The paper tackles conditional multimodal learning using variational methods.
problem Learning conditional distributions between modalities.
method Variational methods for maximizing conditional log-likelihood.
result Generated faces are more representative of the attributes.
Two modifications improve classifier chains for multi-label classification.
problem Discrepancy between training and testing feature spaces in classifier chains.
method Proposed modifications to address attribute noise.
result Improved prediction performance in challenging cases.
This article is a continuation of work on construction and calculation various of modifications of invariant based on the use Euclidean metric values attributed to elements of manifold triangulation. We again address the well investigated lens spaces as a standard tool for checking the nontriviality of topological inva…
A new bias score method optimizes fairness in classification.
problem Ensuring fairness in binary classification under group constraints.
method Introducing bias scores and developing a post-hoc approach to adapt to fairness constraints.
result The method maintains high accuracy while ensuring fairness constraints.
New algorithm disentangles latent space in GANs using video sequences.
problem Learning disentangled latent spaces in GANs without supervision.
method Adversarial training with video sequences, modifying standard GAN algorithm.
result Disentangled latent space into content and motion attributes.
Machine learning identifies Shakespeare and Fletcher's contributions to Henry VIII.
problem Determining the relative contributions of Shakespeare and Fletcher in Henry VIII.
method Combined analysis of vocabulary and versification with machine learning techniques.
result Supports canonical division and new modifications of Henry VIII's authorship.
Paper compares GAN techniques for image generation and modification.
problem Improving image generation and modification techniques using GANs.
method Comparison of supervised and unsupervised GANs, addition of an encoder, use of Capsule Network as discriminator.
result Reconstruction and modification of images possible with GANs.
MCD automates counterfactual design searches for multi-modal tasks.
problem Designing for multi-objective goals and complex constraints.
method Model-agnostic counterfactual search method for multi-modal design modifications.
result MCD streamlines and automates counterfactual search, recommending effective design modifications.
Paper modifies automata learning to improve interpretability.
problem Lack of clear interpretation for automata models.
method Proposes a state-merging approach to modify finite state automata.
result Demonstrates applicability of key properties in various sequential data contexts.
Surrogate Data Analysis (SDA) is a statistical hypothesis testing framework for the determination of weak chaos in time series dynamics. Existing SDA procedures do not account properly for the rich structures observed in stock return sequences, attributed to the presence of heteroscedasticity, seasonal effects and outl…
A neural network approach unifies Lasso for variable selection.
problem Combining statistical and machine learning techniques for variable selection.
method Representing Lasso through a neural network and developing a new optimization algorithm.
result The new optimization algorithm achieves better performance than previous methods.
Kernel handles missing data, improves SVM performance.
problem Handling missing data in machine learning models.
method Constructs genRBF kernel that models missing attribute uncertainty.
result genRBF kernel outperforms other methods in SVM classification.
The UCR Time Series Archive expands from 85 to 128 datasets, offering advice and insights.
problem Lack of comprehensive data sets for time series analysis.
method Periodic expansions of the archive, providing advice and novel insights.
result A significant increase in the number of datasets from 85 to 128.
New flatness measure for neural nets is invariant to reparameterizations.
problem Lack of invariance in existing flatness measures to reparameterizations.
method Proposed a reparameterization-invariant flatness measure.
result The new flatness measure correlates with generalization error.
Develops a comprehensive theory of corruption in supervised learning.
problem Widespread corruption in data collection affects supervised learning problems.
method Introduces a general theory of corruption using Markov kernels, distinguishing and comparing corruption types.
result Establishes a unified framework for corruption types and develops mitigation strategies.
This work improves testing of machine learning model modifications using novel statistical methods.
problem Overfitting and conservative Bonferroni correction when testing multiple model modifications.
method Introduces alpha-recycling and SRGPs to control error rate and approve more beneficial modifications.
result Novel statistical methods approve a higher number of beneficial modifications than previous approaches.
SAM adds semantic attributes to language models for better interpretation and style variation.
problem Improving text interpretation and style variation in language models.
method SAM includes document attributes, scores them, and embeds them into the model's input space.
result SAM generates interpretable texts and shows superior performance on various datasets.
