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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning

Unsupervised Salient Patch Selection for Data-Efficient Rein...

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5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Jiang, Zhaohui Weng, Paul Shanghai Jiao Tong Univ UM SJTU Joint Inst Shanghai Peoples R China

ISBN: (纸本)9783031434204;9783031434211

To improve the sample efficiency of vision-based deep reinforcement learning (RL), we propose a novel method, called SPIRL, to automatically extract important patches from input images. Following Masked Auto-Encoders, SPIRL is based on Vision Transformer models pre-trained in a self-supervised fashion to reconstruct images from randomly-sampled patches. These pre-trained models can then be exploited to detect and select salient patches, defined as hard to reconstruct from neighboring patches. In RL, the SPIRL agent processes selected salient patches via an attention module. We empirically validate SPIRL on Atari games to test its data-efficiency against relevant state-of-the-art methods, including some traditional model-based methods and keypoint-based models. In addition, we analyze our model's interpretability capabilities.

关键词： image-based deep reinforcement learning data efficiency interpretability keypoint

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Is My Neural Net Driven by the MDL Principle?

Is My Neural Net Driven by the MDL Principle?

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作者： Brandao, Eduardo Duffner, Stefan Emonet, Remi Habrard, Amaury Jacquenet, Francois Sebban, Marc Univ Jean Monnet St Etienne CNRS Inst Opt Grad Sch Lab Hubert CurienUMR 5516 F-42023 St Etienne France Univ Lyon CNRS INSA Lyon LIRISUMR5205 F-69621 Villeurbanne France Inst Univ France IUF Paris France

ISBN: (纸本)9783031434143;9783031434150

The Minimum Description Length principle (MDL) is a formalization of Occam's razor for model selection, which states that a good model is one that can losslessly compress the data while including the cost of describing the model itself. While MDL can naturally express the behavior of certain models such as autoencoders (that inherently compress data) most representation learning techniques do not rely on such models. Instead, they learn representations by training on general or, for self-supervised learning, pretext tasks. In this paper, we propose a new formulation of the MDL principle that relies on the concept of signal and noise, which are implicitly defined by the learning task at hand. Additionally, we introduce ways to empirically measure the complexity of the learned representations by analyzing the spectra of the point Jacobians. Under certain assumptions, we show that the singular values of the point Jacobians of Neural Networks driven by the MDL principle should follow either a power law or a lognormal distribution. Finally, we conduct experiments to evaluate the behavior of the proposed measure applied to deep neural networks on different datasets, with respect to several types of noise. We observe that the experimental spectral distribution is in agreement with the spectral distribution predicted by our MDL principle, which suggests that neural networks trained with gradient descent on noisy data implicitly abide the MDL principle.

关键词： Neural Networks MDL Signal-Noise Point Jacobians

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Selecting the Number of Clusters K with a Stability Trade-off: An Internal Validation Criterion 27th

Selecting the Number of Clusters K with a Stability Trade-of...

引用

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Mourer, Alex Forest, Florent Lebbah, Mustapha Azzag, Hanane Lacaille, Jerome Univ Paris 1 Pantheon Sorbonne SAMM Paris France Univ Sorbonne Paris Nord CNRS UMR 7030 LIPN Villetaneuse France Univ Versailles Paris Saclay DAVID Lab Versailles France Safran Aircraft Engines Moissy Cramayel France

ISBN: (纸本)9783031333736;9783031333743

Model selection is a major challenge in non-parametric clustering. There is no universally admitted way to evaluate clustering results for the obvious reason that no ground truth is available. The difficulty to find a universal evaluation criterion is a consequence of the ill-defined objective of clustering. In this perspective, clustering stability has emerged as a natural and model-agnostic principle: an algorithm should find stable structures in the data. If data sets are repeatedly sampled from the same underlying distribution, an algorithm should find similar partitions. However, stability alone is not well-suited to determine the number of clusters. For instance, it is unable to detect if the number of clusters is too small. We propose a new principle: a good clustering should be stable, and within each cluster, there should exist no stable partition. This principle leads to a novel clustering validation criterion based on between-cluster and within-cluster stability, overcoming limitations of previous stability-based methods. We empirically demonstrate the effectiveness of our criterion to select the number of clusters and compare it with existing methods. Code is available at https://***/FlorentF9/skstab.

