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"Topic": models, code, and papers

BASGD: Buffered Asynchronous SGD for Byzantine Learning

Mar 03, 2020
Yi-Rui Yang, Wu-Jun Li

Distributed learning has become a hot research topic, due to its wide application in cluster-based large-scale learning, federated learning, edge computing and so on. Most distributed learning methods assume no error and attack on the workers. However, many unexpected cases, such as communication error and even malicious attack, may happen in real applications. Hence, Byzantine learning (BL), which refers to distributed learning with attack or error, has recently attracted much attention. Most existing BL methods are synchronous, which will result in slow convergence when there exist heterogeneous workers. Furthermore, in some applications like federated learning and edge computing, synchronization cannot even be performed most of the time due to the online workers (clients or edge servers). Hence, asynchronous BL (ABL) is more general and practical than synchronous BL (SBL). To the best of our knowledge, there exist only two ABL methods. One of them cannot resist malicious attack. The other needs to store some training instances on the server, which has the privacy leak problem. In this paper, we propose a novel method, called buffered asynchronous stochastic gradient descent (BASGD), for BL. BASGD is an asynchronous method. Furthermore, BASGD has no need to store any training instances on the server, and hence can preserve privacy in ABL. BASGD is theoretically proved to have the ability of resisting against error and malicious attack. Moreover, BASGD has a similar theoretical convergence rate to that of vanilla asynchronous SGD (ASGD), with an extra constant variance. Empirical results show that BASGD can significantly outperform vanilla ASGD and other ABL baselines, when there exists error or attack on workers.


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Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification

Nov 19, 2019
Hongwei Dong, Siyu Zhang, Bin Zou, Lamei Zhang

Convolutional neural networks (CNNs) have shown good performance in polarimetric synthetic aperture radar (PolSAR) image classification due to the automation of feature engineering. Excellent hand-crafted architectures of CNNs incorporated the wisdom of human experts, which is an important reason for CNN's success. However, the design of the architectures is a difficult problem, which needs a lot of professional knowledge as well as computational resources. Moreover, the architecture designed by hand might be suboptimal, because it is only one of thousands of unobserved but objective existed paths. Considering that the success of deep learning is largely due to its automation of the feature engineering process, how to design automatic architecture searching methods to replace the hand-crafted ones is an interesting topic. In this paper, we explore the application of neural architecture search (NAS) in PolSAR area for the first time. Different from the utilization of existing NAS methods, we propose a differentiable architecture search (DAS) method which is customized for PolSAR classification. The proposed DAS is equipped with a PolSAR tailored search space and an improved one-shot search strategy. By DAS, the weights parameters and architecture parameters (corresponds to the hyperparameters but not the topologies) can be optimized by stochastic gradient descent method during the training. The optimized architecture parameters should be transformed into corresponding CNN architecture and re-train to achieve high-precision PolSAR classification. In addition, complex-valued DAS is developed to take into account the characteristics of PolSAR images so as to further improve the performance. Experiments on three PolSAR benchmark datasets show that the CNNs obtained by searching have better classification performance than the hand-crafted ones.


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Mutual Clustering on Comparative Texts via Heterogeneous Information Networks

Mar 09, 2019
Jianping Cao, Senzhang Wang, Danyan Wen, Zhaohui Peng, Philip S. Yu, Fei-yue Wang

Currently, many intelligence systems contain the texts from multi-sources, e.g., bulletin board system (BBS) posts, tweets and news. These texts can be ``comparative'' since they may be semantically correlated and thus provide us with different perspectives toward the same topics or events. To better organize the multi-sourced texts and obtain more comprehensive knowledge, we propose to study the novel problem of Mutual Clustering on Comparative Texts (MCCT), which aims to cluster the comparative texts simultaneously and collaboratively. The MCCT problem is difficult to address because 1) comparative texts usually present different data formats and structures and thus they are hard to organize, and 2) there lacks an effective method to connect the semantically correlated comparative texts to facilitate clustering them in an unified way. To this aim, in this paper we propose a Heterogeneous Information Network-based Text clustering framework HINT. HINT first models multi-sourced texts (e.g. news and tweets) as heterogeneous information networks by introducing the shared ``anchor texts'' to connect the comparative texts. Next, two similarity matrices based on HINT as well as a transition matrix for cross-text-source knowledge transfer are constructed. Comparative texts clustering are then conducted by utilizing the constructed matrices. Finally, a mutual clustering algorithm is also proposed to further unify the separate clustering results of the comparative texts by introducing a clustering consistency constraint. We conduct extensive experimental on three tweets-news datasets, and the results demonstrate the effectiveness and robustness of the proposed method in addressing the MCCT problem.

