New graphs found in CAT(0) group boundaries.
problem Visual boundaries of CAT(0) complexes not homeomorphic.
method Constructed two homeomorphic locally CAT(0) complexes with distinct visual boundaries.
result Visual boundary contains nonplanar graph in one case, not in the other.
A method to visualize multidimensional local subspaces using implicit differentiation.
problem Understanding the effect of multidimensional projection on local subspaces.
method Implicit function differentiation to analyze local subspaces shaped by multidimensional ellipses.
result Visualization of local subspaces provides insights into the global structure of data.
Associating image regions with text queries has been recently explored as a new way to bridge visual and linguistic representations. A few pioneering approaches have been proposed based on recurrent neural language models trained generatively (e.g., generating captions), but achieving somewhat limited localization accu…
Efficiently visualizes uncertainty in local divergence of 2D vector fields.
problem Uncertainty in vector field data leads to inaccurate divergence computations.
method Closed-form approach for highly efficient and accurate uncertainty visualization of local divergence, assuming independently Gaussian-distributed vector uncertainties.
result Significantly enhanced efficiency and accuracy of our algorithms over classical MC approach.
We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing input regions that are 'important' for predictions -- or visual explanations. Our approach, called Gradient-weighted Class Activation Mapping (Grad-CAM), uses class-specific gradient information to localize…
IsUMap improves data visualization of complex geometries.
problem Accurately representing complex, locally distorted metric spaces.
method Integrates UMAP and Isomap with Vietoris-Rips filtrations.
result Significant improvements in data representation quality.
MIK improves t-SNE's local structure preservation in biological sequence data.
problem Efficiently preserving local structure in high-dimensional biological sequence data.
method Modified Isolation Kernel (MIK) using adaptive density estimation.
result MIK preserves local and global structure better than Gaussian and isolation kernels.
VINE visualizes statistical interactions in complex models.
problem Lack of utilities for regional explanations in black box models.
method VINE algorithm to extract and visualize statistical interaction effects.
result VINE provides a novel evaluation metric for visualizations.
Generative models learn 3D scenes without explicit maps.
problem Learning models for visual 3D localization without explicit maps.
method Generative Query Networks (GQNs) with attention mechanisms.
result GQNs can capture complex 3D scenes and perform localization.
DFF detects similar concepts in images, visualized as heat maps.
problem Localizing similar semantic concepts within images.
method Deep Feature Factorization (DFF) to detect hierarchical cluster structures in feature space.
result Visualizes semantically matching regions across images, revealing network perception.
In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models have been developed. We introduce local feature importance as a local version of a recent model-agnostic global feature importance method. Based on local feature importance…
Unified method visualizes curvature on curves and surfaces.
problem Visualizing curvature on planar curves and surfaces of revolution.
method Tangential angle parameterization.
result Clear, consistent visualizations without arbitrary parameter tuning.
GTSNE improves data visualization for high-dimensional data.
problem Visualizing high-dimensional data points in a 2D map.
method GTSNE is a variation of t-SNE that captures both local and macro structures.
result GTSNE produces better visualizations of high-dimensional data compared to other methods.
New dataset and analysis improve evaluation of visual representation models.
problem Insufficient evaluation methods for visual representation models.
method Analyzed five representations and developed a new dataset.
result Models with poor linear classification performance can still perform well on complex tasks.
A novel supervised visualization technique for data exploration.
problem Lack of supervised dimensionality reduction methods considering class labels.
method Random forest proximities and diffusion-based dimensionality reduction.
result Retains local and global structures in data, emphasizing important variables.
The paper develops a neural network to predict wind speed from visual observations.
problem Accurate wind speed prediction for renewable energy and weather forecasting.
method Coupled Convolutional and Recurrent Neural Network architecture trained on visual flow-structure interactions.
result The model accurately predicts wind speeds with low error compared to cup anemometer measurements.
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual chec…
A framework compares image representations based on local geometry.
problem Comparing image representations based on global structure overlooks local differences.
method Quantify local geometry using Fisher information matrix and optimize differentiation with principal distortions.
result Identifies differences in local sensitivities between models.
DIG visualizes complex time series data.
problem Insufficient visualization of high-dimensional dynamical processes.
method DIG (Dynamical Information Geometry) using diffusion framework.
result Reveals structure in multivariate time series data.
Generative Map learns interpretable neural network maps for camera localization.
problem Creating interpretable maps for neural network-based camera localization.
method Combining generative models with Kalman filters and incorporating additional sensor information.
result Generative Map predicts images closely resembling the true scene and achieves comparable localization performance.
LASE improves local network structure visualization by targeting locally low-dimensional regions.
problem Global spectral embedding fails to capture local geometric features in sparse, transitive networks.
method Local Adjacency Spectral Embedding (LASE) using weighted spectral decomposition.
result LASE reveals locally low-dimensional structure, improving local reconstruction and visualization.
Path connectedness of boundaries for certain CAT(0) groups with isolated flats.
problem Conditions for the path connectedness of boundaries of CAT(0) groups.
method Study of CAT(0) groups with isolated flats acting on CAT(0) spaces.
result Visual boundaries of CAT(0) groups with isolated flats are path connected.
This paper develops a cohomological hierarchy for bistable visual paradoxes.
problem Understanding the hierarchy of visual paradoxes built from bistable elements.
method Develops a cohomological hierarchy using Z2 coefficients and a discrete Stokes theorem. result Reveals a hierarchy of paradox classes from H0 through H2, refined at each degree by the relative/absolute distinction. Enhances visual localization using graph smoothing.
problem Inferring camera pose from a single image.
method Constructs a graph based on GPS coordinates and temporal information, then smooths feature representations.
result Significantly improves localization accuracy on large datasets.