关键词： clustering model selection stability internal validation

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A Streaming Approach to the Core Vector Machine 21st

A Streaming Approach to the Core Vector Machine

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21st International Conference on artificial intelligence and Soft Computing (ICAISC)

作者： Heusinger, Moritz Schleif, Frank-Michael UAS Wurzburg Schweinfurt Dept Comp Sci Sanderheinrichsleitenweg 20 Wurzburg Germany Univ Birmingham Sch Comp Sci Edgbaston Birmingham B15 2TT England

ISBN: (纸本)9783031234798;9783031234804

The Support Vector Machine (SVM) is a widely used algorithm for batch classification with a run and memory efficient counterpart given by the Core Vector Machine (CVM). Both algorithms have nice theoretical guarantees, but are not able to handle data streams, which have to be processed instance by instance. We propose a novel approach to handle stream classification problems via an adaption of the CVM, which is also able to handle multiclass classification problems. Furthermore, we compare our Multiclass Core Vector Machine (MCCVM) approach against another existing Minimum Enclosing Ball (MEB)-based classification approach. Finally, we propose a real-world streaming dataset, which consists of changeover detection data and has only been analyzed in offline settings so far.

关键词： Support vector machines

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Let the Model Make Financial Senses: A Text2Text Generative Approach for Financial Complaint Identification 27th

Let the Model Make Financial Senses: A Text2Text Generative ...

引用

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Das, Sarmistha Singh, Apoorva Jain, Raghav Saha, Sriparna Maurya, Alka Indian Inst Technol Patna Patna Bihar India Crisil Pvt Ltd Mumbai Maharashtra India

ISBN: (纸本)9783031333798;9783031333804

A complaint frequently expresses the complainer's dissatisfaction or objectionable notion to support a belief or claim against a party or parties. Financial loss, material inconvenience, and distress are sufficient examples to intensify the need for an automated complaint analysis tool in the financial domain, particularly on social media with diverse information-related affairs. Recently, advanced approaches like complaint detection with machine learning have escalated the research interest in the area of natural language processing. Earlier, the only research focus was on how complaints are identified linguistically. Substantial modern complaint analytical models attempt to bridge the gap between the interpretability and explainability of financial complaint detection tasks. To address this, we extend an existing complaint dataset X-FINCORP, with the rationale or cause annotations for the complaint/non-compliant labels. Each instance in the dataset is now associated with five labels: complaint, emotion, polarity, severity, and rationales. Our proposed model addresses the multi-task problem as a text-to-text generation task by utilizing a generative framework. Additionally, we introduce commonsense as external information to draw more informative intuitions and enhance the overall performance of the proposed generative model. The empirical results validate the generality of our proposed model over several evaluation metrics compared to state-of-the-art models and other baselines (Resources available at https://***/appy1608/Financial-Complaint-Identification.).

关键词： Complaint Detection Cause Analysis Financial Complaint Corpus Deep learning Emotion Recognition Multi-task learning Severity Level Classification Sentiment Analysis

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Towards a Verified Tableau Prover for a Quantifier-Free Fragment of Set Theory 29th

Towards a Verified Tableau Prover for a Quantifier-Free Frag...

引用

29th International Conference on Automated Deduction (CADE)

作者： Stevens, Lukas Tech Univ Munich Boltzmannstr 3 D-85748 Garching Germany

ISBN: (纸本)9783031384998;9783031384981

Using Isabelle/HOL, we verify the state-of-the-art decision procedure for multi-level syllogistic with singleton (MLSS for short), which is a quantifier-free fragment of set theory. We formalise its syntax and semantics as well as a sound and complete tableau calculus for it. We also provide an executable specification of a decision procedure that exhaustively applies the rules of the calculus and prove its termination. Furthermore, we extend the calculus with a lightweight type system that paves the way for an integration of the procedure into Isabelle/HOL.

关键词： Decision procedures Semantic tableaux Interactive theorem proving Set theory

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Robust Few-Shot Graph Anomaly Detection via Graph Coarsening 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Li, Liting Sun, Yueheng Li, Tianpeng Shao, Minglai Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tianjin Univ Georgia Tech Shenzhen Inst Tianjin Peoples R China Tianjin Univ Sch New Media & Commun Tianjin Peoples R China

ISBN: (数字)9783031402838

ISBN: (纸本)9783031402821;9783031402838

Graph anomaly detection is an important aspect of anomaly detection, especially the graph-level anomaly detection, which can be used into biomolecular research or drug molecular detection and so on. It is necessary to identify anomalies within a limited sample size as the real-world scenarios always lack of anomalous graph labels. Graph-level anomaly detection with few samples mainly faces the following problems: 1) There are not enough samples for the model to effectively learn anomalies;2) There is noise or irrelevant information in the graph, which makes it difficult to quickly learn the key structural information of the graph. To address these issues, we propose a Robust Meta-learning-based Graph Anomaly Detection Framework via Graph Coarsening (RCM-GAD). Specifically, we employ meta-learning to effectively extract and integrate abnormal information from similar networks. Then, we use the Graph Coarsening module to obtain the key structural information of the graph for anomaly detection. We apply this framework to detect anomalies at both the graph-level and subgraph-level. We conduct experiments on four datasets, demonstrating the superiority of our proposed framework, RCM-GAD, over state-of-the-art baselines in graph-level and subgraph-level anomaly detection tasks.

