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    Toward Interpretable, Reliable, and Sustainable AI – the 2nd AI Wave

Toward Interpretable, Reliable, and Sustainable AI – the 2nd AI Wave

Rapid advances in artificial intelligence (AI) in the last decade have been primarily attributed to the applications of deep learning (DL) technologies. DL technologies have been widely applied to speech, audio, image, video, computer graphics, 3D models, and chatbots (e.g., ChatGPTs). These advances are viewed as the first AI wave. There are concerns with the first AI wave. DL solutions are a black box (i.e., not interpretable) and vulnerable to adversarial attacks (i.e., unreliable). Besides, the high carbon footprint yielded by large DL networks is a threat to our environment (i.e., not sustainable).

Many researchers are looking for an alternative solution that is interpretable, reliable, and sustainable. This is expected to be the second AI wave. To this end, MCL members have conducted research on green learning (GL) since 2015. Since GL was inspired by DL, the two share some similarities. As time goes by, the two become more and more different. Low carbon footprints, small model sizes, low computational complexity, and mathematical transparency characterize GL. It offers energy-effective solutions in cloud centers and mobile/edge devices. It has three main modules: 1) unsupervised representation learning, 2) supervised feature learning, and 3) decision learning. GL has been successfully applied to a few applications.

The fundamental ideas of GL solution, its demonstrated examples, and its technical outlook were detailed in a recent overview paper by Kuo and Madni, “Green learning: Introduction, examples, and outlook.” Journal of Visual Communication and Image Representation (2022): 103685. We expect to see more GL solutions and applications emerging. All MCL members will be fully devoted to this field in the next decade.

By |March 19th, 2023|News|Comments Off on Toward Interpretable, Reliable, and Sustainable AI – the 2nd AI Wave|

MCL Genealogical Ancestry Series: Johann Friedrich Pfaff

Zhiruo Zhou studied Johann Friedrich Pfaff and shared her study with MCL members in the pre-seminar sharing on Mar 6th, 2023. Johann Friedrich Pfaff was born 22 December 1765 in Stuttgart, Württemberg (now Germany), and died 21 April 1825 in Halle, Saxony (now Germany). He was an influential German mathematician and well known for his work on systems of partial differential equations.

Johann Friedrich was the second son in the family and had six brothers. Although he was probably the one that gained the most fame among his siblings, his brothers were also talented in various fields. His youngest brother Johann Wilhelm Pfaff was a mathematician, and his second youngest brother Christoph Heinrich Pfaff had experiences working on chemistry, medicine, pharmacy and electricity on animals.

Johann Friedrich was born in a family with the tradition of working for the government and therefore took the school named Hohe Karlsschule to receive training for government officials’ sons when he was nine years old. He spent around eleven years there and left when he had completed studies in law which was supposed to be a fitting subject for a civil servant. Driven by the interest in mathematics and encouragement from academy, Johann Friedrich decided to move toward scientific topics and studied mathematics and physics at the University of Göttingen for two years. Then he moved to Berlin to study astronomy and wrote his first paper on astronomy there.

In 1788, Johann Friedrich accepted the election as a professor of mathematics at the University of Helmstedt and stay there until 1810. He spent a lot of efforts on teaching and increasing the number of students of mathematics. He met the student Gauss there and had a good relationship with Gauss. However, [...]

By |March 12th, 2023|News|Comments Off on MCL Genealogical Ancestry Series: Johann Friedrich Pfaff|
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    MCL Research on Mask-Guided Image Synthesis Presented at AAAI-23

MCL Research on Mask-Guided Image Synthesis Presented at AAAI-23

Dr. Rouhsedaghat, a MCL alumna graduated last Summer, recently presented a work[1] on image synthesis related to her PhD thesis in AAAI-23. Here is the presentation summary from Dr. Rouhsedaghat:

