned a “next-generation” upgrade. The trio was recently awarded $1.8 million for their NSF grant, “Machine Learning Democratization via a Linked, Annotated Repository of Datasets.”

AI/NLP Research Partnership with Allen Institute for AI (AI2)

Image

Professor Sameer Singh and his group have developed a thriving partnership working with researcher Dr. Matt Gardner and colleagues from the Allen Institute for AI (AI2), producing a series of high-profile papers in the past several months on topics such as language modeling and automated question answering systems. AI2 is providing funding to support graduate student researchers who work closely with AI2 researchers co-located in the Computer Science Department in Donald Bren Hall.

Research funding from Qualcomm AI/ML Research Labs

Standard

Qualcomm Inc. has provided gift funding of $255,000 to Computer Science Professors Charless FowlkesStephan Mandt and Padhraic Smyth. This funding will support Ph.D. students involved in basic research projects across the three groups on topics related to the development of new theories and algorithms in the areas of computer vision and machine learning.

The funded projects will involve collaborations with Qualcomm’s rapidly expanding research and development work in artificial intelligence, with a particular focus on Qualcomm AI/ML research labs in San Diego and Amsterdam.

Faculty Positions at UC Irvine

Standard

Faculty Positions at UC Irvine

Application deadline: Jan 15th, 2019 (Applications received by January 1, 2019 will receive fullest consideration.)

Apply online at: https://recruit.ap.uci.edu/apply/JPF04950

The Department of Computer Science in the Donald Bren School of Information and Computer Sciences (ICS) at the University of California, Irvine (UCI) invites applications for multiple tenure-track assistant professor or tenured associate/full professor positions beginning July 1, 2019. The Department is interested in individuals with research interests in all aspects of algorithms, artificial intelligence, machine learning, and theory of computing. One opening is targeted at individuals whose computer science expertise aligns with the growing UCI Data Science Initiative.

Two new NSF awards in Machine Learning for Sameer Singh

Standard

Congratulations to Professor Sameer Singh for receiving two multi-year research awards from the National Science Foundation (NSF). Under the first grant, Sameer and his research team will develop new algorithms to better explain why classifiers make certain decisions, increasing user trust in such models. The second grant focuses on the development of new approached for extracting multimodal information from documents, such as text, images, numbers, and databases, with the goal of automatically creating new knowledge bases from relatively unstructured written documents.

Workshop for the Philosophy of Machine Learning

Standard

UC Irvine held a very successful workshop on the “Philosophy of Machine Learning” on March 17th & 18th, in the Donald Bren Hall Conference Center (DBH 6011). More information may be found at: https://philmachinelearning.wordpress.com/program/.

Organizers: Andrew Holbrook (Statistics) and Kino Zhao (Logic and Philosophy of Science)

Sponsors: UCI School of Social Sciences; UCI Dept of Logic & Philosophy of Science; UCI Data Science Initiative; and Dr. Babak Shahbaba (via NSF).

PhD Students win Best Poster Awards

Standard

Congratulations to CML graduate students for recent poster awards at the 2017 Southern California Machine Learning Symposium held at USC.  Zhengli Zhao and Dheeru Dua (with advisor Sameer Singh) won best poster award for their work on generating natural adversarial examples and Eric Nalisnick (with advisor Padhraic Smyth) won honorable mention for his work on boosting variational inference. There were about 50 student posters presented and over 250 machine learning researchers attended the event. Next SoCal ML Symposium is scheduled for Fall 2018, to be hosted by UCLA.

New Faculty Member: Erik Sudderth

Standard

We are delighted to welcome new faculty member Erik Sudderth to the Center. Erik recently joined the Department of Computer Science at UCI as an Associate Professor. He is well-known for his research in machine learning, with interests in topics such as graphical models and Bayesian nonparametric methods. Erik’s research group is also active in the application of these ideas to artificial intelligence, vision, and the natural and social sciences. More information about Erik and his research group is available at Erik’s Webpage.