An Ensemble Of Epoch-Wise Empirical Bayes For Few-Shot Learning,
2020
Max Planck Institute for Informatics
An Ensemble Of Epoch-Wise Empirical Bayes For Few-Shot Learning, Yaoyao Liu, Bernt Schiele, Qianru Sun
Research Collection School Of Computing and Information Systems
Few-shot learning aims to train efficient predictive models with a few examples. The lack of training data leads to poor models that perform high-variance or low-confidence predictions. In this paper, we propose to meta-learn the ensemble of epoch-wise empirical Bayes models (E3BM) to achieve robust predictions. “Epoch-wise'' means that each training epoch has a Bayes model whose parameters are specifically learned and deployed. ”Empirical'' means that the hyperparameters, e.g., used for learning and ensembling the epoch-wise models, are generated by hyperprior learners conditional on task-specific data. We introduce four kinds of hyperprior learners by considering inductive vs. transductive, and epoch-dependent …
An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks,
2020
Hong Kong Baptist University
An Attention-Based Rumor Detection Model With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how …
Learning Transferrable Parameters For Long-Tailed Sequential User Behavior Modeling,
2020
Zhejiang University
Learning Transferrable Parameters For Long-Tailed Sequential User Behavior Modeling, Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Sequential user behavior modeling plays a crucial role in online user-oriented services, such as product purchasing, news feed consumption, and online advertising. The performance of sequential modeling heavily depends on the scale and quality of historical behaviors. However, the number of user behaviors inherently follows a long-tailed distribution, which has been seldom explored. In this work, we argue that focusing on tail users could bring more benefits and address the long tails issue by learning transferrable parameters from both optimization and feature perspectives. Specifically, we propose a gradient alignment optimizer and adopt an adversarial training scheme to facilitate knowledge transfer …
Dual-Dropout Graph Convolutional Network For Predicting Synthetic Lethality In Human Cancers,
2020
Guangdong University of Technology
Dual-Dropout Graph Convolutional Network For Predicting Synthetic Lethality In Human Cancers, Ruichu Cai, Xuexin Chen, Yuan Fang, Min Wu, Yuexing Hao
Research Collection School Of Computing and Information Systems
Motivation: Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture …
Rethinking Pruning For Accelerating Deep Inference At The Edge,
2020
Beijing University of Aeronautics and Astronautics (Beihang University)
Rethinking Pruning For Accelerating Deep Inference At The Edge, Dawei Gao, Xiaoxi He, Zimu Zhou, Yongxin Tong, Ke Xu, Lothar Thiele
Research Collection School Of Computing and Information Systems
There is a growing trend to deploy deep neural networks at the edge for high-accuracy, real-time data mining and user interaction. Applications such as speech recognition and language understanding often apply a deep neural network to encode an input sequence and then use a decoder to generate the output sequence. A promising technique to accelerate these applications on resource-constrained devices is network pruning, which compresses the size of the deep neural network without severe drop in inference accuracy. However, we observe that although existing network pruning algorithms prove effective to speed up the prior deep neural network, they lead to …
Adaptive Task Sampling For Meta-Learning,
2020
Singapore Management University
Adaptive Task Sampling For Meta-Learning, Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks. The key idea of meta-learning for few-shot classification is to mimic the few-shot situations faced at test time by randomly sampling classes in meta-training data to construct fewshot tasks for episodic training. While a rich line of work focuses solely on how to extract meta-knowledge across tasks, we exploit the complementary problem on how to generate informative tasks. We argue that the randomly sampled tasks could be sub-optimal and uninformative (e.g., the task of classifying “dog” from “laptop” is often trivial) to the meta-learner. …
Feature Pyramid Transformer,
2020
Nanjing University of Science and Technology
Feature Pyramid Transformer, Dong Zhang, Hanwang Zhang, Jinhui Tang, Meng Wang, Xian-Sheng Hua, Qianru Sun
Research Collection School Of Computing and Information Systems
Feature interactions across space and scales underpin modern visual recognition systems because they introduce beneficial visual contexts. Conventionally, spatial contexts are passively hidden in the CNN’s increasing receptive fields or actively encoded by non-local convolution. Yet, the non-local spatial interactions are not across scales, and thus they fail to capture the non-local contexts of objects (or parts) residing in different scales. To this end, we propose a fully active feature interaction across both space and scales, called Feature Pyramid Transformer (FPT). It transforms any feature pyramid into another feature pyramid of the same size but with richer contexts, by using …
A Unified Framework For Sparse Online Learning,
2020
Tencent AL Lab
A Unified Framework For Sparse Online Learning, Peilin Zhao, Dayong Wong, Pengcheng Wu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which suffers from low efficiency and poor scalability. To tackle these challenges, we investigate a unified online learning framework for the big data stream classification task. Different from the existing online data stream classification techniques, we propose a unified Sparse Online Classification (SOC) framework. Based on SOC, we derive a second-order online learning algorithm and a cost-sensitive sparse online …
