Xue (Steve) Liu

Can (Sam) Chen

Doctorat - McGill

Co-superviseur⋅e :

Chris Pal

chencan421@gmail.com

Ralph Cui

Postdoctorat

chengming.hu@mail.mcgill.ca

Google Scholar

Lyu Fuyuan

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Yili Jin

Doctorat - McGill

Google Scholar

Zonglun Li

Doctorat - McGill

Xuan Li

Doctorat - McGill

Junliang Luo

Doctorat - McGill

rishabh.thaney@mail.mcgill.ca

Shaoxiang Qin

Doctorat - McGill

Alejandro Salinas-Medina

Doctorat - McGill

Maîtrise recherche - McGill

Dheeraj Vattikonda

Maîtrise recherche - McGill

Haolun Wu

Doctorat - McGill

Co-superviseur⋅e :

Fernando Diaz

Ye Yuan

Doctorat - McGill

ye.yuan3@mail.mcgill.ca

Google Scholar

Dun Yuan

Maîtrise recherche - McGill

Gaspard Zhao

Postdoctorat - McGill

Co-superviseur⋅e :

Gregory Dudek

Co-enseignement sensible à l’importance pour optimisation hors-ligne fondée sur un modèle

Shuhao Zheng

Doctorat - McGill

Billets de blogue

1 juillet 2024

par

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger

Xue Liu

Lire l'article

Bidirectional Learning for Offline Model-based Optimization

20 septembre 2023

Apprentissage bidirectionnel pour l’optimisation hors ligne basée sur un modèle

par

Can Chen

Yingxue Zhang

Xue Liu

Mark Coates

Lire l'article

Publications

Structure-aware protein self-supervised learning

Can Chen

Jingbo Zhou

Fan Wang

Dejing Dou

2023-04-13

Bioinformatics (publié)

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

Haolun Wu

Yingxue Zhang

Chen Ma

Wei Guo

Ruiming Tang

Mark Coates

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and i… (voir plus)tem representations. Many state-of-the-art (SOTA) methods fuse different sources of information (user, item, knowledge graph, tags, etc.) into a graph and use Graph Neural Networks (GNNs) to introduce the auxiliary information through the message passing paradigm. In this work, we seek an alternative framework that is light and effective through self-supervised learning across different sources of information, particularly for the commonly accessible item tag information. We use a self-supervision signal to pair users with the auxiliary information (tags) associated with the items they have interacted with before. To achieve the pairing, we create a proxy training task. For a given item, the model predicts which is the correct pairing between the representations obtained from the users that have interacted with this item and the tags assigned to it. This design provides an efficient solution, using the auxiliary information directly to enhance the quality of user and item embeddings. User behavior in recommendation systems is driven by the complex interactions of many factors behind the users’ decision-making processes. To make the pairing process more fine-grained and avoid embedding collapse, we propose a user intent-aware self-supervised pairing process where we split the user embeddings into multiple sub-embedding vectors. Each sub-embedding vector captures a specific user intent via self-supervised alignment with a particular cluster of tags. We integrate our designed framework with various recommendation models, demonstrating its flexibility and compatibility. Through comparison with numerous SOTA methods on seven real-world datasets, we show that our method can achieve better performance while requiring less training time. This indicates the potential of applying our approach on web-scale datasets.

2023-04-03

2023 IEEE 39th International Conference on Data Engineering (ICDE) (publié)

Ternary Quantization: A Survey

Danyang Liu

Inference time, model size, and accuracy are critical for deploying deep neural network models. Numerous research efforts have been made to … (voir plus)compress neural network models with faster inference and higher accuracy. Pruning and quantization are mainstream methods to this end. During model quantization, converting individual float values of layer weights to low-precision ones can substantially reduce the computational overhead and improve the inference speed. Many quantization methods have been studied, for example, vector quantization, low-bit quantization, and binary/ternary quantization. This survey focuses on ternary quantization. We review the evolution of ternary quantization and investigate the relationships among existing ternary quantization methods from the perspective of projection function and optimization methods.

