Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years. In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs. The method of …
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48 results for “Graph Downsampling”
Optimal downsampling improves GLM performance in imbalanced classification.
problem Improving GLM performance in imbalanced classification.
method Proposed a pseudo maximum likelihood estimator for optimal downsampling.
result The introduced estimator outperforms existing alternatives in both synthetic and empirical data.
Study reveals significant performance flips in GLOD using repurposed graph classification datasets.
problem Performance discrepancies in graph-level outlier detection using repurposed classification datasets.
method Repurposed binary classification datasets for GLOD; analyzed ROC-AUC performance.
result Performance of GLOD models significantly flips depending on which class is down-sampled.
Downsampling can improve generalization in ridgeless linear regression, especially with optimal sketching size.
problem Improving generalization in ridgeless linear regression with limited data.
method Investigating the effects of downsampling on the sketched ridgeless least square estimator in the proportional regime.
result Optimal sketching size minimizes out-of-sample prediction risks and stabilizes risk curves.
Faster CNN training with downsampling and pooling.
problem Reducing training time in CNNs.
method Interleaved training with downsampling and pooling.
result Up to 23% reduction in training time with minimal loss.
Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph. Major challenges when working with data on graphs are that the support set (the vertices of the graph) do not typically have a natural ordering, and in general, the topology of the graph is not regular …
Recent advances in representation learning on graphs, mainly leveraging graph convolutional networks, have brought a substantial improvement on many graph-based benchmark tasks. While novel approaches to learning node embeddings are highly suitable for node classification and link prediction, their application to graph…
MathNet uses wavelets for graph representation and learning.
problem Graph Neural Networks (GNNs) for graph classification and regression.
method Multiresolution Haar-like wavelets, graph convolution, and pooling.
result MathNet achieves notable accuracy gains on graph classification and regression tasks.
HGP-SL pools and learns graph structure for hierarchical representation learning.
problem Graph pooling is overlooked in GNN models, limiting hierarchical representation learning.
method Integrates graph pooling and structure learning into a unified module.
result HGP-SL improves graph classification performance on benchmarks.
ASAP improves graph pooling for hierarchical graph representations.
problem Pooling in graphs fails to effectively capture substructure or scale to large graphs.
method ASAP uses self-attention and modified GNN to capture node importance and learn sparse soft cluster assignments.
result Combining ASAP with GNN architectures leads to state-of-the-art results on graph classification benchmarks.
The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is s…
Spatial graph representation improves GNN performance.
problem GNNs struggle with distinguishing similar local structures in different graph locations.
method Proposes a spatial graph representation method to distinguish local structures and simplify graph downsampling.
result Proposed graph pooling method achieves competitive results.
Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components.…
On the design of convolutional neural networks for automatic detection of Alzheimer's diseaseeess.IV
Improved CNNs detect Alzheimer's with 14% accuracy boost.
problem Early detection of Alzheimer's Disease using MRI scans.
method Optimized 3D CNNs with instance normalization, spatial downsampling, model widening, and age information.
result 14% increase in test accuracy distinguishing AD, MCI, and controls.
In most convolution neural networks (CNNs), downsampling hidden layers is adopted for increasing computation efficiency and the receptive field size. Such operation is commonly so-called pooling. Maximation and averaging over sliding windows (max/average pooling), and plain downsampling in the form of strided convoluti…
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
problem Domain shift in low-resolution 4D Flow MRI data.
method Distributional deep learning framework for domain generalization.
result Framework significantly outperforms traditional methods in real data applications.
PaGoDA reduces diffusion model training costs by 64x.
problem Diffusion models are computationally expensive during training.
method Three-stage pipeline: downsampled training, distillation, progressive super-resolution.
result PaGoDA achieves state-of-the-art performance with reduced training costs.
A hybrid ASR system using conformer architecture improves word-error-rate and training speed.
problem Improving word-error-rate and training efficiency for hybrid ASR systems.
method Used conformer architecture, applied time downsampling, and transposed convolutions.
result Conformer-based hybrid model achieves competitive results and significantly outperforms BLSTM-based hybrid model.