Improved GAN performance with a novel local attention mechanism.
problem Enhancing the performance of Generative Adversarial Networks (GANs).
method Introducing a two-dimensional local attention mechanism that preserves geometry and locality, and using information flow graphs for design.
result Significant improvements in FID and Inception scores, from 18.65 to 15.94 on ImageNet.
New model explains visual illusions using sub-Riemannian geometry.
problem Mathematical modeling of Poggendorff-type visual illusions.
method Cortical-inspired sub-Riemannian model with sub-Laplacian evolution.
result Sub-Riemannian kernel enhances visual misperceptions and biases.
ExMatrix visualizes RF models, aiding in decision understanding.
problem Lack of interpretability in complex models like RF.
method ExMatrix, a novel visualization method for RF interpretability.
result ExMatrix handles large, complex RF models and promotes interpretability.
Proposes a deep Auto-Encoder-like framework for visual-tactile fusion object clustering.
problem Combining visual and tactile information for better object clustering.
method Deep Auto-Encoder-like Non-negative Matrix Factorization framework, graph regularizer, modality-level consensus regularizer, alternating minimization strategy.
result Improves object clustering performance by leveraging both visual and tactile modalities.
A new approach clusters data first, then embeds each cluster, improving transparency.
problem Visualizing data with latent clusters while preserving global geometry.
method First cluster, then embed each cluster, aligning the clusters.
result The approach is competitive with existing methods and more transparent.
The paper proposes a model to learn motion perception in V1 using vector and matrix representations.
problem Motion perception in primary visual cortex (V1).
method Coupling vector representations of local contents and matrix representations of local pixel displacements.
result The model can learn Gabor-like filter pairs and infer local motions.
New method visualizes tabular feature semantics for better model understanding.
problem Lack of feature interaction interpretation in tabular ML models.
method Feature Vectors method for global tabular dataset interpretability.
result Visualizes semantic relationships among tabular features.
We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model…
We provide complete source code for a front-end GUI and its back-end counterpart for a stock market visualization tool. It is built based on the "functional visualization" concept we discuss, whereby functionality is not sacrificed for fancy graphics. The GUI, among other things, displays a color-coded signal (computed…
New visualization techniques reveal GAN optimization landscapes.
problem Challenges in training GANs compared to standard deep neural networks.
method Visualization techniques for optimization landscapes of GANs.
result GANs converge to saddle points, not minima, achieving excellent performance.
Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc. To apply particle filtering in practice, a critical challenge is to construct probabilistic system models, especially for systems with complex dynamics or r…
This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on mon…
Let X be a symmetric space of non-compact type or a locally finite, strongly transitive Euclidean building, and let B denote the geodesic boundary of X. We reduce the study of visual limits of maximal flats in X to the study of limits of apartments in the spherical building B: this defines a natural, geometric compacti…
PSEUDo learns patterns in multivariate time series with locality-sensitive hashing and relevance feedback.
problem Efficient pattern detection in large, multi-track sequential data with high variance and lack of ground truth.
method Query-aware locality-sensitive hashing for feature learning, sub-linear training and inference time.
result PSEUDo achieves sub-linear time efficiency for pattern modeling and comparison of 10,000 multivariate time series.
Reviews recent findings on neural network landscapes.
problem Non-convexity of loss functions causing bad landscapes.
method Rigorous geometric analysis and empirical exploration.
result Wide neural nets may have sub-optimal local minima.
Visual rendering of graphs is a key task in the mapping of complex network data. Although most graph drawing algorithms emphasize aesthetic appeal, certain applications such as travel-time maps place more importance on visualization of structural network properties. The present paper advocates two graph embedding appro…
We introduce and study graphic lambda calculus, a visual language which can be used for representing untyped lambda calculus, but it can also be used for computations in emergent algebras or for representing Reidemeister moves of locally planar tangle diagrams.
3D filament plots visualize curves in datasets, avoiding visual clutter.
problem Visualizing high-dimensional data relationships in scatterplots of points.
method Construct 3D filament plots using linear isometries and Frenet-Serret systems.
result 3D filament plots preserve Euclidean distances and avoid visual clutter.
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. However, interpreting the decision making process of these 3D-CNNs is still an infeasible task. In this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation Mapping method (3D…
Proposes a neural network method to improve consistencies in high dimensional data analysis.
problem Inconsistencies among dimensionality reduction, clustering, and visualization tasks in high dimensional data analysis.
method Consistent Representation Learning (CRL) neural network that performs NLDR transformations to satisfy LGP constraints.
result Improves consistencies in data interpretation through end-to-end task execution.
Surrogate-based analysis of interactions via local effect smooths
problem Detecting and characterizing feature interactions in machine learning models
method Surrogate-based analysis using generalized additive models
result Empirical validation of effectiveness for pairwise interactions
Local explanation methods, also known as attribution methods, attribute a deep network's prediction to its input (cf. Baehrens et al. (2010)). We respond to the claim from Adebayo et al. (2018) that local explanation methods lack sensitivity, i.e., DNNs with randomly-initialized weights produce explanations that are bo…
Improves visualization of high-dimensional data by correcting misleading artifacts in neighbor embedding methods.
problem Misleading visual artifacts in t-SNE and UMAP due to lack of data-independent manifold learning interpretations.
method LOO-map framework that extends embedding maps to the entire input space, identifying and correcting map discontinuities.
result Developed point-wise diagnostic scores to detect unreliable embedding points and improve hyperparameter selection.
Proposes a method for weakly-supervised object localization to improve few-shot learning.
problem Challenges of few-shot learning, especially with fine-grained categories.
method Introduces a Self-Attention Based Complementary Module (SAC Module) for weakly-supervised object localization.
result Significantly outperforms state-of-the-art methods on benchmark datasets, especially for fine-grained few-shot tasks.