Accelerating Exact Constrained Shortest Paths On Gpus,
2020
National University of Singapore
Accelerating Exact Constrained Shortest Paths On Gpus, Shengliang Lu, Bingsheng He, Yuchen Li, Hao Fu
Research Collection School Of Computing and Information Systems
The recently emerging applications such as software-defined networks and autonomous vehicles require efficient and exact solutions for constrained shortest paths (CSP), which finds the shortest path in a graph while satisfying some user-defined constraints. Compared with the common shortest path problems without constraints, CSP queries have a significantly larger number of subproblems. The most widely used labeling algorithm becomes prohibitively slow and impractical. Other existing approaches tend to find approximate solutions and build costly indices on graphs for fast query processing, which are not suitable for emerging applications with the requirement of exact solutions. A natural question is whether and …
A Generalised Bound For The Wiener Attack On Rsa,
2020
Singapore Management University
A Generalised Bound For The Wiener Attack On Rsa, Willy Susilo, Joseph Tonien, Guomin Yang
Research Collection School Of Computing and Information Systems
Since Wiener pointed out that the RSA can be broken if the private exponent d is relatively small compared to the modulus N, it has been a general belief that the Wiener attack works for d
Aim 2020 Challenge On Video Extreme Super-Resolution: Methods And Results,
2020
Singapore Management University
Aim 2020 Challenge On Video Extreme Super-Resolution: Methods And Results, D. Fuoli, Zhiwu Huang, S. Gu, R. Timofte, A. Raventos, A. Esfandiari, S. Karout, X. Xu, X. Li, X. Xiong, J. Wang, Michelini P. Navarrete, W. Zhang, D. Zhang, H. Zhu, D. Xia, H. Chen, J. Gu, Z. Zhang, T. Zhao
Research Collection School Of Computing and Information Systems
This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020. Common scaling factors for learned video super-resolution (VSR) do not go beyond factor 4. Missing information can be restored well in this region, especially in HR videos, where the high-frequency content mostly consists of texture details. The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos. A single pixel in the lowresolution (LR) domain corresponds to 256 pixels in the high-resolution (HR) domain. …
Measuring Privacy Concerns With Government Surveillance And Right-To-Be-Forgotten In Nomological Net Of Trust And Willingness-To-Share,
2020
Singapore Management University
Measuring Privacy Concerns With Government Surveillance And Right-To-Be-Forgotten In Nomological Net Of Trust And Willingness-To-Share, Gaurav Bansal, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
In the post Snowden revelations era, concerns related to government surveillance and oversight have come to the forefront. The ability of the Internet to remember “everything” (or forget anything) also raises a privacy concern associated with the “right to be forgotten”. Hence, in this paper, we propose and examine privacy concerns by extending the Hong and Thong’s (2013) model with the addition of two dimensions: right to be forgotten as well as government surveillance and oversight. We tested two different measurement models using privacy concerns as a second-order and a third-order construct within a nomological net that includes trusting beliefs …
Maia And Admonita: Mandatory Integrity Control Language And Dynamic Trust Framework For Arbitrary Structured Data,
2020
Western Michigan University
Maia And Admonita: Mandatory Integrity Control Language And Dynamic Trust Framework For Arbitrary Structured Data, Wassnaa Al-Mawee
Dissertations
The expansion of attacks against information systems of companies that operate nuclear power stations and other energy facilities in the United States and other countries, are noticeable with potential catastrophic real-world implications. Data integrity is a fundamental component of information security. It refers to the accuracy and the trustworthiness of data or resources. Data integrity within information systems becomes an important factor of security protection as the data becomes more integrated and crucial to decision-making. The security threats brought by human errors whether, malicious or unintentional, such as viruses, hacking, and many other cybersecurity threats, are dangerous and require mandatory …
Bootstrapping Web Archive Collections From Micro-Collections In Social Media,
2020
Old Dominion University
Bootstrapping Web Archive Collections From Micro-Collections In Social Media, Alexander C. Nwala
Computer Science Theses & Dissertations
In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. The result of this is a shortage of curators, a lack of Web archive collections for various important news events, and a need for an automatic system for generating seeds. …
Love A Restaurant? Swipe Right On Foodrecce,
2020
Singapore Management University
Love A Restaurant? Swipe Right On Foodrecce, Hady W. Lauw, Smu Office Of Research
Research@SMU Infographics
A bunch of your friends wants to meet for dinner, but nobody can agree on where and what to eat? FoodRecce can help! FoodRecce is an app, developed under the Preferred.AI initiative, that provides recommendations on restaurants based on users' locations and past preferences.
