IANN visualizes all input variables effects simultaneously.
problem Inability to visualize all input variables effects simultaneously in black-box functions.
method Interpretable Architecture Neural Network (IANN) approach.
result Visualization of all input variables effects directly and simultaneously.
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.
Visual design improves financial data classification accuracy.
problem Improving financial decision-making through better data representation.
method Comparing numeric vs visual data representations in supervised classification.
result Visual transformation of numeric data leads to higher predictability.
A faster method for visualization recommendations on large datasets.
problem Infeasibility of state-of-the-art vis-rec models on large datasets due to high computational time.
method Reinforcement-learning (RL) framework that identifies optimal statistics within a time budget.
result Significantly reduces time-to-visualize with minimal error compared to baseline approaches.
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.
Visualizes ConvNets without confounding effects.
problem Misinterpretation of saliency maps due to confounding variables.
method Univariate statistical tests and partial back-propagation to remove confounding effects.
result Visualization of confounder-free saliency maps.
This paper explores neural network loss landscapes and their effects on generalization.
problem Understanding the structure of neural network loss functions and their impact on generalization.
method Simple filter normalization and various visualization methods to explore loss landscape structure and network architecture effects.
result Visualizations reveal how network architecture and training parameters affect loss landscape curvature and minimizers.
AVDCNN combines audio and visual data for better speech enhancement.
problem Improving speech quality by reducing noise in audio signals.
method Proposes an AVDCNN model that integrates audio and visual streams into a unified deep CNN network for end-to-end training.
result AVDCNN outperforms audio-only and conventional SE methods in enhancing speech quality.
AVDCNN combines audio and visual data for better speech enhancement.
problem Improving speech quality in noisy environments.
method Multimodal deep CNNs model that integrates audio and visual streams.
result AVDCNN outperforms audio-only and conventional SE methods.
CDDN tackles visual relationship detection with context-dependent diffusion networks.
problem Combustion of combinatorial explosion in relation triplets detection.
method CDDN framework using semantic and visual scene graphs for adaptive information aggregation.
result CDDN achieves state-of-the-art performance on visual relationship detection datasets.
Ray marching method visualizes flat surfaces efficiently.
problem Efficient visualization of flat surfaces.
method Ray marching approach for intuitive exploration.
result Effective visualization of translation surfaces and polyhedra.
Visualizes futures markets using particle physics tools.
problem Understanding high-velocity data in futures markets.
method Uses ROOT, an open-source data-analysis tool, to reconstruct and visualize message-based data.
result Allows stakeholders to gain a better understanding of markets and monitor effectively.
VTAB benchmarks diverse visual tasks to assess representation learning effectiveness.
problem Lack of a unified evaluation for general visual representations.
method Developed VTAB, a benchmark for diverse visual tasks, and evaluated many representation learning algorithms.
result VTAB revealed insights into the effectiveness of various representation learning methods.
Novel model for decoding visual stimuli in human brains.
problem Challenges in MVP techniques, including noise and sparsity, and the cost of brain studies.
method Automatic detection of active regions, new Gaussian smoothing method, combining fMRI data sets.
result Superior performance compared to state-of-the-art methods.
Consensus dimension reduction combines multiple visualizations to identify shared patterns.
problem Conflicting visualizations from different dimension reduction methods.
method Multi-view learning to identify stable patterns across multiple views.
result Consensus visualization effectively identifies shared low-dimensional data structure.
Proposes a spectral method to assess and combine multiple data visualizations.
problem Evaluating and combining the strengths of different data visualization algorithms.
method Spectral method for assessing and combining multiple visualizations.
result Proposes a visualization eigenscore to quantify relative performance and a consensus visualization.
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.
This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in data availability and precision, the transition from firm-centric to system-wide supervision imposes vast data needs. Moreover, except for internal communication as in any orga…
Self-supervised learning of visual semantics in image games.
problem Learning visual semantics in referential emergent language games.
method Investigating the impact of feature extractor weights and tasks on visual semantics, using various image augmentations and additional tasks.
result Communication systems can learn visual semantics in a self-supervised manner by playing the right types of games.
