Paper shows how to use elementary triplets to simplify independence model operations.
problem Simplifying operations with independence models.
method Using elementary triplets to represent conditional independences.
result Elementary triplets help in various operations like finding dominant triplets and computing model unions/intersections.
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.
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.
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.
Triplet networks improved with GANs for better classification.
problem Improving classification performance of triplet networks.
method Training a triplet network as the discriminator in GANs.
result Significant improvement in classification performance using simple k-nn.
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.
We investigate the Dolbeault operator on a pair of pants, i.e., an elementary cobordism between a circle and the disjoint union of two circles. This operator induces a canonical selfadjoint Dirac operator Dt on each regular level set Ct of a fixed Morse function defining this cobordism. We show that as we approac…
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.
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.
TristouNet improves speaker comparison using neural networks and triplet loss.
problem Speaker comparison and change detection in short speech turns.
method Triplet loss for training neural network to project speech sequences into fixed-dimensional space.
result Significant improvements over state-of-the-art techniques for speaker comparison and change detection.
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.
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. 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.
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…
TriMap improves data visualization by preserving global structure better than existing methods.
problem Visualizing high-dimensional data with preserved global structure.
method TriMap uses triplet constraints for dimensionality reduction.
result TriMap outperforms other methods in terms of runtime and quality of embedding.
Study analyzes stock market dynamics using Tsallis statistics and GHE, revealing pre-bubble and post-bubble market characteristics.
problem Understanding stock market dynamics and predicting market bubbles.
method Non-linear analysis using time-dependent Tsallis statistics and Generalized Hurst Exponents.
result Temporal trends of q-triplet values differ before and after market bubbles, indicating significant market dynamics changes.
Study compares crowdsourcing with lab experiments using comparison-based psychophysics.
problem Improving data quality in crowdsourcing psychophysics experiments.
method Comparison-based psychophysics, machine learning for triplet prediction.
result Accuracy of crowdsourcing psychophysics close to lab experiments.
Improved cover detection in music datasets with novel triplet loss.
problem Challenging task of automatically detecting covers in audio datasets.
method Convolutional neural network mapping melodic features to embeddings, training to minimize cover distance and maximize non-cover distance.
result New prototypical triplet loss improves accuracy for large datasets and live songs.
Kernel method detects higher order interactions in multi-view data for schizophrenia.
problem Detecting higher order interactions in multi-view biological data.
method Kernel method on reproducing kernel Hilbert space (RKHS) with mixed-effects linear model.
result Identified 13 triplets with significant correlations to hippocampal volume in schizophrenia.
This paper develops 4-manifold invariants using Hopf algebras.
problem Creating 4-manifold invariants from Hopf algebras.
method Using Hopf triplets and trisection diagrams, the authors construct 4-manifold invariants.
result Every Hopf triplet yields a diffeomorphism invariant of closed 4-manifolds.
Improved speaker diarization with attention-based embeddings.
problem Speaker diarization in automatic speech processing.
method Proposes attention-based embeddings and metric learning in an end-to-end fashion.
result Improved performance compared to existing approaches.
Interstellar searches for recurrent architecture to enhance KG embedding.
problem Learning long-term information in KGs.
method Recurrent neural architecture search for relational paths.
result Effectiveness and efficiency of searched models.
Deep learning has proven itself as a successful set of models for learning useful semantic representations of data. These, however, are mostly implicitly learned as part of a classification task. In this paper we propose the triplet network model, which aims to learn useful representations by distance comparisons. A si…
A CNN model learns complex relationships in knowledge graphs.
problem Exploring complex relationships between entities and relationships in knowledge graphs.
method A Convolutional Neural Network (CNN) is used to learn entity and relationship representations in knowledge graphs.
result The proposed model outperforms state-of-the-art models on exploring unseen relationships.
Paper proposes unsupervised knowledge graph alignment with adversarial learning.
problem Aligning knowledge graphs from different sources or languages without large amounts of aligned triplets.
method Adversarial learning framework to align entity and relation embeddings, with mutual information regularization.
result Framework effectively aligns knowledge graphs in unsupervised and weakly-supervised settings.
Study of commutator subgroups and crystallographic quotients of virtual groups.
problem Investigate commutator subgroups and crystallographic quotients of virtual groups.
method Derived explicit finite presentations and proved crystallographic properties.
result Explicit finite presentations of commutator subgroups and crystallographic quotients.