HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding

• URL: http://arxiv.org/abs/2402.08961v3
• Date: Mon, 04 Nov 2024 09:13:45 GMT
• Title: HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding
• Authors: Zhao Li, Xin Wang, Jun Zhao, Wenbin Guo, Jianxin Li,
• Abstract summary: It is desirable and challenging for knowledge hypergraph embedding to reach a trade-off between model effectiveness and efficiency. We propose an end-to-end efficient knowledge hypergraph embedding model, HyCubE, which designs a novel 3D circular convolutional neural network. Our proposed model consistently outperforms state-of-the-art baselines, with an average improvement of 8.22% and a maximum improvement of 33.82%.
• Score: 21.479738859698344
• License:
• Abstract: Knowledge hypergraph embedding models are usually computationally expensive due to the inherent complex semantic information. However, existing works mainly focus on improving the effectiveness of knowledge hypergraph embedding, making the model architecture more complex and redundant. It is desirable and challenging for knowledge hypergraph embedding to reach a trade-off between model effectiveness and efficiency. In this paper, we propose an end-to-end efficient knowledge hypergraph embedding model, HyCubE, which designs a novel 3D circular convolutional neural network and the alternate mask stack strategy to enhance the interaction and extraction of feature information comprehensively. Furthermore, our proposed model achieves a better trade-off between effectiveness and efficiency by adaptively adjusting the 3D circular convolutional layer structure to handle n-ary knowledge tuples of different arities with fewer parameters. In addition, we use a knowledge hypergraph 1-N multilinear scoring way to accelerate the model training efficiency further. Finally, extensive experimental results on all datasets demonstrate that our proposed model consistently outperforms state-of-the-art baselines, with an average improvement of 8.22% and a maximum improvement of 33.82% across all metrics. Meanwhile, HyCubE is 6.12x faster, GPU memory usage is 52.67% lower, and the number of parameters is reduced by 85.21% compared with the average metric of the latest state-of-the-art baselines.

Related papers

• Efficient Ternary Weight Embedding Model: Bridging Scalability and Performance [15.877771709013743]
  In this work, we propose a novel finetuning framework to ternary-weight embedding models. To apply ternarization to pre-trained embedding models, we introduce self-taught knowledge distillation to finalize the ternary-weights of the linear layers. With extensive experiments on public text and vision datasets, we demonstrated that without sacrificing effectiveness, the ternarized model consumes low memory usage.
  arXiv  Detail & Related papers  (2024-11-23T03:44:56Z)
• Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data [59.6985168241067]
  Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. We propose a new FL framework, FedDUMAP, to leverage the shared insensitive data on the server and the distributed data in edge devices. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches.
  arXiv  Detail & Related papers  (2024-08-11T02:59:11Z)
• Trimming the Fat: Efficient Compression of 3D Gaussian Splats through Pruning [17.097742540845672]
  "Trimming the fat" is a post-hoc gradient-informed iterative pruning technique to eliminate redundant information encoded in the model. Our approach achieves around 50$times$ compression while preserving performance similar to the baseline model, and is able to speed-up computation up to 600 FPS.
  arXiv  Detail & Related papers  (2024-06-26T09:57:55Z)
• Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation [65.62538699160085]
  We propose a Directed Acyclic Graph Factorization Machine (KD-DAGFM) to learn the high-order feature interactions from existing complex interaction models for CTR prediction via Knowledge Distillation. KD-DAGFM achieves the best performance with less than 21.5% FLOPs of the state-of-the-art method on both online and offline experiments.
  arXiv  Detail & Related papers  (2022-11-21T03:09:42Z)
• Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction [6.789370732159177]
  Self-attention is the key to applying query-dependant projections to entities and relations. Our model achieves favorably comparable or better performance than our three best recent state-of-the-art competitors.
  arXiv  Detail & Related papers  (2021-12-20T16:11:01Z)
• Powerpropagation: A sparsity inducing weight reparameterisation [65.85142037667065]
  We introduce Powerpropagation, a new weight- parameterisation for neural networks that leads to inherently sparse models. Models trained in this manner exhibit similar performance, but have a distribution with markedly higher density at zero, allowing more parameters to be pruned safely. Here, we combine Powerpropagation with a traditional weight-pruning technique as well as recent state-of-the-art sparse-to-sparse algorithms, showing superior performance on the ImageNet benchmark.
  arXiv  Detail & Related papers  (2021-10-01T10:03:57Z)
• Causal Incremental Graph Convolution for Recommender System Retraining [89.25922726558875]
  Real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN) based recommender models.
  arXiv  Detail & Related papers  (2021-08-16T04:20:09Z)
• Constructing Stronger and Faster Baselines for Skeleton-based Action Recognition [19.905455701387194]
  We present an efficient Graph Convolutional Network (GCN) baseline for skeleton-based action recognition. On two large-scale datasets, i.e., NTU RGB+D 60 and 120, the proposed EfficientGCN-B4 baseline outperforms other State-Of-The-Art (SOTA) methods.
  arXiv  Detail & Related papers  (2021-06-29T07:09:11Z)
• Mix Dimension in Poincar\'{e} Geometry for 3D Skeleton-based Action Recognition [57.98278794950759]
  Graph Convolutional Networks (GCNs) have already demonstrated their powerful ability to model the irregular data. We present a novel spatial-temporal GCN architecture which is defined via the Poincar'e geometry. We evaluate our method on two current largest scale 3D datasets.
  arXiv  Detail & Related papers  (2020-07-30T18:23:18Z)
• Towards Practical Lipreading with Distilled and Efficient Models [57.41253104365274]
  Lipreading has witnessed a lot of progress due to the resurgence of neural networks. Recent works have placed emphasis on aspects such as improving performance by finding the optimal architecture or improving generalization. There is still a significant gap between the current methodologies and the requirements for an effective deployment of lipreading in practical scenarios. We propose a series of innovations that significantly bridge that gap: first, we raise the state-of-the-art performance by a wide margin on LRW and LRW-1000 to 88.5% and 46.6%, respectively using self-distillation.
  arXiv  Detail & Related papers  (2020-07-13T16:56:27Z)

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.