New method addresses crowding in high-dimensional data visualization.
problem Crowding issue in visualizing high-dimensional data.
method Adjusting capacity of high-dimensional balls and estimating correlation dimension.
result Mitigates crowding in various distance metrics.
Develops a two-stage approach for robust tensor completion of visual data.
problem Estimating missing values in high-order data with outliers.
method Coarse-to-fine framework and M-estimator-based robust tensor ring recovery.
result Superior performance compared to state-of-the-art robust algorithms.
DarkSight visualizes deep classifiers more effectively than t-SNE.
problem Interpreting black box classifiers like deep networks.
method DarkSight embeds data points into a low-dimensional space to compress deep classifiers, using dark knowledge for a new confidence measure.
result DarkSight visualizations are more informative and yield a new confidence measure.
SPREV simplifies visualization of complex labeled datasets.
problem Challenges of reducing dimensions and visualizing labeled datasets with small class size, high dimensionality, and low sample size.
method SPREV uses a novel dimensionality reduction technique integrating geometric principles.
result SPREV effectively visualizes hidden patterns in complex labeled datasets.
General-purpose model learns visual reasoning without strong priors.
problem Achieving visual reasoning in image-related questions.
method Conditional Batch Normalization approach.
result 2.4% error rate on CLEVR Visual Reasoning benchmark.
Visualizes 3D CNNs for protein-ligand scoring.
problem Interpreting complex neural network decisions for protein-ligand scoring.
method Three visualization methods for 3D CNNs, including filters and weights.
result Visualizations aid in tuning and designing neural networks.
Interactive visualization helps understand complex machine learning models.
problem Low interpretability of machine learning models.
method Interactive slice visualization of predictor space, using interaction or touring algorithms.
result Enhances understanding and validation of machine learning model fits.
Neural model predicts object states and physical parameters from visual observations.
problem Computational models struggle with physical reasoning and adapting to new environments.
method Visual prior predicts particle-based system from visual observations; inference module refines estimates subject to dynamics constraints.
result Model can infer physical properties within a few observations and adapt to unseen scenarios.
Visual integration helps understand ensemble model performance.
problem Lack of comprehensibility in ensemble models.
method Visual integration of data and model space for effective exploration and manipulation of ensemble models.
result Improved understanding of how each model contributes to ensemble performance.
AdvReg improves VQA models but introduces instability and bias issues.
problem VQA models over-rely on linguistic biases, ignoring visual context.
method Adversarial regularization to encourage bias-free question representations.
result AdvReg yields side-effects like unstable gradients and reduced performance on in-domain examples.
Proposes a method to measure model parameter similarity for visual tasks.
problem Estimating relations between different visual tasks.
method LPS method using a second-order neural network to align model parameters and learn second-order similarity.
result Extensive experiments validate the effectiveness of the proposed method.
RuleMatrix visualizes machine learning models for non-experts.
problem Making machine learning models transparent and interpretable for non-expert users.
method Extracts rule-based knowledge from model behavior and presents it in an interactive matrix visualization.
result RuleMatrix helps non-expert users understand and validate machine learning models.
FiLM layers improve visual reasoning tasks by modulating features.
problem Visual reasoning tasks that require multi-step, high-level processes.
method General-purpose FiLM layers that apply feature-wise linear transformations based on conditioning information.
result FiLM layers reduce error by half on the CLEVR benchmark and improve feature coherence.
FeatureEnVi aids in feature engineering with visual analytics.
problem Insufficient support for feature engineering in visual analytics tools.
method Stepwise selection and semi-automatic extraction approaches.
result Extracts heavily engineered features evaluated by multiple metrics.
Paper proposes a new black-box attack approach to minimize visual distortion.
problem Constructing adversarial examples that minimize visual distortion in a black-box threat model.
method Learning the noise distribution of adversarial examples to approximate the gradient of a non-differentiable loss function.
result The proposed attack results in much lower visual distortion compared to state-of-the-art black-box attacks.
