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Articles 2491 - 2520 of 8479
Full-Text Articles in Computer Sciences
Linear Algebra For Computer Science, M. Thulasidas
Linear Algebra For Computer Science, M. Thulasidas
Research Collection School Of Computing and Information Systems
This textbook introduces the essential concepts and practice of Linear Algebra to the undergraduate student of computer science. The focus of this book is on the elegance and beauty of the numerical techniques and algorithms originating from Linear Algebra. As a practical handbook for computer and data scientists, LA4CS restricts itself mostly to real fields and tractable discourses, rather than deep and theoretical mathematics.
Deriving Invariant Checkers For Critical Infrastructure Using Axiomatic Design Principles, Cheah Huei Yoong, Venkata Reddy Palleti, Rajib Ranjan Maiti, Arlindo Silva, Christopher M. Poskitt
Deriving Invariant Checkers For Critical Infrastructure Using Axiomatic Design Principles, Cheah Huei Yoong, Venkata Reddy Palleti, Rajib Ranjan Maiti, Arlindo Silva, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
Cyber-physical systems (CPSs) in critical infrastructure face serious threats of attack, motivating research into a wide variety of defence mechanisms such as those that monitor for violations of invariants, i.e. logical properties over sensor and actuator states that should always be true. Many approaches for identifying invariants attempt to do so automatically, typically using data logs, but these can miss valid system properties if relevant behaviours are not well-represented in the data. Furthermore, as the CPS is already built, resolving any design flaws or weak points identified through this process is costly. In this paper, we propose a systematic …
Self-Supervised Learning Disentangled Group Representation As Feature, Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang
Self-Supervised Learning Disentangled Group Representation As Feature, Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang
Research Collection School Of Computing and Information Systems
A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In this paper, we formulate the notion of “good” representation from a group-theoretic view using Higgins’ definition of disentangled representation [38], and show that existing Self-Supervised Learning (SSL) only disentangles simple augmentation features such as rotation and colorization, thus unable to modularize the remaining semantics. To break the limitation, we propose an iterative SSL algorithm: Iterative Partition-based Invariant Risk Minimization (IP-IRM), which successfully grounds the abstract semantics and the group acting on them into concrete contrastive learning. …
Hierarchical Control Of Multi-Agent Reinforcement Learning Team In Real-Time Strategy (Rts) Games, Weigui Jair Zhou, Budhitama Subagdja, Ah-Hwee Tan, Darren Wee Sze Ong
Hierarchical Control Of Multi-Agent Reinforcement Learning Team In Real-Time Strategy (Rts) Games, Weigui Jair Zhou, Budhitama Subagdja, Ah-Hwee Tan, Darren Wee Sze Ong
Research Collection School Of Computing and Information Systems
Coordinated control of multi-agent teams is an important task in many real-time strategy (RTS) games. In most prior work, micromanagement is the commonly used strategy whereby individual agents operate independently and make their own combat decisions. On the other extreme, some employ a macromanagement strategy whereby all agents are controlled by a single decision model. In this paper, we propose a hierarchical command and control architecture, consisting of a single high-level and multiple low-level reinforcement learning agents operating in a dynamic environment. This hierarchical model enables the low-level unit agents to make individual decisions while taking commands from the high-level …
Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh
Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh
Research Collection School Of Computing and Information Systems
Do my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible to provide timely and relevant guidance to individual learners according to their levels of understanding? One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ …
Data Fusion For Trust Evaluation, Zheng Yan, Qinghua Zheng, Laurence T. Yang, Robert H. Deng
Data Fusion For Trust Evaluation, Zheng Yan, Qinghua Zheng, Laurence T. Yang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Trust evaluation is a process to quantify trust by analyzing the data related to the factors that affect trust. It has been widely applied in many fields to facilitate decision making, system entity collaboration and security establishment. For example, in social networking, trust evaluation helps users make a social decision, reduce the risk of social interactions, and ensure the quality of a social networking environment. In digital communications, trust evaluation can be applied to detect malicious nodes, filter unwanted traffic and improve communication security. In e-commerce and cloud services, trust evaluation helps users selecting an appropriate product or service from …
Towards Understanding Why Lookahead Generalizes Better Than Sgd And Beyond, Pan Zhou, Hanshu Yan, Xiaotong Yuan, Jiashi Feng, Shuicheng Yan
Towards Understanding Why Lookahead Generalizes Better Than Sgd And Beyond, Pan Zhou, Hanshu Yan, Xiaotong Yuan, Jiashi Feng, Shuicheng Yan