New method detects RNA modifications without prior training, revealing novel sites.
problem Detecting RNA modifications with high accuracy and sensitivity.
method Anomaly detection using nanopore raw ionic current signals and nearest neighbor comparison.
result Detects diverse RNA modifications without prior training, including a novel 2'-O-methylated site in DENV.
Efficiently updates classifiers after small dataset modifications.
problem Updating classifiers quickly after small dataset changes in large-scale problems.
method Proposes a method to bound optimal classifiers without re-training.
result Provides bounds on optimal classifiers with low computational cost.
We define an operation on homology B4 which we call an n-twist annulus modification. We give a new construction of smoothly slice knots and exotically slice knots via n-twist annulus modifications. As an application, we present a new example of a smoothly slice knot with non-slice derivatives. Such examples we…
AttGAN edits facial attributes by changing only what you want, preserving details.
problem Facial attribute editing with preservation of details.
method Encoder-decoder architecture with attribute classification and reconstruction learning.
result Outperforms state-of-the-arts on realistic attribute editing with preserved details.
This work addresses building fair and calibrated models.
problem Building models that are both fair and calibrated.
method Developed a new definition of fairness and showed that group-wise calibration results in fairness. Proposed post-processing techniques and modifications of calibration losses.
result Demonstrated that ensuring group-wise calibration results in a fair model under the new definition of fairness.
Proposes a Taylor framework to unify and analyze attribution methods.
problem Lack of a unified guideline for feature contribution assignment in machine learning models.
method Introduces a Taylor attribution framework to model the attribution problem and reformulates fourteen mainstream methods.
result Empirically validates the Taylor reformulations and reveals a positive correlation between performance and principles followed.
Unified framework for analyzing machine learning model attributions.
problem Lack of a general and theoretical framework for understanding attribution methods.
method Proposes a Taylor attribution framework to unify and analyze seven mainstream attribution methods.
result Established three principles for good attribution and empirically validated the Taylor reformulations.
Unified model generates representations for all nodes in growing graphs.
problem Cold start problem in growing graphs isolates new nodes.
method Generative graph convolutional network that learns adaptive node representations.
result Superior performance on citation network datasets.
Improved deep learning models using new attribution priors and expected gradients.
problem Improving interpretability and performance of deep learning models.
method Introducing new attribution priors and expected gradients method that satisfies interpretability axioms.
result Improves model performance across various real-world tasks.
Study shows stability of locally conformally balanced condition under modifications but not under small deformations.
problem Stability of locally conformally balanced condition under small deformations and modifications.
method Proved stability under proper modifications and instability under small deformations using examples and Hilbert-Chow map.
result Stability of locally conformally balanced condition under proper modifications and instability under small deformations.
DeepDiff predicts differential gene expression from histone modifications using deep learning.
problem Predicting differential gene expression from histone modification signals, capturing combinatorial effects.
method Attention-based deep learning architecture with multiple LSTM modules and attention mechanisms.
result DeepDiff significantly outperforms state-of-the-art baselines for differential gene expression prediction.
Wavelet Attribution Method (WAM) improves feature attribution for deep models.
problem Inability of pixel-based heatmaps to capture data structure and variability in feature attribution.
method Wavelet domain for feature attribution, leveraging spatial and scale-localized properties of wavelet coefficients.
result WAM provides quantitatively superior explanations across audio, image, and volume modalities.
Paper tackles attribute pattern learning in high-dimensional SLAMs.
problem Learning significant attribute patterns from high-dimensional SLAMs.
method Proposes a penalized likelihood method for selecting attribute patterns.
result Establishes selection consistency in overfitted SLAMs.
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
problem Lack of mutual affinity exploitation between nodes and attributes in graph convolution.
method Dual variational auto-encoders for node and attribute embeddings, Gaussian mixture model priors, mutual distance and clustering assignment hardening losses.
result Demonstrates effectiveness on real-world attributed network datasets.
Generates missing node attributes for better graph-based tasks.
problem Missing or incomplete node attributes degrade graph-based algorithms' performance.
method Deep adversarial learning-based method (NANG) to generate node attributes.
result Generated node attributes improve node classification and link prediction.
