Graph CNNs adapt to varying graph structures for better performance.
problem Fixed graph structures limit the performance of Graph CNNs on real data.
method Adaptive graph learning and distance metric learning for efficient graph construction.
result Adaptive Graph CNNs improve convergence speed and predictive accuracy on various graph datasets.
A new framework combines CNN and GRU for better structural damage detection.
problem Improving damage detection in structural engineering using machine learning.
method Hierarchical CNN and Gated Recurrent Unit (GRU) framework to model spatial and temporal relations.
result The proposed HCG framework significantly outperforms existing methods for structural damage detection.
Transform classical network structures to graph CNN for better graph recognition.
problem Transforming classical network structures to graph CNN for better graph recognition.
method Review and introduce ResNet, Inception, and DenseNet into graph CNN, constructing G_ResNet, G_Inception, G_DenseNet.
result Demonstrated how different network structures work on graph CNN in the graph recognition task.
Deep structured output learning shows great promise in tasks like semantic image segmentation. We proffer a new, efficient deep structured model learning scheme, in which we show how deep Convolutional Neural Networks (CNNs) can be used to estimate the messages in message passing inference for structured prediction wit…
CNNs improve positioning accuracy in massive MIMO systems.
problem Improving positioning accuracy in massive MIMO systems.
method Applying CNNs to learn sparse massive MIMO channel fingerprints.
result Moderately deep CNNs achieve fractional-wavelength positioning accuracy.
Proposes interpretable filters in CNNs for object classification.
problem Learning interpretable filters in deep CNNs without additional annotations.
method Assigns each filter in a CNN to an object part during training.
result Interpretable filters are more semantically meaningful than traditional filters.
CNNs reveal retinal ganglion cell features, linking visual processing to neuroscience.
problem Understanding what CNNs learn about retinal neuronal circuits.
method Trained CNNs on white noise images to predict neural responses from salamander retinas.
result CNN filters resemble biological retinal components and ganglion cell receptive fields.
Efficiently learns CNN structures in order of complexity.
problem Finding optimal CNN structures efficiently.
method Sequential model-based optimization (SMBO) with surrogate model.
result 5-8 times more efficient than state-of-the-art methods.
This paper accelerates sparse CNN layers on GPUs by using unstructured sparsity.
problem Efficiency of sparse CNN layers on GPUs.
method Direct sparse operation and reduced precision.
result Achieving up to 90% sparsity in deep CNN models improves efficiency.
ResNet-type CNNs achieve optimal error rates in function classes with block-sparse structures.
problem Optimal approximation and estimation in function classes with sparse constraints.
method Developed ResNet-type CNNs that can approximate and estimate functions with block-sparse structures.
result ResNet-type CNNs attain minimax optimal error rates in Hölder and Barron classes.
The paper analyzes deep ReLU CNNs' approximation properties in 2D space.
problem Establishing L2 approximation properties for deep ReLU CNNs. method Analysis based on decomposition theorem for convolutional kernels, properties of ReLU activation, and connections with one-hidden-layer ReLU NNs.
result Universal approximation theorem for deep ReLU CNNs with classic structure.
Combines CNN and RNN for hierarchical image classification.
problem Hierarchical relations between image categories are not captured by flat classifiers.
method Uses a CNN for feature extraction and an RNN for capturing hierarchical class relations. Incorporates residual learning.
result Hierarchical networks outperform state-of-the-art CNNs on a real-world dataset.
CNNs use a bottleneck structure to focus on a few frequencies, affecting function representation.
problem Understanding how CNNs focus on specific frequencies in their feature learning.
method Defined Convolution Bottleneck (CBN) structure, measured CBN rank, and analyzed parameter norms.
result Parameter norm scales with depth and CBN rank, and networks with optimal parameters exhibit this structure.
q-CNN learns data features through entangled states.
problem Classifying MNIST and Fashion MNIST datasets.
method Introduces q-CNN model, a tensor network description, and studies entanglement structure.
result q-CNN learns entanglement structure to perform classification tasks accurately.
A hybrid CNN with shared parameters learns to incorporate recurrent structures.
problem Designing efficient recurrent neural networks.
method Parameter sharing scheme combining CNN and recurrent layers.
result Substantial parameter savings with competitive accuracy.
