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arXiv 2024年

作者： Tao, Wei Zhou, Yucheng Wang, Yanlin Zhang, Hongyu Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China School of Software Engineering Sun Yat-sen University Guangdong Zhuhai519082 China School of Big Data and Software Engineering Chongqing University Chongqing China College of Design and Innovation Tongji University Shanghai China Engineering Research Center of AI and Robotics Ministry of Education Academy for Engineering and Technology Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University 220 Handan Road Shanghai200433 China

Commit messages are natural language descriptions of code changes, which are important for software evolution such as code understanding and maintenance. However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i.e., good-practice commits), while the rest do not. On the basis of our empirical study, we discover that training on good-practice commits significantly contributes to the commit message generation. Motivated by this finding, we propose a novel knowledge-aware denoising learning method called KADEL. Considering that good-practice commits constitute only a small proportion of the dataset, we align the remaining training samples with these good-practice commits. To achieve this, we propose a model that learns the commit knowledge by training on good-practice commits. This knowledge model enables supplementing more information for training samples that do not conform to good practice. However, since the supplementary information may contain noise or prediction errors, we propose a dynamic denoising training method. This method composes a distribution-aware confidence function and a dynamic distribution list, which enhances the effectiveness of the training process. Experimental results on the whole MCMD dataset demonstrate that our method overall achieves state-of-the-art performance compared with previous methods. © 2024, CC BY.

关键词： Sampling

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Compressed deep networks: Goodbye SVD, hello robust low-rank approximation

arXiv

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arXiv 2020年

作者： Tukan, Murad Maalouf, Alaa Weksler, Matan Feldman, Dan Robotics and Big Data Lab Department of Computer Science University of Haifa Haifa Israel Samsung Research Israel Israel

A common technique for compressing a neural network is to compute the k-rank `2 approximation Ak,2 of the matrix A ∈ Rn×d that corresponds to a fully connected layer (or embedding layer). Here, d is the number of the neurons in the layer, n is the number in the next one, and Ak,2 can be stored in O((n + d)k) memory instead of O(nd). This l2-approximation minimizes the sum over every entry to the power of p = 2 in the matrix A − Ak,2, among every matrix Ak,2 ∈ Rn×d whose rank is k. While it can be computed efficiently via SVD, the l2-approximation is known to be very sensitive to outliers ("far-away" rows). Hence, machine learning uses e.g. Lasso Regression, `1-regularization, and l1-SVM that use the l1-norm. This paper suggests to replace the k-rank l2 approximation by lp, for p ∈ [1, 2]. We then provide practical and provable approximation algorithms to compute it for any p ≥ 1, based on modern techniques in computational geometry. Extensive experimental results on the GLUE benchmark for compressing BERT, DistilBERT, XLNet, and RoBERTa confirm this theoretical advantage. For example, our approach achieves 28% compression of RoBERTa’s embedding layer with only 0.63% additive drop in the accuracy (without fine-tuning) in average over all tasks in GLUE, compared to 11% drop using the existing l2-approximation. Open code is provided for reproducing and extending our results. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Enhancing subspace clustering based on dynamic prediction

引用

Frontiers of Computer Science 2019年第4期13卷 802-812页

作者： Ratha PECH Dong HAO Hong CHENG Tao ZHOU Complex Lab University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China Center for Robotics University of Electronic Science and Technology of China Chengdu 611731 China

In high dimensional data, many dimensions are irrelevant to each other and clusters are usually hidden under noise. As an important extension of the traditional clustering, subspace clustering can be utilized to simultaneously cluster the high dimensional data into several subspaces and associate the low-dimensional subspaces with the corresponding points. In subspace clustering, it is a crucial step to construct an affinity matrix with block-diagonal form, in which the blocks correspond to different clusters. The distance-based methods and the representation-based methods are two major types of approaches for building an informative affinity matrix. In general, it is the difference between the density inside and outside the blocks that determines the efficiency and accuracy of the clustering. In this work, we introduce a well-known approach in statistic physics method, namely link prediction, to enhance subspace clustering by reinforcing the affinity matrix. More importantly, we introduce the idea to combine complex network theory with machine learning. By revealing the hidden links inside each block, we maximize the density of each block along the diagonal, while restrain the remaining non-blocks in the affinity matrix as sparse as possible. Our method has been shown to have a remarkably improved clustering accuracy comparing with the existing methods on well-known datasets.

关键词： subspace clustering link prediction blockdiagonal matrix low-rank representation sparse representation.