Normalizing Flows improve prediction interval efficiency in CP.
problem Inefficient prediction intervals in CP due to non-uniform error distribution.
method Train a Normalizing Flow to optimize the distance metric between errors and inputs.
result Optimized prediction intervals are more efficient and valid.
A new approach combines attributes and sequences for better item recommendations.
problem Difficulty in leveraging attribute information due to heterogeneity and sparseness.
method Heterogeneous Attribute Recurrent Neural Networks (HA-RNN) that incorporates heterogeneous attributes and captures sequential dependencies.
result Significant improvements over state-of-the-art models in item recommendation.
Proposes simplified SHAP for faster black-box model explanations.
problem Computational expense of SHAP for models with many features.
method Ensemble of random SHAPs with feature selection and point generation.
result Efficiency and properties demonstrated through numerical experiments.
Proposes a method to predict node attributes using network topology.
problem Predicting node attributes in graphs for various applications.
method Creates a feature map using all attributes of neighbors to predict attributes values for a node.
result Significantly improves prediction accuracy compared to baseline approaches.
Behavior modification improves prediction accuracy by nudging user behavior.
problem Improving prediction accuracy using behavior modification techniques.
method Combining prediction and behavior modification with reinforcement learning algorithms.
result Behavior modification can make predictions more certain but may not generalize.
Generates high-resolution faces based on attributes.
problem Creating realistic face images with user-specified attributes.
method Conditional CycleGAN, handling unpaired data and attribute control.
result Produces realistic face images with user-controlled attributes.
Investors seek to attribute performance to various features using Shapley value method.
problem Attributing performance to different features in an investment process.
method Use Shapley value method for attribution, either exactly or approximately.
result Shapley value method provides a preferred attribution approach.
Unified analysis of removal-based feature attributions robustness.
problem Robustness of removal-based feature attributions is not well understood.
method Theoretical analysis and upper bounds derivation for removal-based feature attributions under input and model perturbations.
result Upper bounds for the difference between intact and perturbed attributions derived under various perturbation settings.
A new method for disentangled latent spaces in VAEs that can manipulate attributes.
problem Disentangled representation of attributes in latent spaces of VAEs.
method Attribute-based regularization loss to enforce monotonic relationships between attributes and latent codes.
result Manipulation of attributes in latent spaces post-training.
VarNet learns and manipulates high-level attributes from inputs.
problem Manipulating high-level attributes of inputs.
method Generative model that learns attributes from data and can handle predefined attributes.
result VarNet can learn and manipulate relevant attributes from datasets.
AVA combines feature attribution methods for better model explanations.
problem Improving feature attribution methods for machine learning models.
method AVA: Aggregate Valuation of Antecedents, fusing antecedent event influence and value attribution.
result AVA provides better local and global model explanations.
Paper proposes adversarial modifications for link prediction models to improve robustness and interpretability.
problem Improving accuracy is not enough; robustness and interpretability are also crucial for link prediction models.
method Adversarial modifications to identify influential facts and evaluate model sensitivity and interpretability.
result The approach identifies the most influential facts and evaluates the sensitivity of link prediction models to additional facts.
Study of unimodular Sasaki and Vaisman Lie groups, determining all modifications explicitly.
problem Classifying unimodular Sasaki and Vaisman Lie groups.
method Applying the technique of modification to determine all homogeneous Sasaki and Vaisman manifolds of unimodular Lie groups explicitly.
result Complete classification of unimodular Sasaki and Vaisman Lie groups.
Develops framework to evaluate feature attribution methods.
problem Lack of ground truth for evaluating feature attribution methods.
method Proposes a framework including a dataset and metrics.
result Certain methods produce false positive explanations.
A single BLSTM network tackles ambiguous words in text data.
problem Ambiguity in text data, especially in technical domains.
method Proposes a single Bidirectional LSTM network for all ambiguous words.
result Comparable performance to top WSD algorithms on SensEval-3 benchmark.
Paper tackles embedding attributed sequences in unsupervised learning.
problem Mining tasks over attributed sequences with dependencies between sequences and attributes.
method Proposes a deep multimodal learning framework, NAS, for unsupervised learning of attributed sequences.
result NAS produces task-independent embeddings for various mining tasks on real-world datasets.