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arXiv 2021年

作者： Kumagai, Atsutoshi Iwata, Tomoharu Fujiwara, Yasuhiro NTT Computer and Data Science Laboratories NTT Communication Science Laboratories

We propose a few-shot learning method for unsupervised feature selection, which is a task to select a subset of relevant features in unlabeled data. Existing methods usually require many instances for feature selection. However, sufficient instances are often unavailable in practice. The proposed method can select a subset of relevant features in a target task given a few unlabeled target instances by training with unlabeled instances in multiple source tasks. Our model consists of a feature selector and decoder. The feature selector outputs a subset of relevant features taking a few unlabeled instances as input such that the decoder can reconstruct the original features of unseen instances from the selected ones. The feature selector uses the Concrete random variables to select features via gradient descent. To encode task-specific properties from a few unlabeled instances to the model, the Concrete random variables and decoder are modeled using permutation-invariant neural networks that take a few unlabeled instances as input. Our model is trained by minimizing the expected test reconstruction error given a few unlabeled instances that is calculated with datasets in source tasks. We experimentally demonstrate that the proposed method outperforms existing feature selection methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Random variables

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Fast Binary Network Hashing via Graph Clustering

Fast Binary Network Hashing via Graph Clustering

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IEEE International Conference on Big data

作者： Yasuhiro Fujiwaray Masahiro Nakanoy Atsutoshi Kumagai Yasutoshi Idaz Akisato Kimura Naonori Ueda NTT Communication Science Laboratories NTT Computer and Data Science Laboratories

ISBN: (纸本)9781665480468

Network hashing converts each node of a graph into a compact binary code, and it is a useful graph analytics tool since it can reduce memory cost. INH-MF is a network hashing approach to factorize the high-order proximity matrix representing similarities between nodes. However, since it cuts small nonzero elements from the proximity matrix, it fails to effectively extract insights from the graph. Moreover, it incurs high memory and computational costs since the proximity matrix is large and dense. We propose Graph Clustering-based Network Hashing, a novel network hashing approach. To compute the proximities effectively, it uses the structural relationships between nodes and clusters obtained from a graph clustering approach. Moreover, it can efficiently compute hash codes from eigenvectors of the matrix corresponding to the graph Laplacian by using its low-rank property. Experiments show that it can more efficiently and effectively compute hash codes than previous approaches.

关键词： Laplace equations Costs Binary codes Big data Computational efficiency Matrix converters

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Meta-learning for relative density-ratio estimation 21

Meta-learning for relative density-ratio estimation

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Atsutoshi Kumagai Tomoharu Iwata Yasuhiro Fujiwara NTT Computer and Data Science Laboratories NTT Communication Science Laboratories

ISBN: (纸本)9781713845393

The ratio of two probability densities, called a density-ratio, is a vital quantity in machine learning. In particular, a relative density-ratio, which is a bounded extension of the density-ratio, has received much attention due to its stability and has been used in various applications such as outlier detection and dataset comparison. Existing methods for (relative) density-ratio estimation (DRE) require many instances from both densities. However, sufficient instances are often unavailable in practice. In this paper, we propose a meta-learning method for relative DRE, which estimates the relative density-ratio from a few instances by using knowledge in related datasets. Specifically, given two datasets that consist of a few instances, our model extracts the datasets' information by using neural networks and uses it to obtain instance embeddings appropriate for the relative DRE. We model the relative density-ratio by a linear model on the embedded space, whose global optimum solution can be obtained as a closed-form solution. The closed-form solution enables fast and effective adaptation to a few instances, and its differentiability enables us to train our model such that the expected test error for relative DRE can be explicitly minimized after adapting to a few instances. We empirically demonstrate the effectiveness of the proposed method by using three problems: relative DRE, dataset comparison, and outlier detection.

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Meta-learning for relative density-ratio estimation

arXiv

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arXiv 2021年

作者： Kumagai, Atsutoshi Iwata, Tomoharu Fujiwara, Yasuhiro NTT Computer and Data Science Laboratories NTT Communication Science Laboratories

关键词： Anomaly detection

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Interpretable Skill Learning for Dynamic Treatment Regimes through Imitation 57

Interpretable Skill Learning for Dynamic Treatment Regimes t...

