Safe screening reduces the number of triplets in metric learning.
problem Optimizing a metric over many triplets is computationally expensive and impractical.
method Safe triplet screening identifies and removes redundant triplets.
result Safe triplet screening maintains optimality without increasing computational cost.
New method samples triplets from data distributions for training Triplet networks.
problem Training robust Triplet networks with discriminative triplets.
method Bayesian updating of multivariate normal distributions for dynamic class embedding sampling.
result Experimental validation on MNIST and histopathology CRC datasets shows effectiveness of the proposed method.
Efficiently augments triplet data for better data analytics.
problem Lack of direct pairwise distance information for data analysis.
method Triplets augmentation to infer hidden information from existing data.
result Improves quality of kernel-based and kernel-free data analytics.
New method accelerates large margin metric learning for nearest neighbor classification.
problem Efficiently learning metrics for nearest neighbor classification.
method Triplet mining and stratified sampling for large margin metric learning.
result Improved efficiency and scalability of optimization.
This paper analyzes the stability and generalization of triplet learning algorithms.
problem Lack of theoretical understanding of triplet learning's generalization performance.
method Stability analysis and high-probability generalization bounds for triplet learning algorithms.
result Established general high-probability generalization bound for triplet learning algorithms.
TripletGAN uses triplet loss to improve generative models, preventing mode collapse.
problem Mode collapse in generative models.
method Substituting discriminator's classification loss with triplet loss.
result TripletGAN helps prevent mode collapse and converges to the given distribution.
VBTA learns across domains using triplet information.
problem Learning across different domains using limited data.
method Variational Bi-domain Triplet Autoencoder (VBTA) with triplet constraints.
result Improved performance on various tasks.
Enhances DNN robustness with adversarial training and triplet loss.
problem Vulnerability of DNNs to adversarial examples.
method Adversarial Training with Triplet Loss (AT2L) incorporating triplet loss into adversarial training framework. result Significantly improves DNN robustness without accuracy loss.
Novel method decorrelates batches of triplets for active metric learning.
problem Correlation among triplets degrades active learning performance.
method Proposes a novel method to decorrelate batches of triplets, balancing informativeness and diversity.
result Method outperforms state-of-the-art in active metric learning.
Personalized activity recognition improves performance for diverse users.
problem Poor performance of impersonal algorithms for individual users.
method Personalized activity recognition using deep embeddings from a fully convolutional neural network with triplet loss.
result Novel subject triplet loss provides the best performance overall.
TripletBoost learns classifiers from noisy triplet comparisons.
problem Learning from comparison-based data.
method Aggregate weak classifiers from weakly learned triplets, then boost.
result Theoretical guarantees and empirical competitiveness.
The paper introduces uncertainty estimates for embedding objects based on noisy triplet comparisons.
problem Learning from ordinal data without a distance metric.
method Bootstrap and Bayesian approaches to estimate uncertainty for embedding algorithms.
result Empirical uncertainty estimates are well-calibrated and useful for selecting parameters or quantifying uncertainty.
In an independence model, the triplets that represent conditional independences between singletons are called elementary. It is known that the elementary triplets represent the independence model unambiguously under some conditions. In this paper, we show how this representation helps performing some operations with in…
Paper proposes an unbiased classifier from triplet comparison data.
problem Learning a classifier from triplet comparison data.
method Empirical risk minimization framework with an unbiased estimator.
result The proposed method achieves better performance than baseline methods.
Paper proposes semi-supervised learning with triplet Markov chains.
problem Lack of labels in training data.
method Variational Bayesian inference for semi-supervised learning.
result Derives semi-supervised algorithms for various sequential models.
Visualization tools show how embeddings generalize beyond validation data.
problem Understanding how learned embeddings generalize to new data.
method Visualization tools and triplet selection strategies for metric learning.
result Best performance in metric learning comes from selecting a few well-considered triplets.
This paper shows how optimizing with hard negative examples improves image retrieval.
problem Training with hard negative examples leads to poor training behavior.
method Characterize the space of triplets, derive why hard negatives fail, and offer a fix to the loss function.
result Optimizing with hard negative examples leads to more generalizable features and better image retrieval.
