Incorporation of Deep Neural Network & Reinforcement Learning with Domain Knowledge

• URL: http://arxiv.org/abs/2107.14613v1
• Date: Thu, 29 Jul 2021 17:29:02 GMT
• Title: Incorporation of Deep Neural Network & Reinforcement Learning with Domain Knowledge
• Authors: Aryan Karn, Ashutosh Acharya
• Abstract summary: We present a study of the manners by which Domain information has been incorporated when building models with Neural Networks. Integrating space data is uniquely important to the development of Knowledge understanding model, as well as other fields that aid in understanding information by utilizing the human-machine interface and Reinforcement Learning.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We present a study of the manners by which Domain information has been incorporated when building models with Neural Networks. Integrating space data is uniquely important to the development of Knowledge understanding model, as well as other fields that aid in understanding information by utilizing the human-machine interface and Reinforcement Learning. On numerous such occasions, machine-based model development may profit essentially from the human information on the world encoded in an adequately exact structure. This paper inspects expansive ways to affect encode such information as sensible and mathematical limitations and portrays methods and results that came to a couple of subcategories under all of those methodologies.

Related papers

• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Pruning neural network models for gene regulatory dynamics using data and domain knowledge [24.670514977455202]
  We propose DASH, a framework that guides network pruning by using domain-specific structural information in model fitting. We show that DASH, using knowledge about gene interaction partners within the putative regulatory network, outperforms general pruning methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-05T23:02:55Z)
• Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations [1.9580473532948401]
  This thesis explores the theoretical foundations of deep learning by studying the relationship between the architecture of these models and the inherent structures found within the data they process. We ask What drives the efficacy of deep learning algorithms and allows them to beat the so-called curse of dimensionality. Our methodology takes an empirical approach to deep learning, combining experimental studies with physics-inspired toy models.
  arXiv  Detail & Related papers  (2023-10-24T19:50:41Z)
• Experimental Observations of the Topology of Convolutional Neural Network Activations [2.4235626091331737]
  Topological data analysis provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture. In this paper, we apply cutting edge techniques from TDA with the goal of gaining insight into the interpretability of convolutional neural networks used for image classification.
  arXiv  Detail & Related papers  (2022-12-01T02:05:44Z)
• How to Tell Deep Neural Networks What We Know [2.2186394337073527]
  This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. In each category, we describe techniques that have been shown to yield significant changes in network performance.
  arXiv  Detail & Related papers  (2021-07-21T18:18:02Z)
• A neural anisotropic view of underspecification in deep learning [60.119023683371736]
  We show that the way neural networks handle the underspecification of problems is highly dependent on the data representation. Our results highlight that understanding the architectural inductive bias in deep learning is fundamental to address the fairness, robustness, and generalization of these systems.
  arXiv  Detail & Related papers  (2021-04-29T14:31:09Z)
• Incorporating Domain Knowledge into Deep Neural Networks [2.2186394337073527]
  The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also to many other areas that involve understanding data using human-machine collaboration. This paper examines two broad approaches to encode such knowledge--as logical and numerical constraints--and describes techniques and results obtained in several sub-categories under each of these approaches.
  arXiv  Detail & Related papers  (2021-02-27T10:39:43Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning [95.27249880156256]
  We develop and present a unified architecture of network community-finding methods. We introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
  arXiv  Detail & Related papers  (2021-01-03T02:32:45Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence [48.67755344239951]
  We provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping. It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities.
  arXiv  Detail & Related papers  (2020-06-22T19:01:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.