Proposes SPE for robust speaker verification.
problem Improving text-independent speaker verification accuracy.
method Spatial pyramid encoding and deep length normalization.
result Proposed system outperforms i-vector and d-vector baselines.
We define invariants for colored oriented spatial graphs by generalizing CM invariants, which were defined via non-integral highest weight representations of Uq(sl2). We apply the same method to define Yokota's invariants, and we call these invariants Yokota type invariants. Then we propose a volume conjecture of t…
SPF uses a hierarchical approach to efficiently emulate climate changes.
problem Slow and unstable climate emulation for long horizons.
method Spatiotemporal Pyramid Flows (SPF) model data hierarchically across spatial and temporal scales.
result SPF outperforms flow matching baselines and pre-trained models on ClimateBench.
Improved TSC with BOSS and SP techniques.
problem Comparing BOP and BOSS for time series classification.
method Deconstructed and measured components of BOP and BOSS, adapted CV techniques.
result SP with BOSS significantly more accurate than benchmarks.
GSANet improves semantic segmentation accuracy with selective and global attention.
problem Semantic segmentation accuracy improvement.
method Global and selective attention mechanism with ASPP and sparsemax.
result GSANet achieves state-of-the-art accuracy on ADE20k and Cityscapes datasets.
Generates realistic pedestrian data for training detectors.
problem Lack of labeled pedestrian data for training detectors.
method Pedestrian-Synthesis-GAN using GAN with multiple discriminators and SPP layer.
result Synthetic pedestrians improve detector performance.
The paper analyzes Laplacian pyramids for extending and denoising discrete functions.
problem Analyzing conditions for convergence and stability of Laplacian pyramids.
method Investigates Laplacian pyramids for extension and denoising, providing convergence conditions and stability bounds.
result Mild conditions are provided under which the Laplacian pyramids algorithm converges and stability bounds are proven.
Improves instance segmentation accuracy by integrating low-level features.
problem Low-level features enhance instance localization accuracy.
method Integrates low-level features into all layers of a feature pyramid network.
result Consistent improvement in precision on COCO Dataset.
Paper tackles fixed-size representation learning for variable-sized signatures.
problem Learning feature representations for signatures of varying sizes.
method Modified Spatial Pyramid Pooling to learn fixed-sized representations from variable-sized signatures.
result Comparable performance to state-of-the-art on GPDS dataset, removing size constraint.
A novel approach for augmenting histopathological images by blending Gaussian-Laplacian pyramids.
problem Data imbalance and inter-patient variability in histopathological images.
method Image blending using Gaussian-Laplacian pyramids to distribute inter-patient variability.
result Promising gains in performance compared to existing data augmentation techniques.
Pyramid Attention Networks improve image restoration by leveraging self-similarities across scales.
problem Lack of full exploitation of self-similarities in image restoration by recent deep learning methods.
method Introduces a Pyramid Attention module that processes multi-scale feature correspondences to borrow clean signals from coarser levels.
result Pyramid Attention module achieves state-of-the-art results in various image restoration tasks.
Simulation reveals relationships in stock market pyramid schemes.
problem Understanding pyramid scheme behavior in stock markets.
method Agent-based simulation with four investor types and parameters.
result Relationships between main fund's rate of return and trend investors' proportion.
A new pyramidal diffusion model speeds up image generation.
problem Training and evaluating diffusion models are time-consuming.
method Introduces a pyramidal diffusion model with a single score function.
result Generates high-resolution images from low-resolution starting points efficiently.
Single wide layer followed by a pyramidal structure ensures global convergence in deep networks.
problem Ensuring global convergence in deep neural networks with limited width constraints.
method Proves that a single wide layer followed by a pyramidal structure guarantees global convergence for over-parameterized networks.
result Single wide layer of width N suffices for global convergence in deep networks with constant-width remaining layers. We generalize the observable diameter and the separation distance for metric measure spaces to those for pyramids, and prove some limit formulas for these invariants for a convergent sequence of pyramids. We obtain various applications of our limit formulas as follows. We have a criterion of the phase transition proper…
By formulating N = 1, 2, 4, 8, D = 3, Yang-Mills with a single Lagrangian and single set of transformation rules, but with fields valued respectively in R,C,H,O, it was recently shown that tensoring left and right multiplets yields a Freudenthal-Rosenfeld-Tits magic square of D = 3 supergravities. This was subsequently…
TP-AIS improves sampling efficiency over existing methods.
problem Efficient sampling from complex probability distributions.
method Iterative sampling using a tree pyramid structure.
result TP-AIS outperforms DM-PMC, M-PMC, and LAIS.