TVAE integrates deep metric learning into VAE for better latent embedding.
problem Lack of fine-grained data representation in traditional VAE.
method TVAE combines deep metric learning with VAE, optimizing a triplet loss on VAE's mean vectors.
result TVAE achieves higher triplet accuracy (95.60%) compared to traditional VAE (75.08%).
Abstracts a construction of boundary triplets for self-adjoint elliptic problems.
problem Computing the index of families of self-adjoint elliptic boundary problems.
method Abstract axiomatic version of boundary triplets and their applications.
result Analytic proof of index theorem and computation of index differences.
We analyze the semi-hard triplet loss using Edgeworth expansion for better understanding of its behavior.
problem Understanding the behavior of the semi-hard triplet loss function.
method Developed a higher-order asymptotic analysis using the Edgeworth expansion.
result Derived explicit Edgeworth expansions revealing first-order corrections in terms of the third cumulant.
Paper introduces new loss functions for Siamese networks using FDA.
problem Training Siamese networks with improved loss functions.
method Proposes Fisher Discriminant Triplet (FDT) and Fisher Discriminant Contrastive (FDC) loss functions based on FDA.
result Shows effectiveness of FDT and FDC on MNIST and histopathology datasets.
Develops optimal trading strategy for illiquid currency pairs.
problem Maximizes revenues for a broker liquidating an illiquid currency pair.
method Uses a currency triplet strategy, considering model ambiguity, and employs simulations.
result Mean P&L increases and standard deviation decreases as ambiguity aversion increases.
Triplet networks are widely used models that are characterized by good performance in classification and retrieval tasks. In this work we propose to train a triplet network by putting it as the discriminator in Generative Adversarial Nets (GANs). We make use of the good capability of representation learning of the disc…
Study of symplectic trivialization and reduction of bundles with symmetry and connection.
problem Symplectic trivialization and reduction of bundles with symmetry and connection.
method Analysis of the Tulczyjew's triplet with an Ehresmann connection.
result Trivializations and reductions of iterated tangent and cotangent bundles.
Given only information in the form of similarity triplets "Object A is more similar to object B than to object C" about a data set, we propose two ways of defining a kernel function on the data set. While previous approaches construct a low-dimensional Euclidean embedding of the data set that reflects the given similar…
End-to-end deep triplet ranking network for one-shot learning.
problem Efficiently classifying new classes with only one labeled instance.
method Triplet ranking loss for embedding learning and incorporating one-shot instances.
result Improved performance on one-shot learning datasets.
A new method uses triplet embeddings to improve human annotation for hidden constructs.
problem Improving human annotation for hidden constructs in machine learning.
method Proposes a novel annotation approach using triplet embeddings to lift absolute annotations to relative comparisons.
result Successfully represents synthetic hidden constructs in time under noisy sampling conditions.
PerceptNet learns haptic signal similarity using human data.
problem Designing haptic icons requires accurate perceptual similarity estimation.
method Deep neural network projecting signals to an embedding space with a triplet loss.
result Our method effectively models perceptual dissimilarity compared to alternatives.
Random forest uses only triplet comparisons to learn from metric spaces.
problem Learning from metric spaces without direct access to data or distances.
method A novel random forest algorithm that uses only triplet comparisons.
result The proposed random forest is consistent and competitive with other methods.
Involutory Hopf group-coalgebras provide new invariants for 4-manifold bundles.
problem Developing invariants for flat bundles over 4-manifolds.
method Utilizing Hopf G-triplets and colored trisection diagrams. result Involutory Hopf G-triplets yield well-defined invariants of G-colored trisection diagrams. RaSE screens variables via random subspaces, identifying joint effects.
problem Missing joint effects of predictors in ultra-high dimensional data.
method Random Subspace Ensemble (RaSE) framework combining subspace evaluation criteria.
result RaSE identifies signals with no marginal effect or high-order interactions.