* Knowledge and Information System, 2019 

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Diversity in Machine Learning

Jul 04, 2018
Zhiqiang Gong, Ping Zhong, Weidong Hu

Machine learning methods have achieved good performance and been widely applied in various real-world applications. It can learn the model adaptively and be better fit for special requirements of different tasks. Many factors can affect the performance of the machine learning process, among which diversity of the machine learning is an important one. Generally, a good machine learning system is composed of plentiful training data, a good model training process, and an accurate inference. The diversity could help each procedure to guarantee a total good machine learning: diversity of the training data ensures the data contain enough discriminative information, diversity of the learned model (diversity in parameters of each model or diversity in models) makes each parameter/model capture unique or complement information and the diversity in inference can provide multiple choices each of which corresponds to a plausible result. However, there is no systematical analysis of the diversification in machine learning system. In this paper, we systematically summarize the methods to make data diversification, model diversification, and inference diversification in machine learning process, respectively. In addition, the typical applications where the diversity technology improved the machine learning performances have been surveyed, including the remote sensing imaging tasks, machine translation, camera relocalization, image segmentation, object detection, topic modeling, and others. Finally, we discuss some challenges of diversity technology in machine learning and point out some directions in future work. Our analysis provides a deeper understanding of the diversity technology in machine learning tasks, and hence can help design and learn more effective models for specific tasks.


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Crowd ideation of supervised learning problems

Feb 14, 2018
James P. Bagrow

Crowdsourcing is an important avenue for collecting machine learning data, but crowdsourcing can go beyond simple data collection by employing the creativity and wisdom of crowd workers. Yet crowd participants are unlikely to be experts in statistics or predictive modeling, and it is not clear how well non-experts can contribute creatively to the process of machine learning. Here we study an end-to-end crowdsourcing algorithm where groups of non-expert workers propose supervised learning problems, rank and categorize those problems, and then provide data to train predictive models on those problems. Problem proposal includes and extends feature engineering because workers propose the entire problem, not only the input features but also the target variable. We show that workers without machine learning experience can collectively construct useful datasets and that predictive models can be learned on these datasets. In our experiments, the problems proposed by workers covered a broad range of topics, from politics and current events to problems capturing health behavior, demographics, and more. Workers also favored questions showing positively correlated relationships, which has interesting implications given many supervised learning methods perform as well with strong negative correlations. Proper instructions are crucial for non-experts, so we also conducted a randomized trial to understand how different instructions may influence the types of problems proposed by workers. In general, shifting the focus of machine learning tasks from designing and training individual predictive models to problem proposal allows crowdsourcers to design requirements for problems of interest and then guide workers towards contributing to the most suitable problems.

* 25 pages (double-spaced), 8 figures 

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Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval

Jan 18, 2017
Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang

Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo. We observe that the same landmarks provided by different users over social media community may convey different geometry information depending on the viewpoints and/or angles, and may subsequently yield very different results. In fact, dealing with the landmarks with \illshapes caused by the photography of q-users is often nontrivial and has seldom been studied. In this paper we propose a novel framework, namely multi-query expansions, to retrieve semantically robust landmarks by two steps. Firstly, we identify the top-$k$ photos regarding the latent topics of a query landmark to construct multi-query set so as to remedy its possible \illshape. For this purpose, we significantly extend the techniques of Latent Dirichlet Allocation. Then, motivated by the typical \emph{collaborative filtering} methods, we propose to learn a \emph{collaborative} deep networks based semantically, nonlinear and high-level features over the latent factor for landmark photo as the training set, which is formed by matrix factorization over \emph{collaborative} user-photo matrix regarding the multi-query set. The learned deep network is further applied to generate the features for all the other photos, meanwhile resulting into a compact multi-query set within such space. Extensive experiments are conducted on real-world social media data with both landmark photos together with their user information to show the superior performance over the existing methods.

* Accepted to Appear in IEEE Trans on Image Processing 

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Towards automating the generation of derivative nouns in Sanskrit by simulating Panini

Dec 22, 2015
Amrith Krishna, Pawan Goyal

About 1115 rules in Astadhyayi from A.4.1.76 to A.5.4.160 deal with generation of derivative nouns, making it one of the largest topical sections in Astadhyayi, called as the Taddhita section owing to the head rule A.4.1.76. This section is a systematic arrangement of rules that enumerates various affixes that are used in the derivation under specific semantic relations. We propose a system that automates the process of generation of derivative nouns as per the rules in Astadhyayi. The proposed system follows a completely object oriented approach, that models each rule as a class of its own and then groups them as rule groups. The rule groups are decided on the basis of selective grouping of rules by virtue of anuvrtti. The grouping of rules results in an inheritance network of rules which is a directed acyclic graph. Every rule group has a head rule and the head rule notifies all the direct member rules of the group about the environment which contains all the details about data entities, participating in the derivation process. The system implements this mechanism using multilevel inheritance and observer design patterns. The system focuses not only on generation of the desired final form, but also on the correctness of sequence of rules applied to make sure that the derivation has taken place in strict adherence to Astadhyayi. The proposed system's design allows to incorporate various conflict resolution methods mentioned in authentic texts and hence the effectiveness of those rules can be validated with the results from the system. We also present cases where we have checked the applicability of the system with the rules which are not specifically applicable to derivation of derivative nouns, in order to see the effectiveness of the proposed schema as a generic system for modeling Astadhyayi.

