Scout ArXiv Academic Paper Search

{
  "q": "neural networks",
  "recency": "week",
  "per_page": 5
}

{
  "data": [
    {
      "id": "2306.14753v1",
      "url": "https://arxiv.org/abs/2306.14753v1",
      "date": "2023-06-26T15:09:14Z",
      "title": "The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory",
      "authors": [
        "Sergey Oladyshkin",
        "Timothy Praditia",
        "Ilja Kröker",
        "Farid Mohammadi",
        "Wolfgang Nowak",
        "Sebastian Otte"
      ],
      "pdf_url": "https://arxiv.org/pdf/2306.14753v1",
      "summary": "Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based on Deep Artificial Neural Networks (DANN) are very popular in our days. Depending",
      "updated": "2023-06-26T15:09:14Z",
      "categories": [
        "cs.NE",
        "stat.ML",
        "cs.NE"
      ]
    },
    {
      "id": "2307.05639v2",
      "url": "https://arxiv.org/abs/2307.05639v2",
      "date": "2023-07-11T09:54:30Z",
      "title": "Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks",
      "authors": [
        "Danny D'Agostino",
        "Ilija Ilievski",
        "Christine Annette Shoemaker"
      ],
      "pdf_url": "https://arxiv.org/pdf/2307.05639v2",
      "summary": "Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this challenge, we propose a modification of the radial",
      "updated": "2024-05-11T04:11:23Z",
      "categories": [
        "cs.LG",
        "cs.AI",
        "cs.NE",
        "stat.ML",
        "cs.LG"
      ]
    },
    {
      "id": "1901.06610v2",
      "url": "https://arxiv.org/abs/1901.06610v2",
      "date": "2019-01-20T01:48:43Z",
      "title": "Hierarchical Attentional Hybrid Neural Networks for Document Classification",
      "authors": [
        "Jader Abreu",
        "Luis Fred",
        "David Macêdo",
        "Cleber Zanchettin"
      ],
      "pdf_url": "https://arxiv.org/pdf/1901.06610v2",
      "summary": "Document classification is a challenging task with important applications. The deep learning approaches to the problem have gained much attention recently. Despite the progress, the proposed models do not incorporate the knowledge of the document structure in the architecture efficiently and not tak",
      "updated": "2019-06-28T17:49:18Z",
      "categories": [
        "cs.CL",
        "cs.AI",
        "cs.LG",
        "cs.NE",
        "cs.CL"
      ]
    },
    {
      "id": "1905.05918v1",
      "url": "https://arxiv.org/abs/1905.05918v1",
      "date": "2019-05-15T02:31:45Z",
      "title": "A Neural Network-Evolutionary Computational Framework for Remaining Useful Life Estimation of Mechanical Systems",
      "authors": [
        "David Laredo",
        "Zhaoyin Chen",
        "Oliver Schütze",
        "Jian-Qiao Sun"
      ],
      "pdf_url": "https://arxiv.org/pdf/1905.05918v1",
      "summary": "This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window to estimate the RUL for ",
      "updated": "2019-05-15T02:31:45Z",
      "categories": [
        "cs.LG",
        "cs.NE",
        "stat.ML",
        "cs.LG"
      ]
    },
    {
      "id": "1906.10015v2",
      "url": "https://arxiv.org/abs/1906.10015v2",
      "date": "2019-06-24T15:10:44Z",
      "title": "A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder",
      "authors": [
        "Pablo Lanillos",
        "Daniel Oliva",
        "Anja Philippsen",
        "Yuichi Yamashita",
        "Yukie Nagai",
        "Gordon Cheng"
      ],
      "pdf_url": "https://arxiv.org/pdf/1906.10015v2",
      "summary": "This survey presents the most relevant neural network models of autism spectrum disorder and schizophrenia, from the first connectionist models to recent deep network architectures. We analyzed and compared the most representative symptoms with its neural model counterpart, detailing the alteration ",
      "updated": "2019-10-23T09:25:59Z",
      "categories": [
        "q-bio.NC",
        "cs.AI",
        "cs.NE",
        "q-bio.NC"
      ]
    }
  ],
  "count": 5,
  "query": "neural networks",
  "source": "arxiv",
  "success": true,
  "elapsed_ms": 553
}

{
  "type": "json"
}