Dimensional Reweighting Graph Convolutional Networks

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Ensembling Insights for Baseline Text Models

(Mikolov et al.,2013) and GloVe (Pennington et al.,2014) - and deep neural networks - such as long short-term memory networks (Hochreiter and Schmidhuber,1997) and convolutional neural networks (Collobert and Weston,2008) - became the fashionable approach for text classification.

33rd AAAI Conference on Artificial Intelligence (AAAI-19)

ISB N: 978-1-7138-2859-4 33rd AAAI Conference on Artificial Intelligence (AAAI-19) Honolulu, Hawaii, USA 27 January 1 February 2019 Vision

arXiv:2012.11400v1 [cs.SI] 21 Dec 2020

Networks are ubiquitous in the real world, such as social net-works [Fortunato, 2010], biological networks [Girvan and Newman, 2002] and traffic networks [Asif et al., 2016]. Network embedding (a.k.a. graph embedding) learns low-dimensional latent representations of nodes while preserving (a) Homophily (b) Heterophily (c) Hybrid (proposed)

10-701 Introduction to Machine Learning Midterm Exam

4 Deep Neural Networks - 10 points In homework 3, we counted the model parameters of a convolutional neural network (CNN), which gives us a sense how much memory a CNN will consume. Now we estimate the computation overhead of CNNs by counting the FLOPs (oating point operations). For simplicity we only consider the forward pass.

Frontiers in GNN and Network Embedding

Graph Neural network vs. Network embedding In some sense, they are different Graphs exist in mathematics (Data Structure) Mathematical structures used to model pairwise relations between objects Networks exist in the real world (Data) Social networks, logistic networks, biology networks, transaction networks, etc.

Gradient Descent with Early Stopping is Provably Robust to

When learning from pairwise relations, noisy labels can be connected to graph clustering and community detection problems [1,14,54]. Label noise is also connected to outlier robustness in regression which is a traditionally well-studied topic. In the context of robust regression and high-dimensional statistics, much of


and methods proposed in recent years using convolutional neural networks [10 12]. Xu et al. [2] proposed a new regularizer to approximate the L0 cost in order to recover the latent image and blur ker-nel. Krishnan et al. [4] used an L1=L2 regularization scheme which adapts L1 regularization by reweighting the iterations

Improved protein structure prediction using predicted

former part of the computation graph in TensorFlow (13). Sequence reweighting, calculation of one-site amino acid frequen-cies, entropies, and coevolutionary couplings and related scores take place on the GPU, and the extracted features are passed into the convolutional layers of the network (most previous approaches have precomputed these terms).

Graph convolutional networks for learning with few clean and

computing eigenvectors. Graph convolutional networks (GCN) [17] provide a further simpli cation by a rst-order approximation of graph ltering and are applied to semi-supervised [17] and subsequently few-shot learning [8]. Kipf and Welling [17] apply the loss function to labeled examples to make predictions on unlabeled ones.

Graph Neural Networks Inspired by Classical Iterative Algorithms

Graph Neural Networks Inspired by Classical Iterative Algorithms rithms. In doing so, our design benefits from the following: All architectural components are in one-to-one corre-spondence with the unfolded iterations of robust descent algorithms applied to minimizing a principled graph-regularized energy function. Specifically, we adopt

3DCFS: Fast and Robust Joint 3D Semantic-Instance

3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection Liang Du y1, Jingang Tan 2, Xiangyang Xue3, Lili Chen , Hongkai Wen 1;4, Jianfeng Feng , Jiamao Li2 and

Learning Representations for Images with Hierarchical Labels

Convolutional Neural Networks based models (modified CNN architectures) Attention-based models* Predict labels for each level with a separate neural-network + * See Better Before Looking Closer; T Hu, et al. + Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding, T Chen, et al. 10

Under review as a conference paper at ICLR 2020

We propose Dimensional reweighting Graph Convolutional Networks (DrGCNs), in which the input of each layer of the GCN is reweighted by global node representation information. Our discovery is that the experimental performance of GCNs can be greatly improved under this simple reweighting scheme.

Article An Efficient DenseNet Based Deep Learning Model for

Mar 15, 2021 The proposed methodology employs pretrained Densely Connected Convolutional Networks (DenseNet) to achieve faster preprocessing and training of binary samples. The DenseNet model allows for concatenation of features and utilizes fewer parameters compared to other CNN models.


Graph neural networks are generalizations of convolutional networks to non-Euclidean spaces (Bronstein et al., 2017). Early spectral methods (Bruna et al., 2014; Henaff et al., 2015) have been succeeded by Chebyshev polynomial approximations (Defferrard et al., 2016), which avoid the high computational cost of computing eigenvectors.