2023-03-02

ArXiv (prépublication)

Design and Implementation of Smooth Renewable Power in Cloud Data Centers

Xinxin Liu

Yu Hua

Ling Yang

Yuanyuan Sun

The renewable power has been widely used in modern cloud data centers, which also produce large electricity bills and the negative impacts o… (voir plus)n environments. However, frequent fluctuation and intermittency of renewable power often cause the challenges in terms of the stability of both electricity grid and data centers, as well as decreasing the utilization of renewable power. Existing schemes fail to alleviate the renewable power fluctuation, which is caused by the essential properties of renewable power. In order to address this problem, we propose an efficient and easy-to-use smooth renewable power-aware scheme, called Smoother, which consists of Flexible Smoothing (FS) and Active Delay (AD). First, in order to smooth the fluctuation of renewable power, FS carries out the optimized charge/discharge operation via computing the minimum variance of the renewable power that is supplied to data centers per interval. Second, AD improves the utilization of renewable power via actively adjusting the execution time of deferrable workloads. Extensive experimental results via examining the traces of real-world data centers demonstrate that Smoother significantly reduces the negative impact of renewable power fluctuations on data centers and improves the utilization of renewable power by 250.88 percent on average. We have released the source codes for public use.

2023-03-01

IEEE Transactions on Cloud Computing (publié)

Learning From FM Communications: Toward Accurate, Efficient, All-Terrain Vehicle Localization

Xi Chen

Qiao Xiang

Linghe Kong

Huisan Xu

Vehicle localization service is a fundamental component of intelligent transportation systems. The widely used satellite navigation systems … (voir plus)perform poorly in urban areas because the lines of sight to satellites are blocked by complex terrain characteristics, e.g., buildings, elevated streets and interchanges. In this paper, we design RadioLoc, a novel system achieving accurate, efficient, all-terrain vehicle localization with two key design points. First, RadioLoc harvests the frequency modulation (FM) signal, which has higher availability than satellite signal in complex terrains, as the signal source for localization. Second, RadioLoc integrates modern machine learning techniques into the processing of FM signals to efficiently learn the accurate vehicle localization in all-terrain environments. We validate the feasibility of FM-based vehicle localization and corresponding challenges and practical issues via field tests (e.g., signal distortion, signal inconsistency and limited in- vehicle radio bandwidth), and develop a series of advanced techniques in RadioLoc to address them, including adaptive batching, frequency sweeping, a novel multipath delay spread filter, a reconstructive PCA denoiser and a tailored FM feature extractor. We then develop a generic, modular localization module in RadioLoc, and design different learning-based 3D position identification algorithms for this module. We implement a prototype of RadioLoc and perform extensive field experiments to evaluate its efficiency and efficacy. Results show that (1) RadioLoc achieves a real-time localization latency of less than 100 milliseconds; (2) RadioLoc achieves a worst-case localization accuracy of 99.6% even in an underground parking lot, and (3) the horizontal error of RadioLoc is only one sixth of a dedicated GPS device even when the vehicle is moving at a high-speed (i.e., 80 km/h) in a complex highway scenario.

2023-02-01

IEEE/ACM Transactions on Networking (publié)

Hyperspherical Quantization: Toward Smaller and More Accurate Models

Dan Liu

Xi Chen

Chen Ma

Model quantization enables the deployment of deep neural networks under resource-constrained devices. Vector quantization aims at reducing t… (voir plus)he model size by indexing model weights with full-precision embeddings, i.e., codewords, while the index needs to be restored to 32-bit during computation. Binary and other low-precision quantization methods can reduce the model size up to 32×, however, at the cost of a considerable accuracy drop. In this paper, we propose an efficient framework for ternary quantization to produce smaller and more accurate compressed models. By integrating hyperspherical learning, pruning and reinitialization, our proposed Hyperspherical Quantization (HQ) method reduces the cosine distance between the full-precision and ternary weights, thus reducing the bias of the straight-through gradient estimator during ternary quantization. Compared with existing work at similar compression levels (~30×, ~40×), our method significantly improves the test accuracy and reduces the model size.