Research Collection School Of Computing and Information Systems
To train networks, lookahead algorithm [1] updates its fast weights k times via an inner-loop optimizer before updating its slow weights once by using the latest fast weights. Any optimizer, e.g. SGD, can serve as the inner-loop optimizer, and the derived lookahead generally enjoys remarkable test performance improvement over the vanilla optimizer. But theoretical understandings on the test performance improvement of lookahead remain absent yet. To solve this issue, we theoretically justify the advantages of lookahead in terms of the excess risk error which measures the test performance. Specifically, we prove that lookahead using SGD as its inner-loop optimizer can …
A Theory-Driven Self-Labeling Refinement Method For Contrastive Representation Learning, Pan Zhou, Caiming Xiong, Xiao-Tong Yuan
A Theory-Driven Self-Labeling Refinement Method For Contrastive Representation Learning, Pan Zhou, Caiming Xiong, Xiao-Tong Yuan
Research Collection School Of Computing and Information Systems
For an image query, unsupervised contrastive learning labels crops of the same image as positives, and other image crops as negatives. Although intuitive, such a native label assignment strategy cannot reveal the underlying semantic similarity between a query and its positives and negatives, and impairs performance, since some negatives are semantically similar to the query or even share the same semantic class as the query. In this work, we first prove that for contrastive learning, inaccurate label assignment heavily impairs its generalization for semantic instance discrimination, while accurate labels benefit its generalization. Inspired by this theory, we propose a novel …
Infinite Time Horizon Safety Of Bayesian Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger
Infinite Time Horizon Safety Of Bayesian Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger
Research Collection School Of Computing and Information Systems
Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network’s prediction. We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems. Compared to the existing sampling-based approaches, which are inapplicable to the infinite time horizon setting, we train a separate deterministic neural network that serves as an infinite time horizon safety certificate. In particular, we show that the certificate network guarantees the safety of the system over a subset of the BNN weight posterior’s support. …
Rmix: Learning Risk-Sensitive Policies For Cooperative Reinforcement Learning Agents, Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Rmix: Learning Risk-Sensitive Policies For Cooperative Reinforcement Learning Agents, Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich
Research Collection School Of Computing and Information Systems
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i.e., risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents in complex environments. To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaR …
A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun
A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun
Research Collection School Of Computing and Information Systems
Attribute based encryption is suitable for data protection in data outsourcing systems such as cloud computing. However, the leveraging of encryption technique may retrain some routine operations over the encrypted data, particularly in the field of data retrieval. This paper presents an attribute based date retrieval with proxy re-encryption (ABDR-PRE) to provide both fine-grained access control and retrieval over the ciphertexts. The proposed scheme achieves fine-grained data access management by adopting KP-ABE mechanism, a delegator can generate the re-encryption key and search indexes for the ciphertexts to be shared over the target delegatee’s attributes. Throughout the process of data sharing, …
Checking Smart Contracts With Structural Code Embedding, Zhipeng Gao, Lingxiao Jiang, Xin Xia, David Lo, John Grundy
Checking Smart Contracts With Structural Code Embedding, Zhipeng Gao, Lingxiao Jiang, Xin Xia, David Lo, John Grundy
Research Collection School Of Computing and Information Systems
Smart contracts have been increasingly used together with blockchains to automate financial and business transactions. However, many bugs and vulnerabilities have been identified in many contracts which raises serious concerns about smart contract security, not to mention that the blockchain systems on which the smart contracts are built can be buggy. Thus, there is a significant need to better maintain smart contract code and ensure its high reliability. In this paper, we propose an automated approach to learn characteristics of smart contracts in Solidity, useful for repetitive contract code, bug detection and contract validation. Our new approach is based on …
Beyond Smoothness : Incorporating Low-Rank Analysis Into Nonparametric Density Estimation, Rob Vandermeulen, Antoine Ledent
Beyond Smoothness : Incorporating Low-Rank Analysis Into Nonparametric Density Estimation, Rob Vandermeulen, Antoine Ledent
Research Collection School Of Computing and Information Systems
