One-Class Boundary Peeling detects outliers efficiently and robustly.
problem Unsupervised outlier detection in diverse data distributions.
method One-Class Boundary Peeling uses flexible boundaries generated by one-class SVMs and iteratively peels them.
result One-Class Boundary Peeling outperforms state-of-the-art methods in synthetic data simulations.
Measures neural network decision boundary volume to predict model performance.
problem Understanding the geometry of deep learning models for better performance.
method Local surface volumes to measure decision boundary, applying Weyl's tube formula.
result Smaller surface volume correlates with higher classification accuracy.
Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good technique for knowledge distillation is still an open problem. In this paper, we provide a new perspective based on a decision boundary, which…
The paper introduces boundary thickness as a measure for improving model robustness.
problem Improving the robustness of machine learning models to adversarial and non-adversarial corruptions.
method Introducing boundary thickness as a measure and showing how various procedures can increase it.
result Thicker decision boundaries lead to improved robustness against adversarial and out-of-distribution transforms.
We perform a replacement procedure in order to produce a free boundary minimal surface whose area achieves the min-max value over all disk sweepouts of a manifold whose boundary lie in a submanifold. Our result is based on a proof of the convexity of the energy for free boundary harmonic maps and a generalization of Co…
The study proves that certain manifolds with boundary cannot have metrics with positive intermediate curvatures.
problem Proving the nonexistence of metrics with positive intermediate curvatures on manifolds with boundary.
method Curvature obstruction theorems for manifolds with boundary.
result Topologically nontrivial compact manifolds with boundary cannot have metrics of positive m-intermediate curvature if the boundary is m-convex. Theory for gravity coupled with fields on manifolds with null-boundary.
problem Formulating a theory for gravity coupled with scalar, SU(n), and spinor fields on manifolds with null-boundary.
method Symplectic reduction of boundary fields and constraints analysis.
result The set of constraints does not form a first class system for the three couplings.
BDSG generates samples on distribution boundaries, improving anomaly detection.
problem Difficulty in capturing multimodal supports and approximating distribution tails.
method Invertible Residual Network (IResNet) and Residual Flow (ResFlow) for density estimation; compound loss function for boundary samples.
result Competitive performance on synthetic and multimodal data compared to existing methods.
Estimating boundaries from point clouds with improved accuracy and rigorous error estimates.
problem Identifying the boundary of a domain from point cloud samples.
method Developed new estimators for normal vectors, distances, and boundary tests; provided error estimates.
result Efficient and accurate estimators for boundary properties on point clouds.
In the process of calculating Noether's conservation laws, two sets of integration by parts are performed. Here it is shown why the boundary terms from the first set of integration by parts vanish.
Neural model detects phoneme boundaries from speech, outperforming baselines.
problem Phoneme boundary detection for speech processing applications.
method Learnable segmental features with a structured loss function.
result Model achieves state-of-the-art performance on TIMIT and Buckeye corpora.
We study cobordisms and cobordisms rel boundary of PL locally-flat disk knots $D^{n-2}\into D^n$. Cobordisms of disk knots that do not fix the boundary sphere knots are easily classified by the cobordism properties of these boundaries, and any two even-dimensional disk knots with isotopic boundary knots are cobordant r…
Transformers fine-tuned on synthetic data boost tabular data classification performance.
problem Improving tabular data classification accuracy.
method Fine-tuning ICL-transformers on synthetic datasets with complex decision boundaries.
result Fine-tuned ICL-transformers outperform regular neural networks on real-world datasets.
Unified framework improves cross-corpus EEG emotion recognition by aligning prototypes and refining decision boundaries.
problem Cross-corpus EEG emotion recognition suffers from performance degradation due to physiological variability and device inconsistencies.
method Prototype-driven Adversarial Alignment (PAA) framework with three configurations: local, contrastive, and boundary-aware.
result State-of-the-art performance improvements across four cross-corpus evaluation protocols.
Study optimal control with expectation constraint, proving smooth boundary and deriving numerical methods.
problem Optimal control with expectation constraint in a smooth boundary case.
method Uniform ellipticity proof, truncation argument, approximating sequence of PDEs, convergence analysis, numerical schemes.
result Proved smooth boundary and derived numerical methods for optimal control problem.
The paper explores the geometry and topology of DNN decision boundaries.
problem Understanding the geometric and topological properties of DNN decision boundaries.
method Differential geometry and the Gauss-Bonnet-Chern theorem.
result Computed the Euler characteristics of compact decision boundaries.