3D Steerable CNNs learn equivariant features for 3D data.
problem Learning rotationally equivariant features in volumetric data.
method SE(3)-equivariant convolutions using steerable kernel basis.
result 3D Steerable CNNs are effective for protein structure classification and amino acid propensity prediction.
Paper proposes a new technique to compress CNNs while maintaining accuracy.
problem CNNs struggle with traditional low-rank approximation methods, leading to degraded accuracy.
method Introduces a training technique that finds a flat minimum in low-rank approximation without a decomposed structure.
result CNN models can be compressed with higher accuracy and lower computation than conventional methods.
Interprets deep CNN models via meta-learning.
problem Non-interpretable deep CNN models in machine learning.
method Meta-learning approach using clustering and Random Forest.
result Achieves global interpretation without sacrificing accuracy.
New CNN method improves brain MR segmentation across scanners and protocols.
problem Degradation of CNN accuracy on images from different scanners and protocols.
method Lifelong multi-domain learning with shared filters and domain-specific batch normalization.
result Significantly closes the gap to benchmark performance.
Ego-CNN detects critical structures in graphs efficiently.
problem Lack of precise detection of critical structures in existing graph embedding models.
method Ego-CNN uses ego-convolutions at each layer and stacks them in an ego-centric way.
result Ego-CNN achieves comparable task performance to state-of-the-art models and can incorporate scale-free priors.
DGCNN improves graph CNNs by handling irregular graphs.
problem Handling structural information loss and redundancy in graph CNNs.
method Proposes DGCNN using DGCL with mixed Gaussian model to handle irregular graphs.
result DGCNN outperforms state-of-the-art methods in graph classification and retrieval.
Study on infinitely-wide CNNs and their adaptability to function spatial scales.
problem Understanding how CNNs efficiently learn high-dimensional functions and their adaptability to function spatial scales.
method Study infinitely-wide deep CNNs in the kernel regime, characterizing their spectrum and using generalisation bounds to prove adaptability.
result Deep CNNs adapt to the spatial scale of the target function, with error decay controlled by the effective dimensionality of function subsets.
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding an…
CNN predicts airfoil lift coefficients across various conditions.
problem Developing a CNN for predicting airfoil lift coefficients under different conditions.
method Trained multiple CNN architectures on airfoil lift coefficients with varying flow conditions and object geometries.
result CNN model shows competitive prediction accuracy with minimal geometric constraints.
Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead of using low-dimensional word ve…
CNN improves causal inference by controlling time-structured covariates.
problem Estimating the effect of early retirement on health outcomes while controlling for time-structured covariates.
method Used CNN to fit nuisance models explaining treatment and outcome, combining them into an augmented inverse probability weighting estimator.
result Uniformly valid inference achieved through CNN, providing rates of convergence and uniformly valid inference guarantees.
ResRep prunes CNNs without losing accuracy by separating remembering and forgetting.
problem Pruning CNNs to reduce FLOPs without sacrificing accuracy.
method Decoupling remembering and forgetting in CNNs, using SGD for remembering and a novel update rule for forgetting.
result Achieved lossless pruning with high compression ratio (76.15% accuracy on ImageNet with 45% FLOPs reduction).
CNNs adapted for graphs match image CNNs without prior knowledge.
problem Matching CNN performance on graph-structured data without prior knowledge.
method Strided convolutions and data augmentation on graphs.
result Significant accuracy improvement on fMRI data.
BCD-Net uses identical CNN structures for image recovery in undersampled imaging.
problem Challenges in obtaining accurate images from undersampled or noisy measurements.
method Incorporates image mapping CNN into BCD signal recovery method using alternating direction method of multipliers.
result Significantly more accurate image recovery compared to existing methods.
New framework uses causal inference to explain deep learning models.
problem Deep learning models are opaque and complex, making them hard to trust.
method Formalized a principled framework using causal inference to reason over CNN models.
result Quantitative ranking of convolution layer filters based on counterfactual importance.
B-Spline CNNs on Lie Groups expand G-CNNs to arbitrary groups.
problem Leveraging geometric structure for improved feature learning.
method Lifting feature maps to B-spline expansions on Lie algebra.
result G-CNNs on Lie groups outperform classical 2D CNNs.
Paper proposes a robust deep graph-based classifier for noisy labels.
problem Difficulty in feature learning with noisy training labels.
method Convolutional neural networks with graph Laplacian regularization (GLR).
result Proposed method outperforms state-of-the-art classifiers on noisy datasets.