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57th Annual Conference on Information sciences and Systems, CISS 2023

作者： Jiang, Yushan Yu, Wenchao Song, Dongjin Cheng, Wei Chen, Haifeng University of Connecticut Department of Computer Science and Engineering StorrsCT United States Data Science and System Security Nec Laboratories American PrincetonNJ United States

ISBN: (纸本)9781665451819

Imitation learning that mimics experts' skills from their demonstrations has shown great success in discovering dynamic treatment regimes, i.e., the optimal decision rules to treat an individual patient based on related evolving treatment and covariate history. Existing imitation learning methods, however, still lack the capability to interpret the underlying rationales of the learned policy in a faithful way. Moreover, since dynamic treatment regimes for patients often exhibit varying patterns, i.e., symptoms that transit from one to another, the flat policy learned by a vanilla imitation learning method is typically undesired. To this end, we propose an Interpretable Skill Learning (ISL) framework to resolve the aforementioned challenges for dynamic treatment regimes through imitation. The key idea is to model each segment of experts' demonstrations with a prototype layer and integrate it with the imitation learning layer to enhance the interpretation capability. On one hand, the ISL framework is able to provide interpretable explanations by matching the prototype to exemplar segments during the inference stage, which enables doctors to perform reasoning of the learned demonstrations based on human-understandable patient symptoms and lab results. On the other hand, the obtained skill embedding consisting of prototypes serves as conditional information to the imitation learning layer, which implicitly guides the policy network to provide a more accurate demonstration when the patients' state switches from one stage to another. Thoroughly empirical studies demonstrate that our proposed ISL technique can achieve better performance than state-of-the-art methods. Moreover, the proposed ISL framework also exhibits good interpretability which cannot be observed in existing methods. © 2023 IEEE.

关键词： Demonstrations

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Designing Secure Sparse Coding via Multiple Random Unitary Transforms

Designing Secure Sparse Coding via Multiple Random Unitary T...

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Nakachi, Takayuki Bandoh, Yukihiro University of The Ryukyus Information Technology Center Okinawa Japan Nippon Telegraph and Telephone Corporation NTT Computer and Data Science Laboratories Kanagawa Japan

ISBN: (纸本)9781665419512

In this paper, we propose a design method of secure sparse coding via multiple random unitary transforms (RUTs). The proposed method operates as an Encryption-then-Compression (EtC) system. The multiple RUTs will increase the security strength of the secure sparse coding. Despite the improvement in security strength, the rate-distortion performance does not deteriorate. It provides the same rate-distortion performance as that of sparse coding without encryption. We demonstrate its performance via simulation. © 2021 IEEE.

关键词： Cryptography

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Analysis of the semi-synchronous approach to large-scale parallel community finding 14

Analysis of the semi-synchronous approach to large-scale par...

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2nd ACM Conference on Online Social Networks, COSN 2014

作者： Duriakova, Erika Hurley, Neil Ajwani, Deepak Sala, Alessandra Insight Centre for Data Analytics School of Computer Science and Informatics University College Dublin Dublin Ireland Bell Laboratories Dublin Dublin Ireland

ISBN: (纸本)9781450331982

Community-finding in graphs is the process of identifying highly cohesive vertex subsets. Recently the vertex-centric approach has been found effective for scalable graph processing and is implemented in systems such as GraphLab and Pregel. In the vertex-centric approach, the analysis is decomposed into a set of local computations at each vertex of the graph, with results propagated to neighbours along the vertex's edges. Many community finding algorithms are amenable to this approach as they are based on the optimisation of an objective through a process of iterative local update (ILU), in which vertices are successively moved to the community of one of their neighbours in order to achieve the highest local gain in the quality of the objective. The sequential processing of such iterative algorithms generally benefits from an asynchronous approach, where a vertex update uses the most recent state as generated by the previous update of vertices in its neighbourhood. When vertices are distributed over a parallel machine, the asynchronous approach can encounter race conditions that impact on its performance and destroy the consistency of the results. Alternatively, a semi-synchronous approach ensures that only non-conflicting vertices are updated simultaneously. In this paper we study the semi-synchronous approach to ILU algorithms for community finding on social networks. Because of the heavy-tailed vertex distribution, the order in which vertex updates are applied in asynchronous ILU can greatly impact both convergence time and quality of the found communities. We study the impact of ordering on the distributed label propagation and modularity maximisation algorithms implemented on a shared-memory multicore architecture. We demonstrate that the semi-synchronous ILU approach is competitive in time and quality with the asynchronous approach, while allowing the analyst to maintain consistent control over update ordering. Thus, our implementation results in a more robust