2023-01-02

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)

Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images

Yufei Cui

Ziquan Liu

Xiangyu Liu

Cong Wang

Tei-Wei Kuo

Chun Jason Xue

Antoni B. Chan

Multiple instance learning (MIL) is a popular weakly-supervised learning model on the whole slide image (WSI) for AI-assisted pathology diag… (voir plus)nosis. The recent advance in attention-based MIL allows the model to find its region-of-interest (ROI) for interpretation by learning the attention weights for image patches of WSI slides. However, we empirically find that the interpretability of some related methods is either untrustworthy as the principle of MIL is violated or unsatisfactory as the high-attention regions are not consistent with experts’ annotations. In this paper, we propose Bayes-MIL to address the problem from a probabilistic perspective. The induced patch-level uncertainty is proposed as a new measure of MIL interpretability, which outperforms previous methods in matching doctors annotations. We design a slide-dependent patch regularizer (SDPR) for the attention, imposing constraints derived from the MIL assumption, on the attention distribution. SDPR explicitly constrains the model to generate correct attention values. The spatial information is further encoded by an approximate convolutional conditional random field (CRF), for better interpretability. Experimental results show Bayes-MIL outperforms the related methods in patch-level and slide-level metrics and provides much better interpretable ROI on several large-scale WSI datasets.

2023-01-01

ICLR (publié)

dblp.uni-trier.de

Bayes-MIL: A New Probabilistic Perspective on Attention-based Multiple Instance Learning for Whole Slide Images

Yufei Cui

Ziquan Liu

Xiangyu Liu

Cong Wang

Tei-Wei Kuo

Chun Jason Xue

Antoni Bert Chan

2023-01-01

International Conference on Learning Representations (published)

dblp.uni-trier.de

A Distributed Pricing Strategy for Edge Computation Offloading Optimization in Autonomous Driving

Jie Tang

Weilin Zhu

Xiaoming Li

Shaoshan Liu

The increase of on-vehicle applications has brought explosive computation demands to autonomous vehicles and overwhelmed their limited onboa… (voir plus)rd resources. Edge computing can offload application load and effectively alleviate this problem. However, the introduction of edge computing faces significant challenges, including the considerable amount of resource contention due to the scarcity of edge resources and the competition among edge computing resource providers to earn usersâ€™ services requests. We notice that the problem is not purely technical as solutions for these two problems can become conflicting to each other. In this paper, we propose a distributed pricing strategy to achieve full use of computing resources at the edge and maximize the revenue of service operators, both with guaranteed quality-of-service of on-vehicle applications. More specifically, we first use the multi-leader multi-follower Stackelberg game theory to model the pricing of on-vehicle task offloading under edge computing. Next, we propose a distributed pricing strategy to enable edge servers to adjust their local price distributions so that edge servers can bargain with offloading requesters independently. Experimental results confirm that the proposed distributed pricing strategy can provide more optimized server computing resource utilization while guaranteeing the performance of in-vehicle applications.

2023-01-01

IEEE Network (publié)

A Survey of Diversification Metrics and Approaches in Retrieval Systems: From the Perspective of Search and Recommendation

Haolun Wu

Yansen Zhang

Chen Ma

Fuyuan Lyu

Fernando Diaz

Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (voir plus)and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems

2023-01-01

(publié)

www.semanticscholar.org

Dynamic Consolidation for Continual Learning

Hang Li

Chen Ma

Xi Chen

Abstract Training deep learning models from a stream of nonstationary data is a critical problem to be solved to achieve general artificial … (voir plus)intelligence. As a promising solution, the continual learning (CL) technique aims to build intelligent systems that have the plasticity to learn from new information without forgetting the previously obtained knowledge. Unfortunately, existing CL methods face two nontrivial limitations. First, when updating a model with new data, existing CL methods usually constrain the model parameters within the vicinity of the parameters optimized for old data, limiting the exploration ability of the model; second, the important strength of each parameter (used to consolidate the previously learned knowledge) is fixed and thus is suboptimal for the dynamic parameter updates. To address these limitations, we first relax the vicinity constraints with a global definition of the important strength, which allows us to explore the full parameter space. Specifically, we define the important strength as the sensitivity of the global loss function to the model parameters. Moreover, we propose adjusting the important strength adaptively to align it with the dynamic parameter updates. Through extensive experiments on popular data sets, we demonstrate that our proposed method outperforms the strong baselines by up to 24% in terms of average accuracy.

2022-12-14

Neural Computation (publié)

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

Haolun Wu

Chen Ma

Yingxue Zhang

Ruiming Tang

Mark Coates

2022-10-17

Proceedings of the 31st ACM International Conference on Information & Knowledge Management (publié)