essay

artificial neural network thesis

Help | Advanced Search

Computer Science > Computation and Language

Title: explaining deep neural networks.

Abstract: Deep neural networks are becoming more and more popular due to their revolutionary success in diverse areas, such as computer vision, natural language processing, and speech recognition. However, the decision-making processes of these models are generally not interpretable to users. In various domains, such as healthcare, finance, or law, it is critical to know the reasons behind a decision made by an artificial intelligence system. Therefore, several directions for explaining neural models have recently been explored. In this thesis, I investigate two major directions for explaining deep neural networks. The first direction consists of feature-based post-hoc explanatory methods, that is, methods that aim to explain an already trained and fixed model (post-hoc), and that provide explanations in terms of input features, such as tokens for text and superpixels for images (feature-based). The second direction consists of self-explanatory neural models that generate natural language explanations, that is, models that have a built-in module that generates explanations for the predictions of the model.

Submission history

Access paper:.

  • Other Formats

References & Citations

  • Google Scholar
  • Semantic Scholar

DBLP - CS Bibliography

Bibtex formatted citation.

BibSonomy logo

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

  • Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

IMAGES

  1. High Quality Artificial Neural Network Thesis [Professional PhD Writers]

    artificial neural network thesis

  2. Understanding Artificial Neural network (ANN)

    artificial neural network thesis

  3. Research Artificial Neural Network Thesis Topics (Ideas)

    artificial neural network thesis

  4. Introduction to Neural Networks and Their Key Elements…

    artificial neural network thesis

  5. An Illustrated Guide to Artificial Neural Networks

    artificial neural network thesis

  6. Artificial neural network model diagram a feed forward neural network b

    artificial neural network thesis

VIDEO

  1. Neural Network: Models of artificial neural netwok

  2. Artificial Neural Network Training _ ANN Training

  3. Demystifying Neural Networks: Understanding their Layers and Role in AI

  4. 3MT EANN Chaitanya

  5. Graph Neural Networks vs. Traditional Methods for Recommending MOOC Courses

  6. Artificial Neural Network

COMMENTS

  1. Approximation with Artificial Neural Networks

    An artificial neural network is a (simplified) mathematical model of the human brain. Many different types of neural network models are suited, but we shall describe just one, called feed-forward ...

  2. IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS

    An artificial neural network (ANN) is a set of layers of neurons (in this context they are called units or nodes). In the case of a fully connected ANN, each unit in a layer is connected to each unit in the next layer (Figure 2). FIGURE 2. The artificial neural network architecture (ANN i-h 1-h 2-h n-o). (5.)

  3. [2010.01496] Explaining Deep Neural Networks

    In various domains, such as healthcare, finance, or law, it is critical to know the reasons behind a decision made by an artificial intelligence system. Therefore, several directions for explaining neural models have recently been explored. In this thesis, I investigate two major directions for explaining deep neural networks.