deepstruct -- linking deep learning and graph theory

• URL: http://arxiv.org/abs/2111.06679v1
• Date: Fri, 12 Nov 2021 11:58:13 GMT
• Title: deepstruct -- linking deep learning and graph theory
• Abstract summary: deepstruct connects deep learning models and graph theory. Deepstruct provides deep neural network models with different restrictions which can be created based on an initial graph.
• Score: 1.4504054468850667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: deepstruct connects deep learning models and graph theory such that different graph structures can be imposed on neural networks or graph structures can be extracted from trained neural network models. For this, deepstruct provides deep neural network models with different restrictions which can be created based on an initial graph. Further, tools to extract graph structures from trained models are available. This step of extracting graphs can be computationally expensive even for models of just a few dozen thousand parameters and poses a challenging problem. deepstruct supports research in pruning, neural architecture search, automated network design and structure analysis of neural networks.

Related papers

• Latent Graph Learning in Generative Models of Neural Signals [0.6774024053289015]
  Inferring temporal interaction graphs and higher-order structure from neural signals is a key problem in building generative models for systems neuroscience.<n>Here we explore latent graph learning in generative models of neural signals.
  arXiv  Detail & Related papers  (2025-08-22T20:19:56Z)
• Introduction to Graph Neural Networks: A Starting Point for Machine Learning Engineers [0.0]
  Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges. This survey introduces graph neural networks through the encoder-decoder framework and provides examples of decoders for a range of graph analytic tasks.
  arXiv  Detail & Related papers  (2024-12-27T03:13:02Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Neural Architecture Retrieval [27.063268631346713]
  We define a new problem Neural Architecture Retrieval which retrieves a set of existing neural architectures with similar designs to the query neural architecture. Existing graph pre-training strategies cannot address the computational graph in neural architectures due to the graph size and motifs. We introduce multi-level contrastive learning to achieve accurate graph representation learning.
  arXiv  Detail & Related papers  (2023-07-16T01:56:41Z)
• Neuro-symbolic computing with spiking neural networks [0.6035125735474387]
  We extend previous work on spike-based graph algorithms by demonstrating how symbolic and multi-relational information can be encoded using spiking neurons. The introduced framework is enabled by combining the graph embedding paradigm and the recent progress in training spiking neural networks using error backpropagation.
  arXiv  Detail & Related papers  (2022-08-04T10:49:34Z)
• Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model [64.29487107585665]
  Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning.
  arXiv  Detail & Related papers  (2022-07-14T20:03:52Z)
• Machine learning of percolation models using graph convolutional neural networks [1.0499611180329804]
  Prediction of percolation thresholds with machine learning methods remains challenging. We build a powerful graph convolutional neural network to study the percolation in both supervised and unsupervised ways.
  arXiv  Detail & Related papers  (2022-07-07T15:17:40Z)
• Hyperbolic Graph Neural Networks: A Review of Methods and Applications [55.5502008501764]
  Graph neural networks generalize conventional neural networks to graph-structured data. The performance of Euclidean models in graph-related learning is still bounded and limited by the representation ability of Euclidean geometry. Recently, hyperbolic space has gained increasing popularity in processing graph data with tree-like structure and power-law distribution.
  arXiv  Detail & Related papers  (2022-02-28T15:08:48Z)
• Learning through structure: towards deep neuromorphic knowledge graph embeddings [0.5906031288935515]
  We propose a strategy to map deep graph learning architectures for knowledge graph reasoning to neuromorphic architectures. Based on the insight that randomly and untrained graph neural networks are able to preserve local graph structures, we compose a frozen neural network shallow knowledge graph embedding models. We experimentally show that already on conventional computing hardware, this leads to a significant speedup and memory reduction while maintaining a competitive performance level.
  arXiv  Detail & Related papers  (2021-09-21T18:01:04Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Graph Structure of Neural Networks [104.33754950606298]
  We show how the graph structure of neural networks affect their predictive performance. A "sweet spot" of relational graphs leads to neural networks with significantly improved predictive performance. Top-performing neural networks have graph structure surprisingly similar to those of real biological neural networks.
  arXiv  Detail & Related papers  (2020-07-13T17:59:31Z)
• Neural networks adapting to datasets: learning network size and topology [77.34726150561087]
  We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a gradient-based training. The resulting network has the structure of a graph tailored to the particular learning task and dataset.
  arXiv  Detail & Related papers  (2020-06-22T12:46:44Z)
• Hcore-Init: Neural Network Initialization based on Graph Degeneracy [22.923756039561194]
  We propose an adapted version of the k-core structure for the complete weighted multipartite graph extracted from a deep learning architecture. As a multipartite graph is a combination of bipartite graphs, that are in turn the incidence graphs of hypergraphs, we design k-hypercore decomposition.
  arXiv  Detail & Related papers  (2020-04-16T12:57:14Z)
• Analyzing Neural Networks Based on Random Graphs [77.34726150561087]
  We perform a massive evaluation of neural networks with architectures corresponding to random graphs of various types. We find that none of the classical numerical graph invariants by itself allows to single out the best networks. We also find that networks with primarily short-range connections perform better than networks which allow for many long-range connections.
  arXiv  Detail & Related papers  (2020-02-19T11:04:49Z)

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.