Automated vehicles need efficient testing, which this method addresses.
problem Current testing practices are insufficient for automated vehicles.
method Gaussian Process Classification to identify performance boundaries.
result It is feasible to test for challenging scenarios more efficiently.
We propose the labeled Čech complex, the plain labeled Vietoris-Rips complex, and the locally scaled labeled Vietoris-Rips complex to perform persistent homology inference of decision boundaries in classification tasks. We provide theoretical conditions and analysis for recovering the homology of a decision boundary fr…
Proposes a method to speed up model selection for classification.
problem Time-consuming model selection process and lack of dataset-specific insights.
method Relaxes decision boundaries of neural decision trees to find equivalent or better models.
result Reduces the scope of exploration needed for model selection.
Paper proposes a new classifier for hyperbolic spaces using horospherical boundaries.
problem Optimization of large margin classifiers in hyperbolic spaces.
method Horospherical decision boundaries for geodesically convex optimization.
result Geodesically convex optimization leads to globally optimal solutions.
DeepDIG generates samples near decision boundaries of deep neural networks for better understanding.
problem Limited knowledge of how deep neural networks make decisions.
method Adversarial example generation to create samples near decision boundaries.
result Characterized decision boundaries of various deep neural network models.
BEKAN uses RBFs and evolutionary methods to solve PDEs with boundary conditions.
problem Enforcing boundary conditions in neural networks for PDE solutions.
method Boundary condition-guaranteed evolutionary Kolmogorov-Arnold Network (BEKAN) with radial basis functions (RBFs). Incorporates Dirichlet, periodic, and Neumann conditions.
result BEKAN outperforms MLP and B-splines KAN in solving PDEs with boundary conditions.
Semantic boundary and edge detection aims at simultaneously detecting object edge pixels in images and assigning class labels to them. Systematic training of predictors for this task requires the labeling of edges in images which is a particularly tedious task. We propose a novel strategy for solving this task, when pi…
New method reduces model bias and variance by adjusting training sample weights based on label uncertainty.
problem Tradeoff between model bias and variance in classification models.
method Estimate label uncertainty, adjust training sample weights, and fine-tune decision boundary.
result Improves model performance and reduces variance in physical activity recognition.
Paper shows similarity learning can lead to strong binary classification performance.
problem How similarity learning can lead to good classification performance.
method Product-type formulation of similarity learning is connected to binary classification through an excess risk bound.
result Similarity learning can directly elicit a decision boundary for binary classification.
Proposes a boundary detection method inspired by LLE for high-dimensional data.
problem Identifying boundary points from data on an embedded manifold.
method Inspired by locally linear embedding, uses nearest neighbor search schemes and spectral properties of local covariance matrix.
result Enhanced boundary detection in noisy data.
Paper presents an algorithm to analyze DGNNs by identifying generative boundaries and sampling.
problem Challenges in identifying the generation mechanism of DGNNs.
method Explorative sampling algorithm to analyze DGNNs by defining generative boundaries and optimizing essential set.
result Empirically reveals the characteristics of DGNNs' internal layers and finds more homogeneous samples.
This paper provides a theoretical justification of the superior classification performance of deep rectifier networks over shallow rectifier networks from the geometrical perspective of piecewise linear (PWL) classifier boundaries. We show that, for a given threshold on the approximation error, the required number of b…
MIME uses mutual information minimization for better exploration in environments with abrupt transitions.
problem Agents struggle at abrupt environmental transitions.
method MIME learns a latent representation without predicting future states.
result MIME outperforms surprisal-driven agents at transition boundaries.
New method measures generalizability of deep neural networks based on decision boundary complexity.
problem Lack of generalization methods for deep neural networks.
method Created Decision Boundary Complexity (DBC) score to measure DNN complexity.
result Simpler decision boundaries lead to better generalizability, supporting Occam's Razor.
Given a compact four-dimensional Riemannian manifold (M,g) with boundary, we study the problem of existence of Riemannian metrics on M conformal to g with prescribed Q-curvature in the interior M˚ of M, and zero T-curvature and mean curvature on the boundary ∂M of M. This geometric …
Self-supervised model detects phoneme boundaries without annotations.
problem Unsupervised phoneme segmentation without manual annotations.
method Convolutional neural network trained with Noise-Contrastive Estimation.
result Model outperforms baselines on TIMIT and Buckeye corpora.