The construction and theoretical analysis of the most popular universally consistent nonparametric density estimators hinge on one functional property: smoothness. In this paper we investigate the theoretical implications of incorporating a multi-view latent variable model, a type of low-rank model, into nonparametric density estimation. To do this we perform extensive analysis on histogram-style estimators that integrate a multi-view model. Our analysis culminates in showing that there exists a universally consistent histogram-style estimator that converges to any multi-view model with a finite number of Lipschitz continuous components at a rate of ˜O(1/3√n) in L1 error. In contrast, the standard histogram estimator …
Fine-Grained Generalization Analysis Of Inductive Matrix Completion, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft
Fine-Grained Generalization Analysis Of Inductive Matrix Completion, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft
Research Collection School Of Computing and Information Systems
In this paper, we bridge the gap between the state-of-the-art theoretical results for matrix completion with the nuclear norm and their equivalent in \textit{inductive matrix completion}: (1) In the distribution-free setting, we prove bounds improving the previously best scaling of \widetilde{O}(rd2) to \widetilde{O}(d3/2√r), where d is the dimension of the side information and rr is the rank. (2) We introduce the (smoothed) \textit{adjusted trace-norm minimization} strategy, an inductive analogue of the weighted trace norm, for which we show guarantees of the order \widetilde{O}(dr) under arbitrary sampling. In the inductive case, a similar rate was previously achieved only under uniform sampling …
Strategic Behavior And Market Inefficiency In Blockchain-Based Auctions, Ping Fan Ke, Jianqing Chen, Zhiling Guo
Strategic Behavior And Market Inefficiency In Blockchain-Based Auctions, Ping Fan Ke, Jianqing Chen, Zhiling Guo
Research Collection School Of Computing and Information Systems
Blockchain-based auctions play a key role in decentralized finance, such as liquidation of collaterals in crypto-lending. In this research, we show that a Blockchain-based auction is subject to the threat to availability because of the characteristics of the Blockchain platform, which could lead to auction inefficiency or even market failure. Specifically, an adversary could occupy all of the transaction capacity of an auction by sending transactions with sufficiently high transaction fees, and then win the item in an auction with a nearly zero bid price as there are no competitors available. We discuss how to prevent this kind of strategic …
Functional Signatures: New Definition And Constructions, Qingwen Guo, Qiong Huang, Sha Ma, Meiyan Xiao, Guomin Yang, Willy Susilo
Functional Signatures: New Definition And Constructions, Qingwen Guo, Qiong Huang, Sha Ma, Meiyan Xiao, Guomin Yang, Willy Susilo
Research Collection School Of Computing and Information Systems
Functional signatures (FS) enable a master authority to delegate its signing privilege to an assistant. Concretely, the master authority uses its secret key sk(F) to issue a signing key sk(f) for a designated function f is an element of F-FS and sends both f and sk(f) to the assistant E, which is then able to compute a signature sigma(f) with respect to pk(F) for a message y in the range of f. In this paper, we modify the syntax of FS slightly to support the application scenario where a certificate of authorization is necessary. Compared with the original FS, our …
Concise Mercurial Subvector Commitments: Definitions And Constructions, Yannan Li, Willy Susilo, Guomin Yang, Tran Viet Xuan Phuong, Yong Yu, Dongxi Liu
Concise Mercurial Subvector Commitments: Definitions And Constructions, Yannan Li, Willy Susilo, Guomin Yang, Tran Viet Xuan Phuong, Yong Yu, Dongxi Liu
Research Collection School Of Computing and Information Systems
Vector commitment and its variants have attracted a lot of attention recently as they have been exposed to a wide range of applications in blockchain. Two special extensions of vector commitments, namely subvector commitments and mercurial commitments, have been proposed with attractive features that are desirable in many applications. Nevertheless, to the best of our knowledge, a single construction satisfying all those attractive features is still missing. In this work, we analyze those important properties and propose a new primitive called mercurial subvector commitments, which are efficiently updatable, mercurial hiding, position binding, and aggregatable. We formalize the system model and …
Broadcast Authenticated Encryption With Keyword Search, Xueqiao Liu, Kai He, Guomin Yang, Willy Susilo, Joseph Tonien, Qiong Huang
Broadcast Authenticated Encryption With Keyword Search, Xueqiao Liu, Kai He, Guomin Yang, Willy Susilo, Joseph Tonien, Qiong Huang
Research Collection School Of Computing and Information Systems
The emergence of public-key encryption with keyword search (PEKS) has provided an elegant approach to enable keyword search over encrypted content. Due to its high computational complexity proportional to the number of intended receivers, the trivial way of deploying PEKS for data sharing with multiple receivers is impractical, which motivates the development of a new PEKS framework for broadcast mode. However, existing works suffer from either the vulnerability to keyword guessing attacks (KGA) or high computation and communication complexity. In this work, a new primitive for keyword search in broadcast mode, named broadcast authenticated encryption with keyword search (BAEKS), is …
