Study Group Talk, University of California, Berkeley, U.S.
This talk introduces a theoretical framework for multi-source transferability covering distance, sample sizes, and task complexity at the same time. It extends to continuous data via training neural networks. Futhermore, a consistent algorithm is proposed that works in applications.
Conference Talk, International Conference on Communications (ICC) 2022, Gangnam-gu, Seoul, South Korea
For signal detection tasks in wireless communications, most of the existing algorithms either ignore the co-channel interference or treat it as Gaussian noise, which may result in unsatisfactory accuracy when the interference is non-negligible and complex-distributed. When neural networks are motivated in the design of the detectors, difficulty arises in the training due to the fact that there are few accessible pilots in each packet. In this paper, we consider a data-driven detector based on multi-source transfer learning (MSTL) for signal detection in a fading channel with interference. The MSTL detector transfers channel knowledge of previous packets into the latest detection. In particular, we consider a linear combination of the pilots and historical symbols in the distribution space, and design the optimal combination coefficients based on the number of those symbols as well as distributions similarity. Numerical simulations on a Gauss-Markov flat Rayleigh fading channel with co-channel interference validate the advantages of our algorithms, compared with several existing training schemes including directly applying the fully connected deep neural network (FCDNN) and conventional linear minimum mean square error (LMMSE) detector.
Talk, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, China
This is a review talk. Effectiveness of deep learning is often ascribed to the ability of deep networks to learn representations. Using established principles from Statistics and Information Theory, we introduce the desirable properties of representations and the relationships between them. It is shown that invariance to nuisance factors in a deep neural network is equivalent to information minimality of the learned representation. On the other hand, we want to find parameters (weights) that yield good generalization, so we focus on the information in the weights which can be controlled by implicit or explicit regularization. With some additional assumptions, we get a connection between information in the representations and information in the weights.
Presentation, Guangzhou, Guangzhou, China
This talk introduces some interesting applications of the Game Theory and Probability Theory in our life. To most of us, we don’t want to miss the marriage if we find the person who we think is the best partner.
You may wonder which age to marry achieves the most rationality. Now we can try to solve out this problem. It is supposed that you started dating when you’re 18, and ideally you’d like to be married by the time that you’re 35.
Then you have to choose a percentage that you should reject all your partners to step into your marriage before this percentage of persons you have met. And then you should pick the next person that comes along that is better than everybody you’ve seen before. According to the Optimal Stopping Theory, the best strategy is to reject everybody as serious marriage potential in the first 37％ period. That is about 24.3 years old. So on the mathematical theoretic level, you have the highest probability of meeting the best partner if we start to consider marriage after the 37% time point. Of course, there are many other variables during the period we haven’t consider. If you are interested, you could make a further study of this model. But anyway, it provides us with a rational way to consider our marriage age.
Presentation, University of California, Berkeley, Berkeley, CA
The Free Speech Movement (FSM) was a massive, long-lasting student protest which took place during the 1964–65 academic year on the campus of the University of California, Berkeley. The Movement was informally under the central leadership of Berkeley graduate student Mario Savio. In fact, the Free Speech Movement is a result of the interaction of history and current affairs, international and American self development. From the east to the west, the wave of world revolution swept the country and shacked the whole world. This talk is divided into 4 parts: background, process, Mario Savio and influence.
This is a presentation in UC Berkeley with Jialin Yang, Qiuyi Zhan and Puzheng Zhang. It is worth mentioning that one of our presenters Qiuyi Zhan is also an intern of the well-known《令人心动的offer第二季》 in 2020-2021.