Study smoothings of ellipsoid intersections with singularities.
problem Smooth singular intersections of ellipsoids.
method Study 3D manifolds with singularities as small covers of Coxeter polyhedral orbifolds.
result Introduce n-pyramitoid to generalize n-pyramids. Improves speaker verification for variable-duration utterances using a feature pyramid module.
problem Improving robustness for variable-duration utterances in speaker verification.
method Integrates a feature pyramid module into multi-scale aggregation to enhance speaker-discriminative information from multiple layers.
result Improves performance for both short and long utterances compared to state-of-the-art approaches.
The paper develops a new Ricci flow method in higher dimensions.
problem Constructing a Ricci flow on non-collapsed IC1-limit spaces. method Constructing a pyramid Ricci flow on a subset of space-time.
result Non-collapsed IC1-limit spaces are globally homeomorphic to smooth manifolds. Global homeomorphism constructed from 3D Ricci limit spaces.
problem Global regularity of 3D Ricci limit spaces.
method Local pyramid Ricci flows and partial Ricci flows.
result Global homeomorphism to a smooth manifold.
PyFi uses adversarial agents to train VLMs on financial image understanding.
problem Training VLMs to understand complex financial questions.
method PyFi-600K dataset and adversarial MCTS mechanism.
result Fine-tuned VLMs improve by 19.52% and 8.06% on financial question accuracy.
Enhances neural networks' robustness against adversarial samples without sacrificing clean sample generalization.
problem Limited generalization and time complexity of adversarial training.
method Feature Pyramid Decoder (FPD) framework that integrates denoising and image restoration modules into CNNs and constrains the Lipschitz constant.
result FPD-enhanced CNNs achieve sufficient robustness against general adversarial samples on various datasets.
Pyramidal GNN combines RC and pooling for efficient graph embeddings.
problem Efficiently embedding graphs while maintaining accuracy.
method Alternates RC layers with pooling to reduce complexity.
result Formally shows how pooling reduces complexity and speeds convergence.
Deep neural network detects changepoints at multiple scales in multivariate time series.
problem Detecting gradual and abrupt changes in multivariate time series data.
method Proposed a pyramid recurrent neural network (PRN) for multi-scale changepoint detection.
result PRN outperforms state-of-the-art methods in detecting changepoints at multiple scales.
Detects parking spaces in parcels using satellite images.
problem Locating parking spaces in urban areas using satellite imagery.
method Used Feature Pyramid based Mask RCNN for localizing parking spaces and vehicles in parking lots.
result Average class accuracy of 97.56% for parking spaces and vehicles.
We introduce a wavelet-domain functional analysis of variance (fANOVA) method based on a Bayesian hierarchical model. The factor effects are modeled through a spike-and-slab mixture at each location-scale combination along with a normal-inverse-Gamma (NIG) conjugate setup for the coefficients and errors. A graphical mo…
This paper focuses on a class of linear Hawkes processes with general immigrants. These are counting processes with shot noise intensity, including self-excited and externally excited patterns. For such processes, we introduce the concept of age pyramid which evolves according to immigration and births. The virtue if t…
Biological neural network mimics CCA for multi-channel data.
problem Implementing CCA in a biologically plausible neural network.
method Derive an online CCA algorithm with local synaptic updates for multi-compartmental neurons.
result The derived neural network architecture and synaptic updates resemble cortical pyramidal neuron behavior.
Deep learning improves automatic image segmentation.
problem Automatic object localization and boundary delineation in images and medical scans.
method Proposed and evaluated novel dilated dense encoder-decoder architectures for salient object segmentation and lesion localization in medical images.
result Proposed architectures outperform state-of-the-art models in accuracy and efficiency.
PC-RNN reconstructs MRI images from undersampled data with more details.
problem Recovering fine details from undersampled MRI data.
method Pyramid Convolutional RNN (PC-RNN) with three ConvRNN modules for multi-scale reconstruction.
result PC-RNN outperforms other methods in recovering more details from MRI images.
