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Proceedings of the 41st International Conference on machine Learning

作者： Alexander Munteanu Simon Omlor Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany and Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany

data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points proportional to an individual importance measure called sensitivity. This framework reduces in very general settings the size of data to roughly the VC dimension d times the total sensitivity S while providing strong (1±ε) guarantees on the quality of approximation. The recent work of Woodruff & Yasuda (2023c) improved substantially over the general $\tilde O(\varepsilon^{-2}\mathfrak Sd)$ bound for the important problem of ℓp subspace embeddings to $\tilde O(\varepsilon^{-2}\mathfrak S^{2/p})$ for p ∈ [1, 2]. Their result was subsumed by an earlier $\tilde O(\varepsilon^{-2}\mathfrak Sd^{1-p/2})$ bound which was implicitly given in the work of Chen & Derezinski (2021). We show that their result is tight when sampling according to plain ℓp sensitivities. We observe that by augmenting the ℓp sensitivities by ℓ2 sensitivities, we obtain better bounds improving over the aforementioned results to optimal linear $\tilde O(\varepsilon^{-2}(\mathfrak S+d)) = \tilde O(\varepsilon^{-2}d)$ sampling complexity for all p ∈ [1, 2]. In particular, this resolves an open question of Woodruff & Yasuda (2023c) in the affirmative for p ∈ [1, 2] and brings sensitivity subsampling into the regime that was previously only known to be possible using Lewis weights (Cohen & Peng, 2015). As an application of our main result, we also obtain an Õ(ε-2µd) sensitivity sampling bound for logistic regression, where µ is a natural complexity measure for this problem. This improves over the previous Õ (ε-2µ2d) bound of Mai et al. (2021) which was based on Lewis weights subsampling.

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Turnstile ℓp leverage score sampling with applications 24

Turnstile ℓp leverage score sampling with applications

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Proceedings of the 41st International Conference on machine Learning

The turnstile data stream model offers the most flexible framework where data can be manipulated dynamically, i.e., rows, columns, and even single entries of an input matrix can be added, deleted, or updated multiple times in a data stream. We develop a novel algorithm for sampling rows ai of a matrix A ∈ ℝn×d, proportional to their ℓp norm, when A is presented in a turnstile data stream. Our algorithm not only returns the set of sampled row indexes, it also returns slightly perturbed rows ãi ≈ ai, and approximates their sampling probabilities up to e relative error. When combined with preconditioning techniques, our algorithm extends to ℓp leverage score sampling over turnstile data streams. With these properties in place, it allows us to simulate subsampling constructions of coresets for important regression problems to operate over turnstile data streams with very little overhead compared to their respective off-line subsampling algorithms. For logistic regression, our framework yields the first algorithm that achieves a (1 + ε) approximation and works in a turnstile data stream using polynomial sketch/subsample size, improving over O(1) approximations, or exp(1/ε) sketch size of previous work. We compare experimentally to plain oblivious sketching and plain leverage score sampling algorithms for ℓp and logistic regression.

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On the Effectiveness of Heterogeneous Ensemble Methods for Re-Identification

On the Effectiveness of Heterogeneous Ensemble Methods for R...

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International Conference on machine Learning and Applications (ICMLA)

作者： Simon Klüttermann Jérôme Rutinowski Frederik Polachowski Anh Nguyen Britta Grimme Moritz Roidl Emmanuel Müller TU Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Paderborn University Paderborn Germany Research Center Trustworthy Data Science and Security Dortmund Germany

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace commonly used, complex siamese neural networks with an ensemble of simplified, rudimentary models, providing wider applicability, especially in hardware-restricted scenarios. Each ensemble sub-model uses different types of extracted features of the given data as its input, allowing for the creation of effective ensembles in a fraction of the training duration needed for more complex state-of-the-art models. We reach state-of-the-art performance at our task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%, and introduce five distinct feature extraction approaches, and study their combination using different ensemble methods.

关键词： Training Accuracy Neural networks Metals Feature extraction Silicon Ensemble learning Security Reliability Pallets

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Marine Predators Algorithm for Energy Scheduling Problem Using Renewable Energy

Marine Predators Algorithm for Energy Scheduling Problem Usi...

