Graph-CNN for 3D point cloud classification tackles non-regular graph topology.
problem Classifying 3D point cloud data with non-regular graph topology.
method Developed PointGCN combining localized graph convolutions and graph downsampling.
result Achieves competitive performance on 3D object classification benchmark ModelNet.
This paper analyzes CNNs for malware detection in cloud IaaS.
problem Malware vulnerability in cloud IaaS environments.
method Analysis of Convolutional Neural Networks (CNNs) for online malware detection using process-level performance metrics.
result State-of-the-art DenseNets and ResNets effectively detect malware in online cloud systems.
Enhances CNN generalization in early learning with hierarchical transfer.
problem Improving CNN generalization in limited training time for real-time applications.
method Hierarchical transfer CNN framework combining shallow and cloud CNNs.
result Significant improvement in testing accuracy, up to 20% for CIFAR-10.
SPINN optimizes neural network inference on devices and cloud.
problem Inference on mobile devices is challenging due to high computational demands and dynamic connectivity.
method Synergistic progressive inference with a novel scheduler.
result SPINN achieves up to 2x higher throughput and reduces server cost by up to 6.8x.
Collage inference uses redundancy to reduce cloud image classification latency variance.
problem Reducing latency variance in cloud image classification.
method Integrates collage-cnn for low-cost redundancy in multi-image classification.
result Significant reduction in 99th percentile tail latency and inference latency variation.
Collage-CNN reduces cloud inference latency by 1.47X with 9X reduced latency variation.
problem Reducing latency variance in cloud machine learning inference.
method Proposes a novel Collage-CNN model that combines multiple images for classification, providing redundancy and cost efficiency.
result Significant reduction in 99th percentile tail latency and variation in inference latency.
System detects multiple patients' behaviors in real-time using mmWave radar and CNN.
problem Real-time patient behavior monitoring in hospitals.
method Used mmWave radar for tracking and collecting Doppler patterns. Created a three-layer CNN model for behavior classification.
result System achieved very good inference accuracy in predicting patient behaviors in real-time.
Paper uses CNN to create realistic LiDAR data.
problem Creating high-resolution LiDAR data with missing points.
method Modified per-point loss and perceptual loss applied to generate realistic LiDAR data.
result Significant improvement in geometric and semantic accuracy over traditional methods.
Improved CNN accuracy for encrypted data using approximate activation functions.
problem Low accuracy in classifying encrypted data using homomorphic encryption.
method Used a fourth-order polynomial approximation of the Swish activation function with batch normalization for homomorphic encryption.
result Achieved 99.22% accuracy on MNIST and 80.48% on CIFAR-10, improving by 0.04% and 4.11% respectively.
Pyrocast predicts pyrocumulonimbus clouds six hours before they form.
problem Predicting pyrocumulonimbus clouds to adapt to climate change.
method Database of 148 pyroCb events, Random Forests, CNNs, and Auto-Encoders.
result Best model predicts pyroCb with 90% accuracy.
LatticeNet segments 3D point clouds faster and more efficiently.
problem Challenges in applying CNNs to 3D point cloud data.
method Embeds point cloud geometry into a permutohedral lattice for fast convolutions.
result Achieves state-of-the-art performance in 3D segmentation.
A new CNN method for point cloud data.
problem Processing 3D point cloud data efficiently.
method Creating a mapping of nearest neighbors and applying weights to spatial relationships.
result Achieves a CNN-like architecture for point clouds without extensive feature engineering.
Paper reduces false positives in lung nodule detection using deep learning on point clouds.
problem Reduces false positives in lung nodule detection.
method Uses deep learning models for point clouds to transform 3D CT scan data.
result Achieved 85.98 FROC compared to 77.26 FROC for baseline models.
A new method compresses point clouds efficiently, outperforming existing techniques.
problem Efficiently compressing large point cloud datasets for VR applications.
method Learned convolutional transforms and uniform quantization for joint rate and distortion optimization.
result Significant rate-distortion improvement (51.5% BDBR savings) on Microsoft Voxelized Upper Bodies dataset.
Paper improves deep point cloud compression techniques.
problem Efficiently compressing 3D point cloud data for various applications.
method Integrates scale hyperprior model, deeper transforms, focal loss, and optimal thresholding.
result Achieves significant BD-PSNR gains over existing methods.