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Trusted data Forever: Is AI the Answer?

arXiv

引用

arXiv 2022年

作者： Frontoni, Emanuele Stiber, Michael Paolanti, Marina Duranti, Luciana Lauriault, Tracey P. Abdul-Mageed, Muhammad VRAI Vision Robotics And Artificial Intelligence Lab University Of Macerata Italy University Of Washington BothellWA United States Archival Studies Lab University Of British Columbia VancouverBCV6T 1Z4 Canada Critical Media And Big Data Lab Carleton University OttawaONK1S 5B6 Canada NLP And ML Lab University Of British Columbia VancouverBCV6T 1Z4 Canada

Archival institutions and programs worldwide work to ensure that the records of governments, organizations, communities, and individuals are preserved for future generations as cultural heritage, as sources of rights, and as vehicles for holding the past accountable and to inform the future. This commitment is guaranteed through the adoption of strategic and technical measures for the long-term preservation of digital assets in any medium and form - textual, visual, or aural. Public and private archives are the largest providers of data big and small in the world and collectively host yottabytes of trusted data, to be preserved forever. Several aspects of retention and preservation, arrangement and description, management and administrations, and access and use are still open to improvement. In particular, recent advances in Artificial Intelligence (AI) open the discussion as to whether AI can support the ongoing availability and accessibility of trust-worthy public records. This paper presents preliminary results of the InterPARES Trust AI ("I Trust AI") international research partnership, which aims to (1) identify and develop specific AI technologies to address critical records and archives challenges;(2) determine the benefits and risks of employing AI technologies on records and archives;(3) ensure that archival concepts and principles inform the development of responsible AI;and (4) validate outcomes through a conglomerate of case studies and demonstrations. Copyright © 2022, The Authors. All rights reserved.

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Robust Self-Expression Learning with Adaptive Noise Perception

SSRN

引用

SSRN 2023年

作者： Wang, Yangbo Zhou, Jie Lu, Jianglin Wan, Jun Gao, Can Lin, Qingshui College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China SMILE Lab Department of ECE College of Engineering Northeastern University Boston02115 United States School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Basic Teaching Department Liaoning Technical University Huludao125105 China

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using the raw data to represent each sample under the self-expression framework may not be ideal, as noise points are inevitably involved in the process of representing clean samples. To address this issue, this work proposes a novel self-expression model called robust Self-Expression learning with adaptive Noise Perception (SENP). SENP decomposes each sample into a clean part and a noisy part, and samples with large self-expression losses can be recognized as the noise points. A reliable coefficient matrix can then be learned by using only the clean points to reconstruct the clean part of each sample. By simultaneously detecting the noisy part of each sample and noise points, and adaptively mitigating their negative impacts, the representative ability of the generated coefficient matrix is improved. Moreover, inspired by the solution of non-negative matrix factorization (NMF), an effective algorithm is formed to optimize SENP. Extensive experiments on well-known benchmark datasets demonstrate the superiority of SENP compared to several state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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CoBe - Coded Beacons for Localization, Object Tracking, and SLAM Augmentation

CoBe - Coded Beacons for Localization, Object Tracking, and ...

引用

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Roman Rabinovich Ibrahim Jubran Aaron Wetzler Ron Kimmel Geometric Image Processing (GIP) Lab Computer Science Department Technion - Israel Institute of Technology Israel Robotics & Big Data (RBD) Lab Computer Science Department University of Haifa Israel

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

This paper presents a novel beacon light coding protocol, which enables fast and accurate identification of the beacons in an image. The protocol is provably robust to a predefined set of detection and decoding errors and does not require any synchronization between the beacons themselves and the optical sensor. A detailed guide is then given for developing an optical tracking and localization system, which is based on the suggested protocol and readily available hardware. Such a system operates either as a standalone system for recovering the six degrees of freedom of fast moving objects, or integrated with existing SLAM pipelines providing them with error-free and easily identifiable landmarks. Based on this guide, we implemented a low-cost positional tracking system which can run in real-time on an IoT board. We evaluate our system's accuracy and compare it to other popular methods which utilize the same optical hardware, in experiments where the ground truth is known. A companion video containing multiple real-world experiments demonstrates the accuracy, speed, and applicability of the proposed system in a wide range of environments and real-world tasks. Open source code is provided to encourage further development of low-cost localization systems integrating the suggested technology at its navigation core.

关键词： Optical sensors Robustness Simultaneous localization and mapping Protocols Decoding Adaptive optics Optical imaging

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Introduction to Coresets: Accurate Coresets

arXiv

引用

arXiv 2019年

作者： Jubran, Ibrahim Maalouf, Alaa Feldman, Dan Robotics and Big Data Lab Department of Computer Science University of Haifa Israel