Graph Learning Assisted Multi-Objective Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta, Mingyan Simon Lin
Graph Learning Assisted Multi-Objective Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta, Mingyan Simon Lin
Research Collection School Of Computing and Information Systems
Objective-space decomposition algorithms (ODAs) are widely studied for solvingmulti-objective integer programs. However, they often encounter difficulties inhandling scalarized problems, which could cause infeasibility or repetitive nondominatedpoints and thus induce redundant runtime. To mitigate the issue, we presenta graph neural network (GNN) based method to learn the reduction rule in the ODA.We formulate the algorithmic procedure of generic ODAs as a Markov decisionprocess, and parameterize the policy (reduction rule) with a novel two-stage GNNto fuse information from variables, constraints and especially objectives for betterstate representation. We train our model with imitation learning and deploy it ona state-of-the-art ODA. Results show that …
Learning Large Neighborhood Search Policy For Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Learning Large Neighborhood Search Policy For Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
We propose a deep reinforcement learning (RL) method to learn large neighborhood search (LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to select a subset of variables at each step, which is reoptimized by an IP solver as the repair operator. However, the combinatorial number of variable subsets prevents direct application of typical RL algorithms. To tackle this challenge, we represent all subsets by factorizing them into binary decisions on each variable. We then design a neural network to learn policies for each variable in parallel, trained by a customized actor-critic algorithm. We …
Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to …
Learning To Iteratively Solve Routing Problems With Dual-Aspect Collaborative Transformer, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang
Learning To Iteratively Solve Routing Problems With Dual-Aspect Collaborative Transformer, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang
Research Collection School Of Computing and Information Systems
Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dual-Aspect Collaborative Transformer (DACT) to learn embeddings for the node and positional features separately, instead of fusing them together as done in existing ones, so as to avoid potential noises and incompatible correlations. Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry …
Canita: Faster Rates For Distributed Convex Optimization With Communication Compression, Zhize Li, Peter Richtarik
Canita: Faster Rates For Distributed Convex Optimization With Communication Compression, Zhize Li, Peter Richtarik
Research Collection School Of Computing and Information Systems
Due to the high communication cost in distributed and federated learning, methods relying on compressed communication are becoming increasingly popular. Besides, the best theoretically and practically performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce the number of communications (faster convergence), e.g., Nesterov's accelerated gradient descent (Nesterov, 1983, 2004) and Adam (Kingma and Ba, 2014). In order to combine the benefits of communication compression and convergence acceleration, we propose a \emph{compressed and accelerated} gradient method based on ANITA (Li, 2021) for distributed optimization, which we call CANITA. Our CANITA achieves the \emph{first accelerated rate} $O\bigg(\sqrt{\Big(1+\sqrt{\frac{\omega^3}{n}}\Big)\frac{L}{\epsilon}} + \omega\big(\frac{1}{\epsilon}\big)^{\frac{1}{3}}\bigg)$, …
Video Snapshot: Single Image Motion Expansion Via Invertible Motion Embedding, Qianshu Zhu, Chu Han, Guoqiang Han, Tien-Tsin Wong, Shengfeng He
Video Snapshot: Single Image Motion Expansion Via Invertible Motion Embedding, Qianshu Zhu, Chu Han, Guoqiang Han, Tien-Tsin Wong, Shengfeng He
Research Collection School Of Computing and Information Systems
Unlike images, finding the desired video content in a large pool of videos is not easy due to the time cost of loading and watching. Most video streaming and sharing services provide the video preview function for a better browsing experience. In this paper, we aim to generate a video preview from a single image. To this end, we propose two cascaded networks, the motion embedding network and the motion expansion network. The motion embedding network aims to embed the spatio-temporal information into an embedded image, called video snapshot. On the other end, the motion expansion network is proposed to …
Protein Deep Profile And Model Predictions For Identifying The Causal Genes Of Male Infertility Based On Deep Learning, Fang Xu, Ganggang Guo, Feida Zhu, Xiaojun Tan, Liqing Fan
Protein Deep Profile And Model Predictions For Identifying The Causal Genes Of Male Infertility Based On Deep Learning, Fang Xu, Ganggang Guo, Feida Zhu, Xiaojun Tan, Liqing Fan
Research Collection School Of Computing and Information Systems