* 16th World Sanskrit Conference, Bangkok June 28th - July 02 2015 

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Understanding Human-Centric Images: From Geometry to Fashion

Dec 14, 2015
Edgar Simo-Serra

Understanding humans from photographs has always been a fundamental goal of computer vision. In this thesis we have developed a hierarchy of tools that cover a wide range of topics with the objective of understanding humans from monocular RGB image: from low level feature point descriptors to high level fashion-aware conditional random fields models. In order to build these high level models it is paramount to have a battery of robust and reliable low and mid level cues. Along these lines, we have proposed two low-level keypoint descriptors: one based on the theory of the heat diffusion on images, and the other that uses a convolutional neural network to learn discriminative image patch representations. We also introduce distinct low-level generative models for representing human pose: in particular we present a discrete model based on a directed acyclic graph and a continuous model that consists of poses clustered on a Riemannian manifold. As mid level cues we propose two 3D human pose estimation algorithms: one that estimates the 3D pose given a noisy 2D estimation, and an approach that simultaneously estimates both the 2D and 3D pose. Finally, we formulate higher level models built upon low and mid level cues for understanding humans from single images. Concretely, we focus on two different tasks in the context of fashion: semantic segmentation of clothing, and predicting the fashionability from images with metadata to ultimately provide fashion advice to the user. For all presented approaches we present extensive results and comparisons against the state-of-the-art and show significant improvements on the entire variety of tasks we tackle.

* PhD Thesis, May 2015. BarcelonaTech. 169 pages 

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A Methodology for Player Modeling based on Machine Learning

Dec 13, 2013
Marlos C. Machado

AI is gradually receiving more attention as a fundamental feature to increase the immersion in digital games. Among the several AI approaches, player modeling is becoming an important one. The main idea is to understand and model the player characteristics and behaviors in order to develop a better AI. In this work, we discuss several aspects of this new field. We proposed a taxonomy to organize the area, discussing several facets of this topic, ranging from implementation decisions up to what a model attempts to describe. We then classify, in our taxonomy, some of the most important works in this field. We also presented a generic approach to deal with player modeling using ML, and we instantiated this approach to model players' preferences in the game Civilization IV. The instantiation of this approach has several steps. We first discuss a generic representation, regardless of what is being modeled, and evaluate it performing experiments with the strategy game Civilization IV. Continuing the instantiation of the proposed approach we evaluated the applicability of using game score information to distinguish different preferences. We presented a characterization of virtual agents in the game, comparing their behavior with their stated preferences. Once we have characterized these agents, we were able to observe that different preferences generate different behaviors, measured by several game indicators. We then tackled the preference modeling problem as a binary classification task, with a supervised learning approach. We compared four different methods, based on different paradigms (SVM, AdaBoost, NaiveBayes and JRip), evaluating them on a set of matches played by different virtual agents. We conclude our work using the learned models to infer human players' preferences. Using some of the evaluated classifiers we obtained accuracies over 60% for most of the inferred preferences.

* Thesis presented by Marlos C. Machado as part of the requirements for the degree or Master of Science in Computer Science granted by the Universidade Federal de Minas Gerais. February, 18th, 2013 

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Enabling Synthetic Data adoption in regulated domains

Apr 13, 2022
Giorgio Visani, Giacomo Graffi, Mattia Alfero, Enrico Bagli, Davide Capuzzo, Federico Chesani

The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms, bringing forward new challenges. In particular, the sensitive nature of the information in highly regulated scenarios needs to be accounted for. Specific approaches to address the privacy issue have been developed, as Privacy Enhancing Technologies. However, they frequently cause loss of information, putting forward a crucial trade-off among data quality and privacy. A clever way to bypass such a conundrum relies on Synthetic Data: data obtained from a generative process, learning the real data properties. Both Academia and Industry realized the importance of evaluating synthetic data quality: without all-round reliable metrics, the innovative data generation task has no proper objective function to maximize. Despite that, the topic remains under-explored. For this reason, we systematically catalog the important traits of synthetic data quality and privacy, and devise a specific methodology to test them. The result is DAISYnt (aDoption of Artificial Intelligence SYnthesis): a comprehensive suite of advanced tests, which sets a de facto standard for synthetic data evaluation. As a practical use-case, a variety of generative algorithms have been trained on real-world Credit Bureau Data. The best model has been assessed, using DAISYnt on the different synthetic replicas. Further potential uses, among others, entail auditing and fine-tuning of generative models or ensuring high quality of a given synthetic dataset. From a prescriptive viewpoint, eventually, DAISYnt may pave the way to synthetic data adoption in highly regulated domains, ranging from Finance to Healthcare, through Insurance and Education.


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