A coreset (or core-set) of an input set is its small summation, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models, classifiers, loss functions). Over the past decade, coreset construction algorithms have been suggested for many fundamental problems in e.g. machine/deep learning, computer vision, graphics, databases, and theoretical computer science. This introductory paper was written following requests from (usually non-expert, but also colleagues) regarding the many inconsistent coreset definitions, lack of available source code, the required deep theoretical background from different fields, and the dense papers that make it hard for beginners to apply coresets and develop new ones. The paper provides folklore, classic and simple results including step-by-step proofs and figures, for the simplest (accurate) coresets of very basic problems, such as: sum of vectors, minimum enclosing ball, SVD/ PCA and linear regression. Nevertheless, we did not find most of their constructions in the literature. Moreover, we expect that putting them together in a retrospective context would help the reader to grasp modern results that usually extend and generalize these fundamental observations. Experts might appreciate the unified notation and comparison table that links between existing results. Open source code with example scripts are provided for all the presented algorithms, to demonstrate their practical usage, and to support the readers who are more familiar with programming than math. Copyright © 2019, The Authors. All rights reserved.

关键词： Open source software

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Provable approximations for constrained p regression

arXiv

引用

arXiv 2019年

作者： Jubran, Ibrahim Cohn, David Feldman, Dan Robotics and Big Data Lab Department of Computer Science University of Haifa Haifa Israel

The p linear regression problem is to minimize f(x) = ||Ax - b||p over x ϵ Rd, where A ϵ Rn×d, b 2 Rn, and p > 0. To avoid overfitting and bound ||x||2, the constrained p regression minimizes f(x) over every unit vector x ϵ Rd. This makes the problem non-convex even for the simplest case d = p = 2. Instead, ridge regression is used to minimize the Lagrange form f(x)+λjjxjj2 over x 2 Rd, which yields a convex problem in the price of calibrating the regularization parameter λ > 0. We provide the first provable constant factor approximation algorithm that solves the constrained p regression directly, for every constant p;d λ 1. Using core-sets, its running time is O(n log n) including extensions for streaming and distributed (big) data. In polynomial time, it can handle outliers, p 2 (0;1) and minimize f(x) over every x and permutation of rows in A. Experimental results are also provided, including open source and comparison to existing software. Copyright © 2019, The Authors. All rights reserved.

关键词： Regression analysis

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Tight sensitivity bounds for smaller coresets

arXiv

引用

arXiv 2019年

作者： Maalouf, Alaa Statman, Adiel Feldman, Dan Robotics and Big Data Lab Department of Computer Science University of Haifa Haifa Israel

An ϵ-coreset for Least-Mean-Squares (LMS) of a matrix A ϵ n×dis a small weighted subset of its rows that approximates the sum of squared distances from its rows to every affine k-dimensional subspace of d, up to a factor of 1±ϵ. Such coresets are useful for hyper-parameter tuning and solving many least-mean-squares problems such as low-rank approximation (k-SVD), k-PCA, Lassso/Ridge/Linear regression and many more. Coresets are also useful for handling streaming, dynamic and distributed big data in parallel. With high probability, non-uniform sampling based on upper bounds on what is known as importance or sensitivity of each row in A yields a coreset. The size of the (sampled) coreset is then near-linear in the total sum of these sensitivity bounds. We provide algorithms that compute provably tight bounds for the sensitivity of each input row. It is based on two ingredients: (i) iterative algorithm that computes the exact sensitivity of each point up to arbitrary small precision for (non-affine) k-subspaces, and (ii) a general reduction of independent interest from computing sensitivity for the family of affine k-subspaces in dto (non-affine) (k + 1)- subspaces in d+1. Experimental results on real-world datasets, including the English Wikipedia documentsterm matrix, show that our bounds provide significantly smaller and data-dependent coresets also in practice. Full open source is also provided. Copyright © 2019, The Authors. All rights reserved.

关键词： Matrix algebra

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Fast and accurate least-mean-squares solvers

arXiv

引用

arXiv 2019年

作者： Maalouf, Alaa Jubran, Ibrahim Feldman, Dan The Robotics and Big Data Lab Department of Computer Science University of Haifa Haifa Israel

Least-mean squares (LMS) solvers such as Linear / Ridge / Lasso-Regression, SVD and Elastic-Net not only solve fundamental machine learning problems, but are also the building blocks in a variety of other methods, such as decision trees and matrix factorizations. We suggest an algorithm that gets a finite set of n d-dimensional real vectors and returns a weighted subset of d + 1 vectors whose sum is exactly the same. The proof in Caratheodory's Theorem (1907) computes such a subset in O(n2d2) time and thus not used in practice. Our algorithm computes this subset in O(nd) time, using O(log n) calls to Caratheodory's construction on small but "smart" subsets. This is based on a novel paradigm of fusion between different data summarization techniques, known as sketches and coresets. As an example application, we show how it can be used to boost the performance of existing LMS solvers, such as those in scikit-learn library, up to x100. Generalization for streaming and distributed (big) data is trivial. Extensive experimental results and complete open source code are also provided. Copyright © 2019, The Authors. All rights reserved.

关键词： Decision trees

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