Extends variable screening for ultrahigh-dimensional models, reducing dimensionality to sample size.
problem Statistical inference challenges in ultrahigh-dimensional linear models.
method Extends correlation-based variable screening to arbitrary linear models and post-screening inference techniques.
result Shows a condition (screening condition) sufficient for successful variable screening in arbitrary linear models.
InfoTuple efficiently selects larger tuple queries for ranking multiple objects, improving efficiency and consistency.
problem Efficiently selecting and ranking multiple objects for similarity learning.
method Adaptive selection method using mutual information maximization.
result InfoTuple outperforms state-of-the-art methods on synthetic and human response datasets.
Semi-supervised learning using BiGAN with triplet loss.
problem Training GANs with limited labeled data.
method BiGAN with triplet loss for semi-supervised learning.
result BiGAN latent space features improve classification and retrieval.
Deep Triplet Networks improve brain imaging modality recognition with limited data.
problem Efficiently recognizing new imaging modalities with scarce training data.
method Few-shot learning model based on Deep Triplet Networks.
result The model outperforms traditional CNN classifiers in modality recognition with limited data.
The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/samples. So far, safe screening has been individually studied either fo…
New Bayesian optimization models for efficient material screening.
problem Efficiently screening materials with expensive and cheap tests.
method Flexible multi-test Bayesian optimization models with complex relationships.
result Demonstrated power on synthetic and real data.
This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlation screening methods for three related applications: screening for variables with large correlations within a single trea…
New screening rules improve lasso model fitting efficiency.
problem Efficiently solving high-dimensional lasso problems.
method Look-ahead screening rules to discard predictors.
result Look-ahead screening rules outperform existing methods.
A new screening rule 'dynamic Sasvi' improves sparse optimization speed.
problem Sparse optimization problem identification.
method Flexible framework based on Fenchel-Rockafellar duality for norm-regularized least squares.
result Dynamic Sasvi can eliminate more features and increase solver speed.
Study on lightlike submanifolds in metallic semi-Riemannian manifolds.
problem Characterizing and investigating properties of lightlike submanifolds in metallic semi-Riemannian manifolds.
method Introduced and analyzed subclasses of screen transversal lightlike submanifolds and investigated their geometric properties.
result Necessary and sufficient condition for an isotropic screen transversal lightlike submanifold to be totally geodesic.
Learning a model of perceptual similarity from a collection of objects is a fundamental task in machine learning underlying numerous applications. A common way to learn such a model is from relative comparisons in the form of triplets: responses to queries of the form "Is object a more similar to b than it is to c?". I…
The paper studies special null hypersurfaces in spacetimes.
problem Characterizing null screen isoparametric hypersurfaces in Lorentzian space forms.
method Developed screen isoparametric hypersurface concept for null hypersurfaces of Robertson-Walker spacetimes, derived Cartan identities, and provided local characterizations.
result Derived Cartan identities for the screen principal curvatures of null screen hypersurfaces in Lorentzian space forms and provided a local characterization.
New AI platform screens portfolios for desirable firms and news.
problem Optimizing portfolio selection with AI.
method Two LLM agents screen for firm fundamentals and news sentiment. Agents deliberate to generate buy/sell signals. High-dimensional estimation determines optimal weights.
result Screened portfolio's Sharpe ratio consistently estimates target, superior to baseline and conventional approaches.
New screening test for LASSO reduces complexity.
problem Efficient screening for LASSO problems.
method Joint screening test for LASSO problem, applied to sphere and dome regions.
result Effective screening of atoms reduces computational complexity.
A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonp…
Recent computational strategies based on screening tests have been proposed to accelerate algorithms addressing penalized sparse regression problems such as the Lasso. Such approaches build upon the idea that it is worth dedicating some small computational effort to locate inactive atoms and remove them from the dictio…
A new screening method for high-dimensional data reduces computational cost.
problem Challenges in variable selection for ultrahigh-dimensional linear regression.
method Ordering absolute sample ridge partial correlations to screen variables.
result The method provides sure screening property without strong assumptions.