Hopfield Networks Meet Big Data: A Brain-Inspired Deep Learning Framework for Semantic Data Linking

• URL: http://arxiv.org/abs/2503.03084v1
• Date: Wed, 05 Mar 2025 00:53:22 GMT
• Title: Hopfield Networks Meet Big Data: A Brain-Inspired Deep Learning Framework for Semantic Data Linking
• Authors: Ashwin Viswanathan Kannan, Johnson P Thomas, Abhimanyu Mukerji,
• Abstract summary: This paper presents a brain-inspired distributed cognitive framework that integrates deep learning with Hopfield networks to identify and link semantically related attributes across datasets.<n>Our architecture, implemented on MapReduce with Hadoop Distributed File System (HDFS), leverages deep Hopfield networks as an associative memory mechanism.<n> Experiments show that associative imprints in Hopfield memory are reinforced over time, ensuring linked datasets remain contextually meaningful.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exponential rise in data generation has led to vast, heterogeneous datasets crucial for predictive analytics and decision-making. Ensuring data quality and semantic integrity remains a challenge. This paper presents a brain-inspired distributed cognitive framework that integrates deep learning with Hopfield networks to identify and link semantically related attributes across datasets. Modeled on the dual-hemisphere functionality of the human brain, the right hemisphere assimilates new information while the left retrieves learned representations for association. Our architecture, implemented on MapReduce with Hadoop Distributed File System (HDFS), leverages deep Hopfield networks as an associative memory mechanism to enhance recall of frequently co-occurring attributes and dynamically adjust relationships based on evolving data patterns. Experiments show that associative imprints in Hopfield memory are reinforced over time, ensuring linked datasets remain contextually meaningful and improving data disambiguation and integration accuracy. Our results indicate that combining deep Hopfield networks with distributed cognitive processing offers a scalable, biologically inspired approach to managing complex data relationships in large-scale environments.

Related papers

• A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap [50.079224604394]
  We present a novel model-agnostic framework called textbfContext-textbfEnhanced textbfFeature textbfAment (CEFA) CEFA consists of a feature alignment module and a context enhancement module. Our method can serve as a plug-and-play module to improve the detection performance of HOI models on rare categories.
  arXiv  Detail & Related papers  (2024-07-31T08:42:48Z)
• Autoencoder based approach for the mitigation of spurious correlations [2.7624021966289605]
  Spurious correlations refer to erroneous associations in data that do not reflect true underlying relationships. These correlations can lead deep neural networks (DNNs) to learn patterns that are not robust across diverse datasets or real-world scenarios. We propose an autoencoder-based approach to analyze the nature of spurious correlations that exist in the Global Wheat Head Detection (GWHD) 2021 dataset.
  arXiv  Detail & Related papers  (2024-06-27T05:28:44Z)
• BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model [6.888608574535993]
  BiSHop handles the two major challenges of deep tabular learning: non-rotationally invariant data structure and feature sparsity in data. BiSHop uses a dual-component approach, sequentially processing data both column-wise and row-wise. We show BiSHop surpasses current SOTA methods with significantly less HPO runs.
  arXiv  Detail & Related papers  (2024-04-04T23:13:32Z)
• Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data [2.193937336601403]
  We present a novel modeling architecture called BrainRGIN for predicting intelligence (fluid, crystallized, and total intelligence) using graph neural networks on rsfMRI derived connectivity matrices. Our approach incorporates a clustering-based embedding and graph isomorphism network in the graph convolutional layer to reflect the nature of the brain sub-network organization.
  arXiv  Detail & Related papers  (2023-11-06T20:58:07Z)
• A Framework for Inference Inspired by Human Memory Mechanisms [9.408704431898279]
  We propose a PMI framework that consists of perception, memory and inference components. The memory module comprises working and long-term memory, with the latter endowed with a higher-order structure to retain extensive and complex relational knowledge and experience. We apply our PMI to improve prevailing Transformers and CNN models on question-answering tasks like bAbI-20k and Sort-of-CLEVR datasets.
  arXiv  Detail & Related papers  (2023-10-01T08:12:55Z)
• Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs [59.96266198512243]
  We propose a decentralized and federated learning algorithm for tasks that are positively and negatively correlated. Our algorithm uses gradients to calculate the correlations among tasks automatically, and dynamically adjusts the communication graph to connect mutually beneficial tasks and isolate those that may negatively impact each other. We conduct experiments on a synthetic Gaussian dataset and a large-scale celebrity attributes (CelebA) dataset.
  arXiv  Detail & Related papers  (2022-12-21T18:58:24Z)
• Brain-like combination of feedforward and recurrent network components achieves prototype extraction and robust pattern recognition [0.0]
  Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. We combine a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule. We demonstrate that the recurrent attractor component implements associative memory when trained on the feedforward-driven internal (hidden) representations.
  arXiv  Detail & Related papers  (2022-06-30T06:03:11Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
  We propose a topological perspective to represent a network into a complete graph for analysis. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
  arXiv  Detail & Related papers  (2020-08-19T04:53:31Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

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.