关键词： Iterative methods

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Meta-ticket: finding optimal subnetworks for few-shot learning within randomly initialized neural networks 22

Meta-ticket: finding optimal subnetworks for few-shot learni...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Daiki Chijiwa Shin'ya Yamaguchi Atsutoshi Kumagai Yasutoshi Ida NTT Computer and Data Science Laboratories NTT Corporation NTT Computer and Data Science Laboratories NTT Corporation and Kyoto University

ISBN: (纸本)9781713871088

Few-shot learning for neural networks (NNs) is an important problem that aims to train NNs with a few data. The main challenge is how to avoid overfitting since over-parameterized NNs can easily overfit to such small dataset. Previous work (e.g. MAML by Finn et al. 2017) tackles this challenge by meta-learning, which learns how to learn from a few data by using various tasks. On the other hand, one conventional approach to avoid overfitting is restricting hypothesis spaces by endowing sparse NN structures like convolution layers in computer vision. However, although such manually-designed sparse structures are sample-efficient for sufficiently large datasets, they are still insufficient for few-shot learning. Then the following questions naturally arise: (1) Can we find sparse structures effective for few-shot learning by meta-learning? (2) What benefits will it bring in terms of meta-generalization? In this work, we propose a novel meta-learning approach, called Meta-ticket, to find optimal sparse subnetworks for few-shot learning within randomly initialized NNs. We empirically validated that Meta-ticket successfully discover sparse subnetworks that can learn specialized features for each given task. Due to this task-wise adaptation ability, Meta-ticket achieves superior meta-generalization compared to MAML-based methods especially with large NNs.

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Acoustic XR Technology Merging Real and Virtual Sounds

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NTT Technical Review 2024年第6期22卷 30-34页

作者： Noguchi, Kenichi Chiba, Hironobu Kako, Tatsuya Kozuka, Shihori Kurokawa, Yoshiaki Watanabe, Yuki Nakayama, Akira Ultra-Reality Computing Group NTT Computer and Data Science Laboratories

With the spread of open-ear earphones that do not cover the ear, new listening experiences are being proposed that combines real ambient sounds with virtual sounds heard from earphones. At NTT, we call this merging of the real and virtual sounds through open-ear earphones "acoustic XR (extended reality) technology," which we are now developing with a view to actual services. In this article, we describe this technology with a focus on actual trials and touch upon future developments. © 2024 Nippon Telegraph and Telephone Corp.. All rights reserved.

关键词： Merging

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Toward data Efficient Model Merging between Different datasets without Performance Degradation

arXiv

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arXiv 2023年

作者： Yamda, Masanori Yamashita, Tomoya Yamaguchi, Shin'ya Chijiwa, Daiki NTT Social Informatics Laboratories NTT Computer and Data Science Laboratories

Model merging is attracting attention as a novel method for creating a new model by combining the weights of different trained models. While previous studies reported that model merging works well for models trained on a single dataset with different random seeds, model merging between different datasets remains unsolved. In this paper, we attempt to reveal the difficulty in merging such models trained on different datasets and alleviate it. Our empirical analyses show that, in contrast to the single-dataset scenarios, dataset information needs to be accessed to achieve high accuracy when merging models trained on different datasets. However, the requirement to use full datasets not only incurs significant computational costs but also becomes a major limitation when integrating models developed and shared by others. To address this, we demonstrate that dataset reduction techniques, such as coreset selection and dataset condensation, effectively reduce the data requirement for model merging. In our experiments with SPLIT-CIFAR10 model merging, the accuracy is significantly improved by 31% when using the full dataset and 24% when using the sampled subset compared with not using the dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： data assimilation

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