Symmetric CNNs improve sequential recommendation and protein structure prediction.
problem Improving prediction accuracy in sequential recommendation and protein structure inference.
method Developed a CNN architecture that preserves symmetry in convolutional layers, using parameterized convolutional kernels.
result Symmetric structured CNNs achieve better performance with fewer parameters.
L-CNNs learn gauge invariant quantities on lattices.
problem Learning gauge invariant quantities on lattices.
method Novel convolutional layer preserving gauge equivariance and forming Wilson loops.
result L-CNNs can approximate any gauge covariant function on the lattice.
New bounds for CNNs show better generalization than previous models.
problem Improving understanding of CNNs' generalization ability.
method Proposed tighter generalization bounds for CNNs by exploiting the sparse and permutation structure of weight matrices and spectral norms of convolution operations.
result Theoretical and experimental results show tighter bounds for CNNs than existing bounds.
CNNs adapted for brain images predict age with improved accuracy.
problem Applying CNNs to brain images without considering their 3D structure.
method Two modifications to existing CNN architectures tailored for brain images.
result Achieved a mean absolute error (MAE) of 1.4 years compared to 1.6 years for a baseline.
Early diagnosis, playing an important role in preventing progress and treating the Alzheimer\{'}s disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocamp…
Study shows fewer samples needed for CNNs and RNNs than FNNs.
problem Estimating the number of samples needed for CNNs and RNNs.
method Localized empirical process analysis and new lemma characterizing CNNs and RNNs.
result Sample complexity scales linearly with intrinsic dimension for CNNs and RNNs.
DFNets uses feedback-looped filters for better graph CNN performance.
problem Improving CNN performance on graph structured data.
method DFNets incorporates feedback-looped spectral graph filters.
result DFNets outperforms state-of-the-art methods in document and entity classification tasks.
Early diagnosis, playing an important role in preventing progress and treating the Alzheimer's disease (AD), is based on classification of features extracted from brain images. The features have to accurately capture main AD-related variations of anatomical brain structures, such as, e.g., ventricles size, hippocampus …
A deep learning algorithm designs low-cost SPP films.
problem Designing SPP films with high accuracy and low cost.
method Guided deep learning algorithm with low-cost sample replacement.
result Deep CNN learns to replace precious metals with ordinary metals, reducing cost.
The paper analyzes deep neural networks using information theory to improve classification accuracy.
problem Improving classification accuracy in deep neural networks.
method Modeling the output of convolutional filters as a random variable conditioned on class and network structure, computing conditional entropy as a compact code.
result The conditional entropy feature analysis leads to higher classification accuracy than the original CNN.
New CNN initialization scheme derived from modern architectures.
problem Stability of CNN model parameters initialization.
method Derived new initialization scheme from modern CNN architectures.
result New initialization method outperforms de facto standard schemes.
Deep CNNs can approximate any function, proving their efficiency.
problem Understanding the approximation and generalization ability of deep learning methods.
method Demonstrated universality of deep convolutional neural networks (CNNs).
result Deep CNNs can approximate any continuous function to arbitrary accuracy.
New CNN approach detects cell nuclei with prior information.
problem Challenges in detecting cell nuclei due to image quality and morphology diversity.
method Develops SP-CNN with trainable shape prior layer to guide CNN learning.
result TSP-CNN outperforms state-of-the-art alternatives on challenging datasets.
New graph CNN layers improve accuracy on graph datasets.
problem Graph data relations are better represented as graphs, not grids.
method Proposed new graph CNN layers for vertex and edge features.
result Improved classification accuracy on graph datasets.
New CNN model for fast unsupervised document embedding.
problem Efficient unsupervised document embedding with parallelizable architecture.
method Convolutional Neural Network (CNN) for parallel inference and stochastic forward prediction for unsupervised learning.
result Comparable accuracy to state-of-the-art at significantly reduced computational cost.
NTS-NOTEARS learns DBNs from time-series data with prior knowledge.
problem Learning dynamic Bayesian networks from time-series data with nonlinear and lagged relationships.
method Uses 1D CNNs to model DBNs, incorporating prior knowledge as constraints.
result Achieves state-of-the-art DAG structure quality compared to parametric and nonparametric methods.