New method uses weighting vectors for efficient boundary and outlier detection.
problem Boundary and outlier detection in machine learning.
method Recast metric space magnitude as weighting vector, solve kernelized SVM, apply nearest neighbor methods.
result Weighting vector can be efficiently approximated in linear time, outperforming state-of-the-art techniques.
Homotopy theory for (2n+1)-dimensional manifold triads with fixed boundary.
problem Classifying stable moduli spaces of (2n+1)-dimensional manifold triads. method Homotopy-theoretic description of stable moduli spaces, stabilization by boundary connected sum with SnimesDn+1. result Established homology of stable moduli spaces for (2n+1)-dimensional manifold triads. Improved fourth-order compact scheme for option valuation with Robin boundary condition.
problem Lower convergence rates in numerical methods for American options.
method High-order compact scheme, Robin boundary condition, coupled nonlinear PDEs.
result Fourth-order convergence rate achieved without mesh refinement.
SageMath package diffstrata calculates intersection theory on abelian differentials.
problem Computing intersection theory on the boundary of strata of abelian differentials.
method Explicit combinatorial description of the boundary, implemented algorithms in SageMath.
result Computes the Euler characteristic of strata using intersection theory.
Proposes a novel method for generating hard negatives near time series data boundaries.
problem Challenges in generating effective negative samples for time series anomaly detection.
method Reconstruction-driven boundary negative generation framework using reinforcement learning.
result Improves anomaly representation learning and achieves competitive detection performance.
A new classifier improves one-class predictions on unevenly sampled data.
problem Non-uniformly sampled data affects one-class classifier performance.
method Dynamic decision boundary based on minimum spanning tree.
result Proves effectiveness and robustness compared to state-of-the-art classifiers.
We use flip points to explain and audit deep learning models, revealing decision boundaries and improving model performance.
problem Lack of interpretability in deep learning models hinders their use in important applications.
method Flip points are used to analyze decision boundaries of deep learning models with continuous output scores.
result Flip points reveal the least changes in input that would alter a model's classification, enabling better understanding and improvement of model behavior.
Deep neural networks' decision boundaries move closer to natural images during training.
problem Limited understanding of deep neural networks' decision boundaries and regions.
method Examined the minimum distance of data points to the decision boundary over training.
result The decision boundary moves closer to natural images during training, even in late epochs.
Deep learning models generalize by extending decision boundaries outside the convex hull of training data.
problem Understanding how deep learning models generalize beyond their training data.
method Investigation of decision boundaries inside and outside the convex hull of training sets, using various neural network architectures and training regimes.
result Over-parameterization is necessary for deep learning models to extend decision boundaries outside the convex hull of their training data.
GCAO improves clustering of high-dimensional data by grouping low-density boundary points.
problem Stability and accuracy of clustering in high-dimensional, non-uniform data.
method Group-level optimization with gravitational attraction and optimization.
result GCAO outperforms 11 clustering methods on multiple datasets.
FROB model improves robustness and reliable confidence for few-shot OoD detection.
problem Challenges in few-shot classification and OoD detection due to limited samples and adversarial attacks.
method FROB model combines support boundary generation and few-shot Outlier Exposure (OE) for improved robustness and reliable confidence.
result FROB achieves generalization to unseen OoD and maintains robustness independent of few-shot number.
MCBP detects boundaries in high-dimensional data using curvature.
problem Boundary detection in high-dimensional data.
method MCBP uses mean curvature to model data manifold curvature.
result MCBP improves clustering performance in complex scenarios.
Paper develops physics-informed, boundary-constrained Gaussian process for fluid flow field reconstruction.
problem Reconstructing fluid flow fields from limited data.
method Physics-informed, boundary-constrained Gaussian process regression.
result Derives physics-informed kernels for simulating incompressible flows.
We propose a simple but efficient method termed Guided Learning for weakly-labeled semi-supervised sound event detection (SED). There are two sub-targets implied in weakly-labeled SED: audio tagging and boundary detection. Instead of designing a single model by considering a trade-off between the two sub-targets, we de…
The goal of a decision-based adversarial attack on a trained model is to generate adversarial examples based solely on observing output labels returned by the targeted model. We develop HopSkipJumpAttack, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision…
A new method solves American put options with high accuracy and speed.
problem Solving American put options with high accuracy and speed.
method Adaptive fourth-order Runge-Kutta-Fehlberg method coupled with a fourth-order compact scheme.
result The method provides a more accurate solution and better performance in terms of computational speed.