The challenge of object categorization in images is largely due to arbitrary translations and scales of the foreground objects. To attack this difficulty, we propose a new approach called collaborative receptive field learning to extract specific receptive fields (RF's) or regions from multiple images, and the selected…
Advances conditions for neural networks to learn connected decision regions.
problem Conditions for neural networks to learn connected decision regions.
method Developed sufficient and necessary conditions for connected decision regions using a wider class of activation functions.
result Proven neural networks with pyramidal structure and certain activation functions produce connected decision regions.
Non-linear dimensionality reduction techniques such as manifold learning algorithms have become a common way for processing and analyzing high-dimensional patterns that often have attached a target that corresponds to the value of an unknown function. Their application to new points consists in two steps: first, embedd…
A deep learning framework assesses physical rehabilitation exercises.
problem Lack of versatile, robust, and practical assessment methods for rehabilitation exercises.
method Deep learning framework with metrics, scoring functions, and neural networks.
result First implementation of deep neural networks for rehabilitation performance assessment.
Paper proposes using NLPD for cGANs to improve image realism and segmentation.
problem Lack of perceptual evaluation in cGANs.
method Used NLPD to measure perceptual similarity, comparing with L1 distance.
result NLPD improves realism and segmentation accuracy in generated images.
Reduced CNN complexity for exoplanet detection without significant loss in accuracy.
problem Reducing the number of parameters in CNNs for time-series data.
method Two methods for complexity reduction in AstroNet: tactical reduction of layers and modification of input data.
result Non-substantial loss in accuracy with up to 85% reduction in training time.
Hyperbolic links in thickened torus decompose into angled tetrahedra.
problem Hyperbolicity of links in thickened torus.
method Decomposition into torihedra, angled pyramids, and angled tetrahedra.
result Augmented links in thickened torus are hyperbolic.
The predictions of the S&P 500 returns made in 2007 have been tested and the underlying models amended. The period between 2003 and 2008 should be described by the dependence of the S&P 500 stock market index on real GDP because the population pyramid was highly inaccurate. The 2008 trough and 2009 rally are well predi…
Develops LSTM for predicting neuronal dynamics over long time-horizons.
problem Understanding and controlling complex brain behaviors.
method Long Short-Term Memory (LSTM) neural network architecture for multi-time step predictions.
result LSTM improves short time-horizon prediction accuracy and multi-time step predictions of neuronal dynamics.
ADAVI tackles variational inference for large HBM models in neuroimaging.
problem Large, pyramidally-organized HBM models in neuroimaging studies.
method Automatic dual amortized variational inference using neural networks and attention-based hierarchical encoding.
result Significantly reduced parameterization of the variational family, maintaining expressivity.
Study on convergence of transformed metric spaces as dimensions grow.
problem Conditions for convergence of transformed metric spaces.
method Clarifying conditions for convergence of transformed spaces from original sequence and vice versa.
result Spheres and projective spaces converge to Gaussian space and its quotient as dimensions increase.
While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that thi…
MRCNet tackles crowd counting and density mapping in aerial imagery.
problem Accurate crowd counting and density estimation in aerial imagery.
method MRCNet is a novel encoder-decoder CNN that combines VGG-16 with FPN-inspired lateral connections.
result MRCNet outperforms state-of-the-art methods in aerial and CCTV-based crowd counting.
A novel approach predicts long-term stock price trends using 2D-convolutional encoders and semantic segmentation.
problem Predicting long-term daily stock price changes with deep learning models.
method Proposes a hierarchical CNN structure with Atrous Spatial Pyramid Pooling blocks to capture both long and short-term temporal relationships.
result Achieved overall accuracy and AUC of 78.18% and 0.88 for predicting trends over the next 20 days.
Spatial variable selection is crucial for reliable spatial predictions in machine learning.
problem Spatial autocorrelation leads to overfitting and poor spatial predictions.
method Used Random Forests with non-spatial and spatial cross-validation strategies.
result Spatial variable selection is essential for reliable spatial predictions.
Spatial blind source separation simplifies multivariate spatial prediction.
problem Predicting multivariate measurements at unobserved locations with spatial dependencies.
method Spatial blind source separation as a pre-processing tool compared to Cokriging and neural networks.
result Spatial blind source separation simplifies spatial prediction by avoiding cross-dependencies.
The paper introduces groupoid racks for spatial surfaces.
problem Coloring diagrams of spatial surfaces for invariant calculation.
method Introduces groupoid racks with universal properties.
result Groupoid racks provide an invariant for spatial surfaces.