关键词： Few-shot learning Graph-level anomaly detection Subgraph-level anomaly detection Graph Coarsening

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ParaSum: Contrastive Paraphrasing for Low-Resource Extractive Text Summarization 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Tang, Moming Wang, Chengyu Wang, Jianing Chen, Cen Gao, Ming Qian, Weining East China Normal Univ Sch Data Sci & Engn Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9783031402890

ISBN: (纸本)9783031402883;9783031402890

Existing extractive summarization methods achieve state-ofthe-art (SOTA) performance with pre-trained language models (PLMs) and sufficient training data. However, PLM-based methods are known to be data-hungry and often fail to deliver satisfactory results in low-resource scenarios. Constructing a high-quality summarization dataset with human-authored reference summaries is a prohibitively expensive task. To address these challenges, this paper proposes a novel paradigm for low-resource extractive summarization, called ParaSum. This paradigm reformulates text summarization as textual paraphrasing, aligning the text summarization task with the self-supervised Next Sentence Prediction (NSP) task of PLMs. This approach minimizes the training gap between the summarization model and PLMs, enabling a more effective probing of the knowledge encoded within PLMs and enhancing the summarization performance. Furthermore, to relax the requirement for large amounts of training data, we introduce a simple yet efficient model and align the training paradigm of summarization to textual paraphrasing to facilitate network-based transfer learning. Extensive experiments over two widely used benchmarks (i.e., CNN/DailyMail, Xsum) and a recent open-sourced high-quality Chinese benchmark (i.e., CNewSum) show that ParaSum consistently outperforms existing PLM-based summarization methods in all low-resource settings, demonstrating its effectiveness over different types of datasets.

关键词： low-resource scenarios extractive summarization textual paraphrasing transfer learning pre-trained language model

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Candidate-Aware Attention Enhanced Graph Neural Network for News Recommendation 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Li, Xiaohong Li, Ruihong Peng, Qixuan Ma, Huifang Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

ISBN: (数字)9783031402890

ISBN: (纸本)9783031402883;9783031402890

Facing the daily explosive growth of the news quantity, personalized news recommendation for users becomes an increasingly challenging problem. Modeling user interests is critical for accurate news recommendation. Most of existing methods are often lack of comprehensiveness because they do not fully consider the higher-order structure between user interaction and the news, the variation of sequences, and the importance of candidate news to user modeling. In this paper, we propose Candidate-aware Attention Enhanced Graph Neural Network for News Recommendation (GNNR), which encodes high-order connections into the representation of news through information propagation along the graph. And then combine obtained representations with representations from news content. For news representations, we learn click news and candidate news content information embedding. Then we combine it with the information from high-order structure in the graph via a graph neural network. This allows node information and topology to be integrated naturally, and dependencies between nodes can be effectively modeled. In addition, to address users' diverse interests, we also design an attention module to dynamically aggregate a user's history with respect to current candidate news. Extensive experiments on benchmark datasets show that our approach can effectively improve performance in news recommendation.

关键词： Graph neural network Candidate-aware attention network Gated recurrent unit News recommendation

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TAP-AHGNN: An Attention-Based Heterogeneous Graph Neural Network for Service Recommendation on Trigger-Action Programming Platform 1

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Huang, Zijun Li, Jiangfeng Zhang, Huijuan Zhang, Chenxi Yu, Gang Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Shanghai Univ SILC Business Sch Shanghai 201800 Peoples R China

ISBN: (数字)9789819947522

ISBN: (纸本)9789819947515;9789819947522

Trigger-Action Programming (TAP) is a popular IoT programming paradigm that enables users to connect IoT services and automate IoT workflows by creating if-trigger-then-action rules. However, with the increasing number of IoT services, specifying trigger and action services to compose TAP rules becomes progressively challenging for users due to the vast search space. To facilitate users in programming, a novel method named TAP-AHGNN is proposed to recommend feasible action services to auto-complete the rule based on the user-specified trigger service. Firstly, a heterogeneous TAP knowledge graph is designed, from which five meta-paths can be extracted to construct services' neighborhoods. Then, the model incorporates a multi-level attention-based heterogeneous graph convolution module that selectively aggregates neighbor information, and a transformer-based fusion module that enables the integration of multiple types of features. With the two modules mentioned before, the final representations of services can capture both semantic and structural information, which helps generate better recommendation results. Experiments on the real-world dataset demonstrate that TAP-AHGNN outperforms the most advanced baselines at HR@k, NDCG@k and MRR@k. To the best of our knowledge, TAP-AHGNN is the first method for service recommendation on TAP platforms using the heterogeneous graph neural network technique.

关键词： Trigger-Action Programming (TAP) Internet of Things (IoT) Heterogeneous Graph Neural Network Smart Service Recommendation

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