cGAP visualizes high-dimensional categorical data with interpretable geometric structure.
problem Lack of visualization tools for high-dimensional categorical data.
method cGAP uses Homogeneity Analysis (HOMALS) to embed data in a 3D space and maps it to colors.
result cGAP reveals coherent clusters, outliers, and local-to-global structure in categorical data.
cGAP visualizes high-dimensional categorical data with interpretable geometric structure.
problem Lack of visualization tools for high-dimensional categorical data.
method cGAP uses Homogeneity Analysis (HOMALS) to embed data in a 3D space and maps it to colors for visualization.
result cGAP reveals coherent clusters, outliers, and local-to-global structure in categorical data.
Automated method selects eye tracking variables for categorization tasks.
problem Limited duration of infant cooperation and biases in handpicked eye tracking variables.
method Automated selection of eye tracking variables using statistical techniques.
result Same eye tracking variables classify category learners from non-learners in adults and infants with high accuracy.
New report on machine learning visualization techniques and trends.
problem Improving trust in machine learning models through visualization.
method Analysis of peer-reviewed articles on machine learning visualization techniques.
result Rapid growth in machine learning visualization techniques over the past three years.
VCML learns concepts and metaconcepts from images and questions.
problem Learning concepts and metaconcepts from visual data.
method Bidirectional connection between visual concepts and metaconcepts.
result VCML can generalize from limited data and noisy inputs.
This paper proposes a method to reduce complexity in GLMs with categorical predictors.
problem Wasteful, hard-to-interpret, and prone to overfitting of traditional one-hot encoding for high-cardinality categorical predictors.
method Clustering categories of categorical predictors through a numerical method that preserves or improves accuracy while reducing the number of coefficients.
result Clustering categories of categorical predictors reduces complexity substantially without harming accuracy.
L*ReLU improves deep learning for fine-grained image classification.
problem Fine-grained image classification requires specific AFs.
method Proposes L*ReLU, piece-wise linear AFs for deep learning.
result L*ReLU achieves superior results on FGVC datasets.
People can learn complex visual concepts from just a few examples.
problem Understanding how people learn and categorize visual concepts from limited data.
method Bayesian program learning model that searches for the best explanation of observations.
result People's judgments are broadly consistent with a Bayesian program learning model, indicating they can learn rich algorithmic abstractions from sparse input data.
Report on enhancing trust in ML models with visualizations.
problem Understanding and trusting the results of black box ML models.
method Categorization of visualization techniques, statistical overview, topic analyses, and interactive web-based survey.
result Expanded categorization of trust against different facets of interactive ML.
Detects anomalies in autonomous mobile robots using vision.
problem Anomaly detection for autonomous mobile robots.
method Unsupervised deep learning methods and a novel dataset.
result State-of-the-art approach tested on a new dataset.
Deep neural network features model human image categorization.
problem Modeling human categorization using natural images.
method Used convolutional neural network features to model human behavior.
result Representations from deep neural networks can model human natural image classifications.
DPERC efficiently estimates covariance matrices for mixed data with missing values.
problem Estimating covariance matrices for datasets with missing values and mixed features.
method Direct Parameter Estimation for Randomly Missing Data with Categorical Features (DPERC).
result DPERC outperforms other methods in estimating covariance matrices for mixed data with missing values.
Teaches categories with visual explanations to improve learning.
problem Challenges of traditional machine teaching methods in providing clear explanations.
method Proposes a teaching framework that provides interpretable explanations as feedback.
result Participants achieve better test set performance with interpretable explanations.
Models use embeddings and attention for better claim severity prediction.
problem Improving predictive modeling of claim severity with categorical data.
method Developed neural networks and attention-based architectures with embeddings.
result Attention-based models enhance predictive performance with contextual augmentation.
This paper extends rack and quandle covering theory using higher categorical Galois theory.
problem Developing a higher covering theory of racks and quandles.
method Applying techniques from higher categorical Galois theory to extend and clarify the foundations of rack and quandle coverings.
result Identification of meaningful higher-dimensional centrality conditions defining higher coverings of racks and quandles.
Fine-grained visual categorization (FGVC) is to categorize objects into subordinate classes instead of basic classes. One major challenge in FGVC is the co-occurrence of two issues: 1) many subordinate classes are highly correlated and are difficult to distinguish, and 2) there exists the large intra-class variation (e…
New method visualizes brain activity changes over time.
problem Understanding representational dynamics in neural responses.
method Procrustes-aligned Multidimensional Scaling (pMDS) on RDM movies.
result Multidimensional scaling alignment captures representational dynamics.
Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e.…
A framework for measuring differences in categorical data.
problem Measuring differences between categorical variables.
method Introduces a general framework for efficient and flexible implementation of distances for categorical variables.
result Framework allows for incorporation of existing distances and introduces new formulations.
Develops an efficient method for real-time data analysis and visualization.
problem Challenges of analyzing high-dimensional data.
method Incremental non-linear manifold approximation using GMRA framework.
result Accurately represents non-linear manifolds with small initial samples.
New visual quality index for fuzzy clustering.
problem No accurate quality index for fuzzy clustering across datasets.
method Proposes a new visual quality index and graph-based solution.
result Validated through extensive experiments on various datasets.
Art historians and archaeologists have long grappled with the regional classification of ancient Near Eastern ivory carvings. Based on the visual similarity of sculptures, individuals within these fields have proposed object assemblages linked to hypothesized regional production centers. Using quantitative rather than …
Bayesian approach updates pretrained convnet for new image categories.
problem Learning new categories with limited data.
method Bayesian procedure using pretrained convnet weights as prior.
result Competitive performance with state-of-the-art methods.