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Cyber Resilience (ICCR), International Conference on

作者： Sharif Naser Makhadmeh Ammar Kamal Abasi Mohammed Azmi Al-Betar Department of Data Science and Artificial Intelligence University of Petra Amman Jordan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi United Arab Emirates Artificial Intelligence Research Center (AIRC) Ajman University Ajman United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The Energy Scheduling Problem (ESP) involves scheduling smart home appliances based on electricity pricing schemes. This entails adjusting the timing of operations for these appliances across different periods. The primary aim of this scheduling approach is to reduce cost and the Peak-to-Average Ratio (PAR), while enhancing user comfort. This study employs the Marine Predators Algorithm (MPA) to address ESP and derive an optimal schedule for smart home appliances. Additionally, a renewable energy source (RES) on the basis of a PV system is introduced to further optimize appliance schedules by providing a smart home with energy, particularly during peak periods. The obtained outcomes demonstrate the effectiveness of the introduced RES in reducing costs of electricity and enhancing user satisfaction. Furthermore, the performance of MPA is compared with that of the wind-driven optimization algorithm, with MPA proving superior in achieving ESP objectives.

关键词： Home appliances Optimization Pricing Heuristic algorithms Optimal scheduling Electricity Dynamic scheduling

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Universality of kernel random matrices and kernel regression in the quadratic regime

arXiv

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arXiv 2024年

作者： Pandit, Parthe Wang, Zhichao Zhu, Yizhe Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India Department of Mathematics University of California San Diego United States Department of Mathematics University of Southern California United States

Kernel ridge regression (KRR) is a popular class of machine learning models that has become an important tool for understanding deep learning. Much of the focus has been on studying the proportional asymptotic regime, n ≍ d, where n is the number of training samples and d is the dimension of the dataset. In this regime, under certain conditions on the data distribution, the kernel random matrix involved in KRR exhibits behavior akin to that of a linear kernel. In this work, we extend the study of kernel regression to the quadratic asymptotic regime, where n ≍ d2. In this regime, we demonstrate that a broad class of inner-product kernels exhibit behavior similar to a quadratic kernel. Specifically, we establish an operator norm approximation bound for the difference between the original kernel random matrix and a quadratic kernel random matrix with additional correction terms compared to the Taylor expansion of the kernel functions. The approximation works for general data distributions under a Gaussian-moment-matching assumption with a covariance structure. This new approximation is utilized to obtain a limiting spectral distribution of the original kernel matrix and characterize the precise asymptotic training and generalization errors for KRR in the quadratic regime when n/d2 converges to a non-zero constant. The generalization errors are obtained for both deterministic and random teacher models. Our proof techniques combine moment methods, Wick’s formula, orthogonal polynomials, and resolvent analysis of random matrices with correlated entries. © 2024, CC BY.

关键词： Deep learning

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Temporal Knowledge Graph Reasoning via Time-Distributed Representation Learning

Temporal Knowledge Graph Reasoning via Time-Distributed Repr...

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IEEE International Conference on data Mining (ICDM)

作者： Kangzheng Liu Feng Zhao Guandong Xu Xianzhi Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Temporal knowledge graph (TKG) reasoning has attracted significant attention. Recent approaches for modeling historical information have led to great advances. However, the problems of time variability and unseen entities have become two major obstacles preventing further development. The time variability problem means that different historical timestamps play different roles in the inference process. Furthermore, in the context of time variability, the unseen entity problem means that a query cannot obtain a predicted entity that is unseen in the scale-varying history rather than in a fixed set, thus turning from static to dynamic. In this paper, we propose a novel method named DHU-NET for addressing the time variability challenge and the dynamic unseen entity challenge derived from it. With regard to the former concern, we propose a time-distributed representation learning method based on a graph convolutional network(GCN) and a self-attention mechanism, which learns the distributed representations of facts at different historical timestamps and comprehensively pays different levels of attention to the different timestamps. With regard to the latter issue, we extract the unseen entities from a global static KG based on a copy mechanism and bring them into consideration during the final prediction step. Experiments on six benchmark datasets demonstrate the substantial improvements achieved by DHUNET in terms of multiple evaluation metrics. Our released codes are available at https://***/CGCL-codes/DHUNET.

关键词： Representation learning Measurement Knowledge engineering Predictive models Turning Cognition data models

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RETIA: Relation-Entity Twin-Interact Aggregation for Temporal Knowledge Graph Extrapolation

RETIA: Relation-Entity Twin-Interact Aggregation for Tempora...