A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. Millions of genes have been sequenced in data-driven genomics era, but their causal relationships with disease phenotypes remain limited, due to the difficulty of elucidating underlying causal genes by laboratory-based strategies. Here, we proposed an innovative deep learning computational modeling alternative (DPPCG framework) for identifying causal (coding) genes for a specific disease phenotype. In terms of male infertility, we introduced proteins as intermediate cell variables, leveraging integrated deep knowledge representations (Word2vec, ProtVec, Node2vec, and Space2vec) quantitatively represented as ‘protein deep profiles’. We …
Information Technology And Organizational Learning: Managing Behavioral Change In The Digital Age By Arthur M. Langer, Siu Loon Hoe
Information Technology And Organizational Learning: Managing Behavioral Change In The Digital Age By Arthur M. Langer, Siu Loon Hoe
Research Collection School Of Computing and Information Systems
As the world battles yet another crisis because of the spread of COVID-19, the idea of digitalization brings about a whole new meaning. Many professionals and information technology (IT) managers have remarked that the spread of the coronavirus has accelerated the pace of digital transformation much more so than any effort put forth by C-suite executives. While it is true that most organizations do not accept new technology readily because of embedded legacy systems, changing the corporate cultures does play an important role in affecting the rate of IT adoption. Very often, leaders and senior executives focus on the technological …
An Economic Analysis Of Rebates Conditional On Positive Reviews, Jianqing Chen, Zhiling Guo, Jian Huang
An Economic Analysis Of Rebates Conditional On Positive Reviews, Jianqing Chen, Zhiling Guo, Jian Huang
Research Collection School Of Computing and Information Systems
Strategic sellers on some online selling platforms have recently been using a conditional-rebate strategy to manipulate product reviews under which only purchasing consumers who post positive reviews online are eligible to redeem the rebate. A key concern for the conditional rebate is that it can easily induce fake reviews, which might be harmful to consumers and society. We develop a microbehavioral model capturing consumers’ review-sharing benefit, review-posting cost, and moral cost of lying to examine the seller’s optimal pricing and rebate decisions. We derive three equilibria: the no-rebate, organic-review equilibrium; the low-rebate, boosted-authentic-review equilibrium; and the high-rebate, partially-fake-review equilibrium. We …
Representation Learning On Multi-Layered Heterogeneous Network, Delvin Ce Zhang, Hady W. Lauw
Representation Learning On Multi-Layered Heterogeneous Network, Delvin Ce Zhang, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Network data can often be represented in a multi-layered structure with rich semantics. One example is e-commerce data, containing user-user social network layer and item-item context layer, with cross-layer user-item interactions. Given the dual characters of homogeneity within each layer and heterogeneity across layers, we seek to learn node representations from such a multi-layered heterogeneous network while jointly preserving structural information and network semantics. In contrast, previous works on network embedding mainly focus on single-layered or homogeneous networks with one type of nodes and links. In this paper we propose intra- and cross-layer proximity concepts. Intra-layer proximity simulates propagation along …
Topic Modeling For Multi-Aspect Listwise Comparison, Delvin Ce Zhang, Hady W. Lauw
Topic Modeling For Multi-Aspect Listwise Comparison, Delvin Ce Zhang, Hady W. Lauw
Research Collection School Of Computing and Information Systems
As a well-established probabilistic method, topic models seek to uncover latent semantics from plain text. In addition to having textual content, we observe that documents are usually compared in listwise rankings based on their content. For instance, world-wide countries are compared in an international ranking in terms of electricity production based on their national reports. Such document comparisons constitute additional information that reveal documents' relative similarities. Incorporating them into topic modeling could yield comparative topics that help to differentiate and rank documents. Furthermore, based on different comparison criteria, the observed document comparisons usually cover multiple aspects, each expressing a distinct …
Learning To Teach And Learn For Semi-Supervised Few-Shot Image Classification, Xinzhe Li, Jianqiang Huang, Yaoyao Liu, Qin Zhou, Shibao Zheng, Bernt Schiele, Qianru Sun
Learning To Teach And Learn For Semi-Supervised Few-Shot Image Classification, Xinzhe Li, Jianqiang Huang, Yaoyao Liu, Qin Zhou, Shibao Zheng, Bernt Schiele, Qianru Sun
Research Collection School Of Computing and Information Systems
This paper presents a novel semi-supervised few-shot image classification method named Learning to Teach and Learn (LTTL) to effectively leverage unlabeled samples in small-data regimes. Our method is based on self-training, which assigns pseudo labels to unlabeled data. However, the conventional pseudo-labeling operation heavily relies on the initial model trained by using a handful of labeled data and may produce many noisy labeled samples. We propose to solve the problem with three steps: firstly, cherry-picking searches valuable samples from pseudo-labeled data by using a soft weighting network; and then, cross-teaching allows the classifiers to teach mutually for rejecting more noisy …