We offer a method for one-shot mask-guided image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. Our proposed method, entitled MAGIC , leverages structured gradients from a pre-trained quasi-robust classifier to better preserve the input semantics while preserving its classification accuracy, thereby guaranteeing credibility in the synthesis. Unlike current methods that use complex primitives to supervise the process or use attention maps as a weak supervisory signal, MAGIC aggregates gradients over the input, driven by a guide binary mask that enforces a strong, spatial prior. MAGIC implements a series of manipulations with a single framework achieving shape and location control, intense non-rigid shape deformations, and copy/move operations in the presence of repeating objects and gives users firm control over the synthesis by requiring to simply specify binary guide masks. Our study and findings are supported by various qualitative comparisons with the state-of-the-art on the same images sampled from ImageNet and quantitative analysis using machine perception along with a user survey of 100+ participants that endorse our synthesis quality.

[1]Rouhsedaghat, Mozhdeh, et al. “MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust Classifier.” arXiv preprint arXiv:2209.11549 (2022).

By |March 5th, 2023|News, Research|Comments Off on MCL Research on Mask-Guided Image Synthesis Presented at AAAI-23|

MCL Research on Advanced Knowledge Graph Embedding

Knowledge graph embedding has been intensively studied in recent years. TransE, RotatE, and PairRE are the most representative and effective among distance-based KG embeddings that make use of simple translation, rotation, and scaling operations respectively to represent relations between entities. These models use simple geometric transformations yet achieve relatively good performance in link prediction. However, it is unclear how to leverage the strength of these individual operations and produce a better embedding model. It turns out that we can treat each operation as a basic building block, from which we can build the most effective KG embedding scoring functions. The composition of geometric operators is a well-established tool for image manipulation. It is natural to apply the same strategy to combine translation, rotation, and scaling operators in the 3D space. Our previous work CompoundE has demonstrated that the composition of affine operations can be an effective strategy in designing KG embedding. We are interested to know whether we can achieve even better results by cascading 3D operators.

We propose to extend our previous work by including more affine operations other than only translation, rotation, and scaling in our framework, and moving from 2D transformations to 3D transformations. In addition, we propose to enhance our CompoundE by addressing two critical issues. First, although Compounding operations lead to plenty of model variants, it is still unclear how to systematically search for a scoring function that performs the best for an individual dataset. We propose to use beam search to gradually build more complex scoring functions from simple but effective variants. To speed up the model tuning during the search, we use the pre-trained entity embedding from base models as initialization. Second, although ensemble learning is a popular [...]

By |February 26th, 2023|News|Comments Off on MCL Research on Advanced Knowledge Graph Embedding|

Celebration of SIPI 50th Anniversary

The SIPI 50th Anniversary Celebration was held on the USC Campus on Saturday, February 18th. The USC Signal and Image Processing Institute (SIPI) was one of the first research organizations in the world dedicated to image processing. Image processing work began at USC in 1962, and the Institute was founded in 1972 by William K. Pratt and Harry C. Andrews with support from the Defense Advanced Research Projects Agency (DARPA). SIPI has graduated around 500 PhDs over the last 50 years. As part of the SIPI 50th Anniversary celebration program, the inaugural SIPI Distinguished Alumni Awards have been established to honor USC SIPI graduates who have made professional and technical contributions that bring extraordinary distinction to themselves, the Institute, and the University.

Among the eight awardees, there are three MCL alumnus Ioannis Katsavounidis, Shan Liu and Chao Yang. Dr. Ioannis Katsavounidis won the senior academia award for outstanding contributions to objective video quality metrics and perceptual optimization of streaming videos. Dr. Shan Liu won the mid-career industry award for contributions to and leadership in multimedia technologies, standardization, and applications. Dr. Chao Yang won the junior industry award for contributions to computer vision, machine learning, and artificial intelligence in research publications and in commercial products and services. Congratulations to them!

Many MCL alumnus and current lab members joined the celebration and re-union. We had a great time together refreshing our memories on the past time and bridging different generations which composed of an important part of the MCL family. Happy 50th anniversary to SIPI and look forward to the brighter future for everyone!


Some information credit to:

SIPI 50th Anniversary