Paper proposes a method to compress deep learning models using PU setting and cloud data.
problem Compression and acceleration of deep learning models on portable devices.
method Positive-Unlabeled (PU) setting, attention-based multi-scale feature extractor, robust knowledge distillation.
result An efficient model can be obtained using only 8% of ImageNet data.
Federated learning on edge devices achieves high accuracy with minimal data exchange.
problem Training deep neural networks on edge devices while maintaining user privacy.
method Training CNN, LSTM, and MLP on MNIST data using federated learning on edge devices (Raspberry Pi4s). Experimentally tested on IID and non-IID samples.
result Up to 85% test accuracy achieved with 2 minutes of training time and <10 MB data exchange per device.
mm-Pose detects human skeletons in real-time using mmWave radar and CNNs.
problem Real-time human skeletal posture estimation in various scenarios.
method mmWave radar, radar-to-image representation, forked CNN architecture.
result Accurate predictions for human skeletal joints in 3D space.
Paper explores modern CNNs for IoT-based farms.
problem Insufficient insight from agricultural IoT data.
method Review of state-of-the-art CNN architectures and their applications.
result Benchmarking guide for selecting CNN architectures.
Proposes dynamic graph and node feature learning in GCNNs for better adaptability.
problem Fixed graphs for all GCNN layers limit adaptability to node feature structures.
method Dynamic graph and node feature learning using Mahalanobis distance metric.
result Superior performance in point clouds and citation networks.
Faster and accurate JPEG2000 image classification without reconstruction.
problem Efficiently classify j2k-compressed images without reconstructing them.
method Train a deep CNN using DWT coefficients directly from j2k-compressed images, using different augmentation techniques.
result Achieved faster and more accurate classification of j2k images without additional computation.
Paper introduces privacy-preserving deep-learning service.
problem Privacy loss in deep-learning-as-a-service.
method Homomorphic Encryption tailored for CNNs.
result Effectiveness of proposed privacy-preserving architecture.
New method reduces mobile device energy for image classification.
problem High energy consumption of CNNs on mobile devices.
method Global optimization of CNN architectures with Bayesian optimization.
result Reduces energy consumption by up to 6x compared to blackbox CNNs.
Copycat CNN learns from random data to mimic target models.
problem Protecting state-of-the-art CNNs from copying.
method Querying target CNN with random non-labeled data to create a fake dataset, then training a copycat network.
result Copycat CNNs can achieve similar performance to target models, even from non-problem domain data.
GMLS-Nets extend CNNs to unstructured data points.
problem Learning from irregularly spaced data points in science and engineering.
method Introducing GMLS for non-parametric estimation and parameterizing it for learning operators with unstructured stencils.
result GMLS-Nets provide a framework for functional regression and quantity prediction from unstructured data.
FedMA improves federated learning for neural nets, matching and averaging model elements.
problem Federated learning's communication burden and model performance.
method Layer-wise construction of shared model by matching and averaging hidden elements.
result FedMA outperforms state-of-the-art algorithms on deep CNN and LSTM architectures.
New method compresses LSTM networks using MPS tensor trains.
problem Challenges in maintaining performance of compressed RNNs.
method Use of MPS tensor trains for LSTM network compression.
result MPS tensor trains outperform MPOs in storage and inference time.
Proposes Neural Random Subspace for deep learning.
problem Combining random subspace method with deep learning.
method Neural Random Subspace (NRS) integrating deep learning and random subspace.
result Achieves superior performance on 35 datasets and improves recognition tasks.
Deep model forecasts correlated multivariate time series.
problem Forecasting correlated multivariate time series.
method Deep learning structural model using CNN-LSTM architecture.
result Model outperforms state-of-the-art methods in various time series data sets.
SGAS improves neural architecture search by choosing and pruning operations greedily.
problem NAS often fails to generalize in final evaluation.
method Divides search into sub-problems and chooses/prunes candidate operations greedily.
result SGAS finds state-of-the-art architectures with minimal computational cost.
FCN improves lidar cloud detection accuracy.
problem Segmenting lidar imagery into cloud locations.
method Semi-supervised learning with pre-training and fully supervised learning.
result FCN achieves higher cloud identification accuracy.
Study uses CNNs to upscale wind speed data from 100 km to 3 km, improving subgrid-scale variability.
problem Recovering fine-scale wind speed information from coarse data.
method Convolutional neural networks (CNNs) with different input configurations (coarse wind speed, fine-scale topography, diurnal cycle) were tested.
result CNN models with coarse wind and fine topography inputs perform best in generalizing to unseen regions.