ThriftyNet uses a single convolutional layer recursively to maximize parameter usage.
problem Maximizing the use of parameters in deep convolutional neural networks.
method A single convolutional layer is used recursively, with normalization, non-linearities, downsampling, and shortcuts to maintain model expressivity.
result ThriftyNet achieves competitive performance with significantly fewer parameters.
AutoEmbedder clusters unlabeled data using semi-supervised DNN embedding.
problem Clustering unlabeled data efficiently and effectively.
method Semi-supervised DNN embedding system using Siamese network architecture.
result AutoEmbedder outperforms existing DNN-based semi-supervised methods.
Paper improves Oja's algorithm for Markovian data streams.
problem Estimating the top eigenvector of a covariance matrix from Markovian data.
method Improves Oja's algorithm for streaming PCA with Markovian dependence.
result First sharp rate for Oja's algorithm on entire data stream, removing sample size dependence.
DNArch learns CNN architectures by backpropagation.
problem Discovering optimal CNN architectures.
method Differentiable Neural Architectures (DNArch) learns CNN architectures by backpropagation, controlling kernel sizes, channels, downsampling positions, and depth.
result DNArch finds performant CNN architectures across various tasks.
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions. Current Video Super-Resolution methods are not robust to mismatch between training and testing degradation models since they are trained against a single…
This paper simplifies diffusion models for high resolution images.
problem Applying diffusion models to high resolution images is challenging.
method Adjust noise schedule, scale specific parts, add dropout, and use downsampling.
result Achieved state-of-the-art image generation performance.
In this brief technical report we introduce the CINIC-10 dataset as a plug-in extended alternative for CIFAR-10. It was compiled by combining CIFAR-10 with images selected and downsampled from the ImageNet database. We present the approach to compiling the dataset, illustrate the example images for different classes, g…
Gaussian Process upsampling boosts OCR accuracy from low-res images.
problem Low-quality and downsampled image data hinders OCR accuracy.
method Gaussian Process upsampling model for improving OCR on low-resolution documents.
result Upsampling improves OCR accuracy on low-resolution images.
New method samples DPPs efficiently without downsampling or low-rank approximations.
problem Efficient sampling from DPPs for diverse selections.
method Directly approximates distribution function of linear statistics using Laplace inversion.
result Scalable sampling for general DPPs, beyond symmetric kernels.
A new training method speeds up ResNet training by 3x with minimal accuracy loss.
problem Training ResNets is slow due to dependencies between modules.
method Serial-parallel hybrid training strategy with data augmentation and downsampling.
result Significant speedup over traditional methods with comparable accuracy.
HKT improves sequence processing with multi-scale attention and kernel analysis.
problem Processing sequences at multiple scales with efficient attention mechanisms.
method Trainable causal downsampling and convex weights for level-specific score matrices.
result HKT achieves consistent gains over standard attention across various tasks.
In this work we apply variations of ResNet architecture to the task of atrial fibrillation classification. Variations differ in number of filter after first convolution, ResNet block layout, number of filters in block convolutions and number of ResNet blocks between downsampling operations. We have found a range of mod…
Efficient neural network for audio source separation.
problem End-to-end general purpose audio source separation.
method SuDoRMRF structure with simple one-dimensional convolutions for feature aggregation.
result SuDoRMRF achieves high quality audio source separation with minimal computational resources.
Data augmentation methods improve worst-case model performance.
problem Ensuring fair predictions across subpopulations in large models.
method Linear last layer retraining with data augmentation techniques.
result Optimal worst-group accuracy achieved for Gaussian latent representation distribution.
