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Showing 1 to 4 of 4 for “"graph convolutions"”.

  1. Molecular graph Self attention and graph convolution for drug discovery

    … development. We model molecules as undirected graphs and use graph convolutions and self-attention to predict molecular properties. With a series of ablation studies, we demonstrate the added value of several key components in our network. We analyze two standard datasets: BBBP, which includes …

    mit Repository record for Molecular graph Self attention and graph convolution for drug discovery (opens in a new tab)

  2. A graph-based framework for information extraction

    … predictions. In this thesis, we introduce a graph-based framework (GraphIE) that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating …

    mit Repository record for A graph-based framework for information extraction (opens in a new tab)

  3. Efficient Deep Learning Systems for Visual Perception on the Edge

    … driving benchmarks. It also seamlessly supports graph convolutions, achieving 2.6-7.6× faster inference speed compared with state-of-the-art graph deep learning libraries. Furthermore, to democratize the power of large foundation models in edge AI, we propose AWQ and TinyChat, a hardware-friendly …

    mit Repository record for Efficient Deep Learning Systems for Visual Perception on the Edge (opens in a new tab)

  4. The resurgence of structure in deep neural networks

    … (operating on sparse multimodal and graph-structured data), and a structure-informed learning algorithm for graph neural networks, demonstrating significant outperformance of conventional baseline models and algorithms.

    cambridge Repository record for The resurgence of structure in deep neural networks (opens in a new tab)