Generative model for 3D point clouds using invertible flows.
problem Generating realistic 3D point clouds.
method Invertible flow-based models for point cloud generation with parameter sharing and embedding vectors.
result The model generates high-quality 3D point clouds with good similarity.
Graph neural network constructs a sparse latent point cloud from dense point clouds.
problem Efficiently reconstructing and simulating point clouds with fine details.
method Irregular graph convolutional neural network with non-isotropic operations.
result The model can reconstruct dense point clouds from a sparse latent representation.
Personalized deep learning reduces inappropriate shocks in VA detection.
problem High inappropriate shock rate in traditional VA detection methods.
method Personalized deep learning framework using CNN for real-time VA detection and collaborative inference.
result 6.6% reduction in inappropriate shock rate compared to traditional methods.
New method upsamples sparse, non-uniform point clouds more accurately.
problem Suboptimal results from existing point cloud upsampling methods.
method Imposes manifold distribution constraints using Gaussian functions.
result Generates higher-quality, more uniformly distributed dense point clouds.
In recent decades, the use of 3D point clouds has been widespread in computer industry. The development of techniques in analyzing point clouds is increasingly important. In particular, mapping of point clouds has been a challenging problem. In this paper, we develop a discrete analogue of the Teichmüller extremal mapp…
Framework optimizes cloud container sizing for ML tasks.
problem Challenges in cloud container configuration for ML services.
method Autonomous scaling using nested-loop Monte Carlo simulation.
result Reduces compute cost and accelerates ML algorithms.
Study compares cloud ML services for binary classification tasks.
problem Evaluate performance of major cloud ML services on binary classification.
method Constructed benchmark using Kaggle datasets; compared Azure and Amazon services.
result Identifies strengths and weaknesses of current cloud ML services.
Convolutional LSTMs classify clouds as noise, improving remote sensing accuracy.
problem Clouds hinder remote sensing accuracy; current methods are inadequate.
method Used a Convolutional LSTM network to classify clouds as noise.
result Network internalizes cloud-filtering mechanism without explicit training.
Proposes a multi-level learning approach for 3D object recognition.
problem Improving 3D object recognition accuracy through multi-scale spatial features.
method End-to-end multi-level learning on a multi-level voxel grid.
result Comparable object recognition performance with lower memory usage.
A novel method compresses point cloud attributes by folding them onto a 2D grid.
problem Efficiently compressing point cloud attributes for storage and transmission.
method Interpreting point clouds as 2D manifolds, folding onto a grid, and mapping attributes to the grid using optimized methods.
result The proposed folding-based approach achieves performance comparable to state-of-the-art codecs.
Self-supervised method learns from unlabelled point clouds by reconstructing them.
problem Efficiently learning from large, unlabelled 3D point cloud datasets.
method Trains neural networks to reconstruct point clouds with randomly rearranged parts.
result Method learns semantic properties of point clouds and improves downstream object classification.
Efficiently synthesizes atmospheric cloud images using neural networks and Monte Carlo integration.
problem Rendering atmospheric clouds, especially their characteristic silverlining and whiteness, is challenging.
method Pre-learning the radiant flux distribution from cloud exemplars and using a deep neural network to predict radiance.
result The method synthesizes clouds nearly indistinguishable from reference solutions in seconds.
A novel method compares 3D point clouds using information geometry.
problem Comparing 3D point clouds in machine learning applications.
method Interprets point clouds as probability density functions on a statistical manifold, using GMM and Modified Symmetric KL divergence.
result Demonstrates effectiveness through various case studies.
Equivariant neural network simplifies particle physics models.
problem Complexity and interpretability in particle physics classification.
method Lorentz group equivariant neural network architecture.
result Simplified, interpretable models with fewer parameters.
Deep learning detects cloud changes due to human aerosols.
problem Uncertainty in the effect of anthropogenic aerosols on cloud properties and Earth's energy balance.
method Deep convolutional neural networks to analyze cloud images.
result Identified and characterized specific cloud perturbations due to human aerosols.
Develops a method for conformal parameterization of point clouds without fixed boundaries.
problem Desirable distortion in fixed-boundary parameterizations of point clouds.
method Free-boundary conformal parameterization method involving approximation of point cloud Laplacian and boundary treatment.
result High-quality point cloud meshing achieved through the proposed method.