Young Scholar

Intersections are essential road infrastructures for traffic in modern metropolises; however, they can also be the bottleneck of traffic flows due to traffic incidents or the absence of traffic coordination mechanisms such as traffic lights. Thus, various control and coordination mechanisms that are beyond traditional control methods have been proposed to improve the efficiency of intersection traffic. Amongst these methods, the control of foreseeable hybrid traffic that consists of human-driven vehicles (HVs) and robot vehicles (RVs) has recently emerged. We propose a decentralized reinforcement learning approach for the control and coordination of hybrid traffic at real-world, complex intersections–a topic that has not been previously explored. Comprehensive experiments are conducted to show the effectiveness of our approach. We show that using 5% RVs, we can prevent congestion formation inside the intersection under the actual traffic demand of 700 vehicles per hour. When there exist more than 50% RVs in traffic, our method starts to outperform traffic signals on the average waiting time of all vehicles at the intersection.

Deep neural networks are a powerful tool for the characterization of quantum states. Existing networks are typically trained with experimental data gathered from the specific quantum state that needs to be characterized. In this talk, Mr. Yan Zhu, from Department of Computer Science, will introduce a model of network that can be trained with classically simulated data from a fiducial set of states and measurements, and can later be used to characterize quantum states that share structural similarities with the states in the fiducial set. With little guidance of quantum physics, the network builds its own data-driven representation of quantum states, and then uses it to predict the outcome statistics of quantum measurements that have not been performed yet. The state representation produced by the network can also be used for tasks beyond the prediction of outcome statistics, including clustering of quantum states and identification of different phases of matter.

In computer science, a graph is a network modeling objects and their unique interactions. The graph learning model is a specialized machine learning model that learns on graphs. Similar to traditional machine learning models, a well-performed graph learning model can capture the global data distribution with sufficient and unbiased training data. However, in a distributed subgraph system, most data owners only possess small amounts of the data (small subgraphs) in their local systems and can have unpredictable biases.
In this talk, the speaker will introduce this novel yet realistic setting – subgraph federated learning, which aims to let distributed data owners collaboratively train a powerful and generalized graph learning model without directly sharing their subgraphs. Towards this setting, two major techniques are proposed by the research team. (1) FedSage, which trains a GraphSage model based on FedAvg to integrate node features, link structures, and task labels on multiple local subgraphs; (2) FedSage+, which trains a missing neighbor generator along FedSage to deal with missing links across local subgraphs. Empirical results and theoretical analysis of proposed models respectively demonstrate the effectiveness and prove the generalization ability.

Distributed training using multiple devices (i.e., GPU servers) has been widely adopted for learning DNN models over large datasets. However, the performance of large-scale distributed training tends to be far from linear speed-up in practice. Given the complexity of distributed systems, it is challenging to identify the root cause(s) of inefficiency and exercise effective performance optimizations when unexpected low training speed occurs. To date, there exists no software tool which diagnoses performance issues and helps expedite distributed DNN training, while the training can be run using different machine learning frameworks. This paper proposes dPRO, a toolkit that includes: (1) an efficient profiler that collects runtime traces of distributed DNN training across multiple frameworks, especially fine-grained communication traces, and constructs global data flow graphs including detailed communication operations for accurate replay; (2) an optimizer that effectively identifies performance bottlenecks and explores optimization strategies (from computation, communication and memory aspects) for training acceleration. We implement dPRO on multiple deep learning frameworks (PyTorch, TensorFlow, MXNet) and representative communication schemes (AllReduce and Parameter Server architecture). Extensive experiments show that dPRO predicts performance of distributed training in various settings with<5% errors in most cases and finds optimization strategies with up to87.1%speed-up over the baselines.