Stochastic optimization naturally arises in machine learning. Efficient algorithms with provable guarantees, however, are still largely missing, when the objective function is nonconvex and the data points are dependent. This paper studies this fundamental challenge through a streaming PCA problem for stationary time s…
Machine learning models classify celestial objects like pulsars and black holes.
problem Classifying high-energy celestial objects using photometric data.
method Applied tree-based models and RNN to classify pulsars and black holes.
result RNN showed potential for real-time object discrimination and classification.
PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at https://github.com/openai/pixel-cnn. Our implementation contains a number of modifications to the original model that both simplify its structure an…
This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-ima…
Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domainscs.LG
RAD improves robustness to domain annotation noise without explicit domain annotations.
problem Robustness to domain annotation noise in training data.
method Regularized Annotation of Domains (RAD) for last layer retraining.
result RAD outperforms state-of-the-art methods even with 5% noise in training data.
Image compression is an essential approach for decreasing the size in bytes of the image without deteriorating the quality of it. Typically, classic algorithms are used but recently deep-learning has been successfully applied. In this work, is presented a deep super-resolution work-flow for image compression that maps …
A new tensor-based layer reduces neural network dimensions without losing important features.
problem Reducing dimensionality in tensor-structured feature data for deep neural networks.
method TensorProjection layer that projects input tensors into output tensors with reduced dimensions through mode-wise projections.
result The TensorProjection layer outperforms traditional downsampling methods in tasks like medical image classification and segmentation.
The paper verifies deep neural networks' ability to approximate functions on spheres.
problem Theoretical verification of deep neural networks' performance on spherical functions.
method Spherical analysis using reproducing kernels and convolutional factorizations.
result Rates of uniform approximation for functions in Sobolev spaces and additive ridge forms.
Electronic records contain sequences of events, some of which take place all at once in a single visit, and others that are dispersed over multiple visits, each with a different timestamp. We postulate that fine temporal detail, e.g., whether a series of blood tests are completed at once or in rapid succession should n…
Convolution and pooling improve kernel methods in image classification.
problem Understanding the interplay between approximation and generalization in convolutional architectures.
method Characterized RKHS of kernels with convolution, pooling, and downsampling, computed generalization error.
result Convolution and pooling operations trade off approximation with generalization power.
This paper develops efficient bounds on the Wasserstein metric for discrete measures.
problem Computing the exact Wasserstein metric is computationally expensive.
method Formulates and solves a Kantorovich problem on a coarse grid using quantized measures and cost matrices, followed by upscaling and correction.
result Achieves a 10x-100x speedup while maintaining low approximation error.
NDMs enable non-linear transformations in diffusion models for better generative tasks.
problem Limited to linear transformations, diffusion models struggle with generative tasks.
method Presented NDMs that allow time-dependent non-linear transformations.
result NDMs outperform conventional diffusion models in likelihood and sample quality.
Paper addresses CPA line forecasting in online advertising mid-flight.
problem Forecasting ad campaign performance mid-flight considering bidding mechanisms.
method Generates relationships between metrics and optimization signals, estimates sensitivity, and characterizes advertiser spends vs. eCPA.
result Demonstrates promising accuracy in forecasting against actual deliveries.
Modern audio source separation techniques rely on optimizing sequence model architectures such as, 1D-CNNs, on mixture recordings to generalize well to unseen mixtures. Specifically, recent focus is on time-domain based architectures such as Wave-U-Net which exploit temporal context by extracting multi-scale features. …
The paper tackles skeptical binary inferences in multi-label problems with sets of probabilities.
problem Making distributionally robust, skeptical inferences for multi-label problems.
method Study of distributionally robust, skeptical inferences for multi-label problems using Hamming loss.
result Skeptical inferences provide partial predictions for a sufficiently big set of probability distributions.
The paper tackles imbalance in production data by proposing sampling methods to improve model performance on underrepresented observations.
problem Imbalance in production data negatively impacts model predictive performance on underrepresented observations.
method Three sampling approaches are investigated to adjust for imbalance in training data and improve model performance.
result Fitting a model using sampled data yields a small reduction in overall predictive performance but a better performance on underrepresented observations.