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International Conference on data Engineering

Temporal knowledge graph (TKG) extrapolation aims to predict future unknown events (facts) based on historical information, and has attracted considerable attention due to its great practical significance. Accurate representations (embeddings) of entities and relations form the basis of TKG extrapolation. Recent work has been devoted to improving the rationality of entity representations. However, on the one hand, ignoring relation modeling results in incomplete relation representations; therefore, some approaches aggregate only immediately adjacent entities of relations, but this can lead to the "message islands" problem of relation modeling. On the other hand, ignoring the association constraints between relations and entities can make the embeddings of both relations and entities prone to overfitting. To address the abovementioned challenges, we propose an advanced method, namely, RETIA. For the former issue, we generate twin hyperrelation subgraphs for each historical subgraph and then aggregate both the adjacent entities and relations in the hyperrelation subgraphs through a graph convolutional network (GCN). About the latter concern, we propose a twin-interact module (TIM), which provides communication channels for relation aggregation and entity aggregation during the evolution of the historical sequence. Experiments conducted on five public datasets show that RETIA has made great improvements across several evaluation metrics. Our released code is available at https://***/CGCL-codes/RETIA.

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Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection

arXiv

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arXiv 2023年

作者： Yu, Haojun Li, Youcheng Wu, QuanLin Zhao, Ziwei Chen, Dengbo Wang, Dong Wang, Liwei National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University Beijing China Center of Data Science Peking University Beijing China Center for Machine Learning Research Peking University Beijing China Yizhun Medical AI Co. Ltd Beijing China Guangdong China

During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexplored. General-purpose real-time object detection models can mistakenly report obvious false positives (FPs) when applied to ultrasound videos, potentially misleading junior radiologists. One key issue is their failure to utilize negative symptoms in previous frames, denoted as negative temporal contexts (NTC) [15]. To address this issue, we propose to extract contexts from previous frames, including NTC, with the guidance of inverse optical flow. By aggregating extracted contexts, we endow the model with the ability to suppress FPs by leveraging NTC. We call the resulting model UltraDet. The proposed UltraDet demonstrates significant improvement over previous state-of-the-arts and achieves real-time inference speed. We release the code, checkpoints, and high-quality labels of the CVA-BUS dataset [9] in https://***/HaojunYu1998/UltraDet. © 2023, CC BY-NC-ND.

关键词： Object detection

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DSRGAN: Explicitly learning disentangled representation of underlying structure and rendering for image generation without tuple supervision

arXiv

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arXiv 2019年

作者： Hao, Guang-Yuan Yu, Hong-Xing Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Collaborative Innovation Center of High Performance Computing Nudt Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou

We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independently controlled respectively, yet using no tuple supervision. The setting is significant since tuple supervision is costly and sometimes even unavailable. However, the task is highly unconstrained and thus ill-posed. To address this problem, we propose to introduce an auxiliary domain which shares a common underlying-structure space with the target domain, and we make a partially shared latent space assumption. The key idea is to encourage the partially shared latent variable to represent the similar underlying spatial structures in both domains, while the two domain-specific latent variables will be unavoidably arranged to present renderings of two domains respectively. This is achieved by designing two parallel generative networks with a common Progressive Rendering Architecture (PRA), which constrains both generative networks' behaviors to model shared underlying structure and to model spatially dependent relation between rendering and underlying structure. Thus, we propose DSRGAN (GANs for Disentangling Underlying Structure and Rendering) to instantiate our method. We also propose a quantitative criterion (the Normalized Disentanglability) to quantify disentanglability. Comparison to the state-of-The-Art methods shows that DSRGAN can significantly outperform them in disentanglability. Copyright © 2019, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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Characterization of exact one-query quantum algorithms

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Physical Review A 2020年第2期101卷 022325-022325页

作者： Weijiang Chen Zekun Ye Lvzhou Li Institute of Computer Science Theory School of Data and Computer Science Sun Yat-Sen University Guangzhou 510006 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518055 China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-Sen University Guangzhou 510006 China

The quantum query model is one of the most important models in quantum computing. Several well-known quantum algorithms are captured by this model, including the Deutsch-Jozsa algorithm, the Simon algorithm, the Grover algorithm, and others. In this paper, we characterize the computational power of exact one-query quantum algorithms. It is proved that a total Boolean function f:{0,1}n→{0,1} can be exactly computed by a one-query quantum algorithm if and only if f(x)=xi1 or xi1⊕xi2 (up to isomorphism). Note that, unlike most work in the literature based on the polynomial method, our proof does not resort to any knowledge about the polynomial degree of f.

关键词： Quantum algorithms

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