Prototype-based memory network learns visual categories from unlabeled data.
problem Learning from nonstationary, unlabeled data with sequential dependencies.
method Online prototype-based memory network with contrastive loss.
result Significantly better category recognition compared to state-of-the-art methods.
The output scores of a neural network classifier are converted to probabilities via normalizing over the scores of all competing categories. Computing this partition function, Z, is then linear in the number of categories, which is problematic as real-world problem sets continue to grow in categorical types, such as …
Method trains multi-modal policy from unlabeled mixed demonstrations.
problem Training policies from unlabeled mixed demonstrations.
method Variational autoencoder with categorical latent variable to discover latent factors of variation.
result Policy can reproduce specific behaviors by conditioning on categorical vectors.
Visualizes information flow in ML systems for better understanding and analysis.
problem Understanding the flow of information in complex ML systems.
method Proposes a visual approach using Sankey Diagrams to analyze flow of information.
result Demonstrates the effectiveness of the proposed technique in diagnosing model performance.
This paper generalizes graph representation for diverse data types.
problem Representing and querying hybrid data types in a unified way.
method Introducing a directed Tensor-Typed Multi-Graph with embeddings.
result Unified representation for visual, linguistic, and auditory data.
Rotation-equivariant CNN reveals common features in V1 neurons.
problem V1 models fail to predict natural stimuli responses accurately.
method Rotation-equivariant convolutional neural network model.
result Rotation-equivariant network outperforms regular CNN and reveals common features.
Improves neural network mapping functionality using latent feature generation.
problem Improving neural network performance in visual recognition tasks.
method Reversible learning for generating and learning latent features.
result The proposed method outperforms existing state-of-the-art methods in visual recognition.
We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid models. A data set of temporal event sequences can be represented as a data set of three-dimensional points, each point is defined by three varia…
Survey on embedding techniques in source code.
problem Applying word embedding techniques to source code.
method Collection and categorization of articles from related work and scholarly searches.
result Word embedding has been successfully applied to various granularities of source code.
This paper categorizes GANs' theoretical contributions and solutions.
problem GANs' theoretical limitations and training issues.
method Categorization and comparison of GAN-related research papers.
result Systematic understanding of GANs' theoretical progress.
A new method for domain generalization using source-specific classifiers.
problem Generalizing across multiple sources for any target domain.
method Multiple domain-specific classifiers and a domain agnostic component.
result Improved performance on public benchmarks.
This study addresses the issue of predicting the glaucomatous visual field loss from patient disease datasets. Our goal is to accurately predict the progress of the disease in individual patients. As very few measurements are available for each patient, it is difficult to produce good predictors for individuals. A rece…
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
Layer-wise relevance propagation (LRP) is a recently proposed technique for explaining predictions of complex non-linear classifiers in terms of input variables. In this paper, we apply LRP for the first time to natural language processing (NLP). More precisely, we use it to explain the predictions of a convolutional n…
StructureBoost improves gradient boosting for complex categorical variables efficiently.
problem Efficiently handling complex categorical variables with known structure.
method Two methods to overcome computational obstacles in SCDT enumeration for structured categorical variables.
result StructureBoost outperforms existing packages on complex categorical problems.
Categorical bundles provide a natural framework for gauge theories involving multiple gauge groups. Unlike the case of traditional bundles there are distinct notions of triviality, and hence also of local triviality, for categorical bundles. We study categorical principal bundles that are product bundles in the categor…
Bayesian model improves categorization of explosions from sparse data.
problem Challenges in categorizing explosions from limited data.
method Bayesian update to Event Categorization Matrix model with Bayesian Decision Theory.
result Consistent gains in overall accuracy and lower false negative rates.
New method embeds RNN Seq2Seq models to visualize spatiotemporal data.
problem Visualizing and interpreting spatiotemporal data in sequence prediction tasks.
method Embedding approach to visualize and interpret RNN Seq2Seq model representations.
result Embedding space projections of RNN Seq2Seq models capture spatiotemporal dynamics.
UNTIE learns representations of coupled categorical data.
problem Challenges in learning from unlabeled categorical data with complex couplings.
method UNTIE approach for unsupervised representation learning of heterogeneous couplings.
result UNTIE significantly improves categorical data representations on 25 diverse datasets.
Paper introduces Categorical Normalizing Flows for better handling of categorical data.
problem Limited application of normalizing flows on categorical data due to lack of intrinsic order.
method Categorical Normalizing Flows use continuous transformations to model latent relations in categorical data, optimizing both continuous representation and model likelihood.
result GraphCNF, a permutation-invariant generative model, outperforms state-of-the-art on molecule generation.
Authors conjecture categorically diagonalizable complex for full twists.
problem Categorically diagonalizing the complex of Soergel bimodules for full twists.
method Utilizes categorical diagonalization theory.
result Proves conjecture in type A, categorifies Young idempotents.
A new method optimises problems with both continuous and categorical inputs.
problem Optimising black-box problems with mixed continuous and categorical inputs.
method Continuous and Categorical Bayesian Optimisation (CoCaBO) combining multi-armed bandits and Bayesian optimisation.
result CoCaBO outperforms existing methods on synthetic and real-world tasks.
The paper shows how integrating categorical semantics can enhance unsupervised domain translation.
problem Improving unsupervised domain translation between perceptually different domains.
method Learning invariant categorical semantic features in an unsupervised manner and conditioning them on the style encoder.
result Conditioning the style encoder on learned categorical semantics improves translation and stylization.
New model predicts persistent weather patterns better than persistence model.
problem Improving weather forecasting accuracy for persistent patterns.
method Mixture of experts model using ordinal neural network.
result The model outperforms persistence and other models for longer time horizon predictions.
Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sa…