Specialization: Do Your Job Well Helping Students Who Are Considering A Career In Programming Know How To Invest Their Time.,
2020
Brigham Young University, Provo
Specialization: Do Your Job Well Helping Students Who Are Considering A Career In Programming Know How To Invest Their Time., Scott Pulley
Marriott Student Review
The article examines the effects of specialization on the hiring process for undergraduates studying programming whether in information systems or computer science.
Novel Technique To Analyze The Effects Of Cognitive And Non-Cognitive Predictors On Students Course Withdrawal In College,
2020
The University of Texas at Tyler
Novel Technique To Analyze The Effects Of Cognitive And Non-Cognitive Predictors On Students Course Withdrawal In College, Mohammed Ali
Technology Faculty Publications and Presentations
A novel technique was applied to a college student database to identify the cognitive and non-cognitive factors that predict college students’ course withdrawal behaviors. Predictors such as high school grade point average (HSGPA), standardized test scores (ACT–American College Test or SAT-Scholastic Aptitude Test), number of credit hours enrolled, and age were analyzed in this study. Data mining software algorithms were used to study information about undergraduate students at a west-south-central state university in the United States. The study results revealed that two factors, number of enrolled credit hours, and a student’s age have the most effect on collegiate course withdrawal …
Lulling Waters: A Poetry Reading For Real-Time Music Generation Through Emotion Mapping,
2020
New York University
Lulling Waters: A Poetry Reading For Real-Time Music Generation Through Emotion Mapping, Ashley Muniz, Toshihisa Tsuruoka
Electronic Literature Organization Conference 2020
Through a poetic narrative, “Lulling Waters” tells the story of a whale overcoming the loss of his mother, who passed away from ingesting plastic, as he attempts to escape from the polluted oceanic world. The live performance of this poem utilizes a software system called Soundwriter, which was developed with the goal of enriching the oral storytelling experience through music. This video demonstrates how Soundwriter’s real-time hybrid system was able to analyze “Lulling Waters” through its lexical and auditory features. Emotionally salient words were given ratings based on arousal, valence, and dominance while the emotionally charged prosodic features of the …
Patterns Of Population Displacement During Mega-Fires In California Detected Using Facebook Disaster Maps,
2020
Chapman University
Patterns Of Population Displacement During Mega-Fires In California Detected Using Facebook Disaster Maps, Shenyue Jia, Seung Hee Kim, Son V. Nghiem, Paul Doherty, Menas Kafatos
Mathematics, Physics, and Computer Science Faculty Articles and Research
The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented …
Deep Learning For Real-World Object Detection,
2020
Singapore Management University
Deep Learning For Real-World Object Detection, Xiongwei Wu
Dissertations and Theses Collection (Open Access)
Despite achieving significant progresses, most existing detectors are designed to detect objects in academic contexts but consider little in real-world scenarios. In real-world applications, the scale variance of objects can be significantly higher than objects in academic contexts; In addition, existing methods are designed for achieving localization with relatively low precision, however more precise localization is demanded in real-world scenarios; Existing methods are optimized with huge amount of annotated data, but in certain real-world scenarios, only a few samples are available. In this dissertation, we aim to explore novel techniques to address these research challenges to make object detection algorithms …