New method to evaluate visual explanations from neural networks.
problem Lack of consensus on measuring effectiveness of visual explanations.
method Proposed a new procedure for evaluating explanations using a range of sources.
result Demonstrated the benefit of combining different sources and the impact of bias parameters.
This work tackles long-term visual planning by goal-conditioned hierarchical predictors.
problem Current learning approaches fail on long-horizon tasks due to lack of goal information and coarse-to-fine planning.
method Formulate goal-conditioned predictors (GCPs) and hierarchical models to predict trajectories between observations.
result GCPs enable effective long-term planning with much longer horizons than before.
Previously, we proposed a physically-inspired method to construct data points into an effective in-tree (IT) structure, in which the underlying cluster structure in the dataset is well revealed. Although there are some edges in the IT structure requiring to be removed, such undesired edges are generally distinguishable…
Two approaches for understanding and visualizing 3D surgery on manifolds.
problem Understanding and visualizing the effects of 3D surgery on manifolds.
method Two approaches: fundamental group alterations and visualizations of rotations.
result New visualizations and methods for understanding 3D surgery.
New methods for assessing and visualizing feature groups in machine learning models.
problem Lack of methods for interpreting feature groups in machine learning models.
method Permutation-based, refitting, and Shapley-based techniques for grouped feature importance. Introduced a sequential procedure for identifying stable feature combinations. Developed a combined features effect plot.
result Effective methods for assessing and visualizing the importance and effect of feature groups in machine learning models.
Hypothesis identifies useful transformations for self-supervised learning.
problem Understanding effective transformations for self-supervised learning.
method Observation and derivation of the VTSS hypothesis.
result Predicts when a transformation-based self-supervision will be useful.
Enhanced PCA method highlights essential features of clusters in high-dimensional data.
problem Interpreting clusters in dimensionality reduction results is challenging.
method Contrastive Principal Component Analysis (cPCA) for identifying essential features.
result ccPCA method effectively highlights essential features of clusters in high-dimensional data.
Interactive tool helps non-experts understand neural nets.
problem Difficulty in understanding deep learning for non-experts.
method Direct manipulation visualization via an open-source interactive tool.
result Enables quick intuition about neural nets through experimentation.
Visual system compares and evaluates machine learning models for clinical data predictions.
problem Challenges in comparing and evaluating different machine learning models for medical predictions.
method Developed a visual analytics system to compare and evaluate multiple models' prediction criteria and consistency.
result Demonstrated the effectiveness of the visual analytics system in assisting clinicians and researchers.
Paper proposes a deep learning architecture for generating long stories from images.
problem Maintaining context in long event sequences for visual storytelling.
method Hierarchical deep learning architecture with encoder-decoder networks and natural language descriptions.
result Our method outperforms state-of-the-art techniques on automatic evaluation metrics.
This work examines how non-identical data distributions affect Federated Learning performance.
problem The impact of non-identical data distributions on Federated Learning performance.
method Synthesized datasets with varying degrees of data distribution similarity, evaluated Federated Averaging algorithm performance, proposed server momentum mitigation.
result Performance of Federated Learning degrades as data distributions differ more, and a mitigation strategy improves accuracy.
Interactive tool for better understanding t-SNE projections.
problem Interpreting t-SNE projections can be challenging and misleading.
method Interactive visualization tool with different views.
result Improves understanding of t-SNE and its results.
TS-Insight visualizes Thompson Sampling for better debugging and trust.
problem Thompson Sampling's black box nature hinders debugging and trust.
method TS-Insight is a visual analytics tool that traces evolving posteriors and evidence counts.
result Visualizations help in verifying, diagnosing, and explaining Thompson Sampling dynamics.
SAFE detects fake news by analyzing text and image similarities.
problem Detecting fake news with less focus on text-image similarity.
method SAFE uses neural networks to extract text and visual features, then learns their relationship to predict fake news.
result SAFE effectively recognizes fake news based on text, images, or mismatches.