32 citations · 128 across the 20 of their papers we have counts for
20 papers
Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
Yanzhao Wu, Ka-Ho Chow, Wenqi Wei +1
Deep neural network ensembles hold the potential of improving generalization performance for complex learning tasks. This paper presents formal analysis and empirical evaluation to…
Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction
Xigang Sun, Jingya Zhou, Ling Liu +1
Predicting information cascade popularity is a fundamental problem in social networks. Capturing temporal attributes and cascade role information (e.g., cascade graphs and cascade…
Securing Distributed SGD against Gradient Leakage Threats
Wenqi Wei, Ling Liu, Jingya Zhou +2
This paper presents a holistic approach to gradient leakage resilient distributed Stochastic Gradient Descent (SGD). First, we analyze two types of strategies for privacy-enhanced…
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
Ka-Ho Chow, Ling Liu, Wenqi Wei +2
Federated Learning (FL) has been gaining popularity as a collaborative learning framework to train deep learning-based object detection models over a distributed population of clie…
Adaptive Deep Neural Network Inference Optimization with EENet
Fatih Ilhan, Ka-Ho Chow, Sihao Hu +9
Well-trained deep neural networks (DNNs) treat all test samples equally during prediction. Adaptive DNN inference with early exiting leverages the observation that some test exampl…
Metric Learning as a Service with Covariance Embedding
Imam Mustafa Kamal, Hyerim Bae, Ling Liu
With the emergence of deep learning, metric learning has gained significant popularity in numerous machine learning tasks dealing with complex and large-scale datasets, such as inf…
Selecting and Composing Learning Rate Policies for Deep Neural Networks
Yanzhao Wu, Ling Liu
The choice of learning rate (LR) functions and policies has evolved from a simple fixed LR to the decaying LR and the cyclic LR, aiming to improve the accuracy and reduce the train…
Gradient Leakage Attack Resilient Deep Learning
Wenqi Wei, Ling Liu
Gradient leakage attacks are considered one of the wickedest privacy threats in deep learning as attackers covertly spy gradient updates during iterative training without compromis…
Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering
Zhongwei Xie, Ling Liu, Yanzhao Wu +2
This paper introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineer…
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding
Jingya Zhou, Ling Liu, Wenqi Wei +1
Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Over doz…
Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images
Zhongwei Xie, Ling Liu, Lin Li +1
This paper presents a three-tier modality alignment approach to learning text-image joint embedding, coined as JEMA, for cross-modal retrieval of cooking recipes and food images. T…
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning
Zhongwei Xie, Ling Liu, Lin Li +1
This paper introduces a two-phase deep feature calibration framework for efficient learning of semantics enhanced text-image cross-modal joint embedding, which clearly separates th…
Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
Zhongwei Xie, Ling Liu, Yanzhao Wu +2
It is widely acknowledged that learning joint embeddings of recipes with images is challenging due to the diverse composition and deformation of ingredients in cooking procedures.…
Parallel Detection for Efficient Video Analytics at the Edge
Yanzhao Wu, Ling Liu, Ramana Kompella
Deep Neural Network (DNN) trained object detectors are widely deployed in many mission-critical systems for real time video analytics at the edge, such as autonomous driving and vi…
Computational Morphology with Neural Network Approaches
Ling Liu
Neural network approaches have been applied to computational morphology with great success, improving the performance of most tasks by a large margin and providing new perspectives…
Promoting High Diversity Ensemble Learning with EnsembleBench
Yanzhao Wu, Ling Liu, Zhongwei Xie +3
Ensemble learning is gaining renewed interests in recent years. This paper presents EnsembleBench, a holistic framework for evaluating and recommending high diversity and high accu…
Efficient Orchestration of Host and Remote Shared Memory for Memory Intensive Workloads
Juhyun Bae, Gong Su, Arun Iyengar +2
Since very few contributions to the development of an unified memory orchestration framework for efficient management of both host and remote idle memory have been made, we present…
Delivering Scientific Influence Analysis as a Service on Research Grants Repository
Yuming Wang, Yanbo Long, Lai Tu +1
Research grants have played an important role in seeding and promoting fundamental research projects worldwide. There is a growing demand for developing and delivering scientific i…
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks
Ka-Ho Chow, Wenqi Wei, Yanzhao Wu +1
Deep neural networks (DNNs) have demonstrated impressive performance on many challenging machine learning tasks. However, DNNs are vulnerable to adversarial inputs generated by add…
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
Yanzhao Wu, Ling Liu, Juhyun Bae +6
Learning Rate (LR) is an important hyper-parameter to tune for effective training of deep neural networks (DNNs). Even for the baseline of a constant learning rate, it is non-trivi…