A new method for few-shot learning using embedded class models and shot-free meta training.
problem Few-shot learning with limited data and varying number of samples per class.
method Learning embeddings for few-shot learning with embedded class models and shot-free meta training.
result Achieves state-of-the-art performance on standard few-shot benchmark datasets.
New framework infers multiple classes per image for one-shot learning.
problem Inferring multiple classes per image in one-shot learning.
method Compositional embedding framework with joint training of embedding and composition/query functions.
result Compositional embedding models outperform existing methods on various datasets.
Zero-shot audio classification using class label embeddings.
problem Classifying audio without labeled data.
method Bilinear model with audio feature embeddings and class label embeddings.
result Achieved accuracy up to 39.7% for natural audio categories.
CILF learns adaptive embeddings for class-incremental learning with novel class detection and model update.
problem Handling unknown classes and model update in streaming data with new classes.
method CILF uses decoupled prototype based loss for intra-class and inter-class structure improvement, and a learnable curriculum clustering operator for adaptive embedding.
result CILF effectively detects multiple novel classes and mitigates embedding confusion, while updating the model without catastrophic forgetting.
A novel multi-layer architecture for one-class classification using graph-embedded kernel ridge regression.
problem Outlier detection in one-class classification using only normal samples.
method Stacking various Graph-Embedded Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion.
result The proposed method outperforms existing one-class classifiers on 21 benchmark datasets.
ELM improves neural model embeddings for long-tail learning.
problem Learning skewed label distributions in neural models.
method Enforces margins in logit space and regularizes embedding distribution.
result ELM reduces generalization gap and tightens tail class embeddings.
Study explores embedding signature-changing manifolds into higher-dimensional spaces.
problem Smooth metric signature changes in spacetimes.
method Global isometric embeddings into higher-dimensional pseudo-Euclidean spaces.
result Explicit constructions of global embeddings into Minkowski and Misner spaces.
A probabilistic method for deep embedding that improves classification accuracy and interpretability.
problem Improving classification accuracy and interpretability in deep learning.
method A probabilistic approach that treats embeddings as random variables, using a product distribution over labeled instances and marginalizing prototype proximity.
result Superior large- and open-set classification accuracy compared to state-of-the-art methods.
Supervised (linear) embedding models like Wsabie and PSI have proven successful at ranking, recommendation and annotation tasks. However, despite being scalable to large datasets they do not take full advantage of the extra data due to their linear nature, and typically underfit. We propose a new class of models which …
Gen1S learns novel classes with 1-shot data using residual space and generative models.
problem Learning new classes with limited data in a growing dataset.
method Mapping embeddings to a residual space, using generative models to learn multi-modal distribution, and applying it as a structural prior.
result Consistent improvement over state-of-the-art methods in recognizing novel classes.
Article provides conditions for embedding braid groups into mapping class groups.
problem Embedding braid groups into mapping class groups.
method Necessary and sufficient condition for embedding finite index subgroups.
result Conditions for embedding braid groups into mapping class groups.
Enhanced word embeddings boost multiclass text classification accuracy.
problem Improving multiclass text classification accuracy using pre-trained embeddings.
method Proposed word-class embeddings (WCEs) to enhance pre-trained word embeddings.
result WCEs significantly improve multiclass text classification accuracy.
One of the earliest conjectures in computational learning theory-the Sample Compression conjecture-asserts that concept classes (equivalently set systems) admit compression schemes of size linear in their VC dimension. To-date this statement is known to be true for maximum classes---those that possess maximum cardinali…
Embedding calculus proves convergence for surfaces.
problem Proving convergence of embedding calculus for surfaces.
method Goodwillie-Weiss' embedding calculus for spaces of embeddings into a manifold of dimension at most two.
result Relates Johnson filtration of mapping class group to embedding calculus.
This paper tackles UDA by learning domain-invariant embeddings using distribution alignment and pseudo-labels.
problem Unsupervised domain adaptation between two visual domains.
method Shared deep encoder, Sliced-Wasserstein Distance, deep classifier, pseudo-labels for class alignment.
result Effective solution for training deep classification networks on source domain to generalize to target domain.
A new method for federated learning with only positive labels.
problem Learning with only positive labels leads to poor classifier performance.
method Federated Averaging with Spreadout (FedAwS) framework.
result FedAwS can almost match the performance of conventional learning with negative labels.
AVDA transfers knowledge from source to target domains using embeddings.
problem Transferring knowledge from a source domain to a target domain with limited labeled data.
method Adversarial Variational Domain Adaptation (AVDA) with deep embeddings and Gaussian Mixture Model.
result AVDA outperforms previous methods in semi-supervised few-shot domain adaptation.
Cost-effective method improves and re-purposes pre-trained GANs by fine-tuning class-embeddings.
problem Fine-tuning BigGANs from scratch is impractical due to instability and high computational cost.
method Fine-tuning only the class-embedding layer of pre-trained GANs.
result Significantly improved realism and diversity of samples, re-purposed for new tasks, and de-biased or improved diversity.
The paper classifies vertices in weighted networks using spectral embedding and edge weight distributions.
problem Classifying vertices in weighted networks where edge weights and adjacencies encode class membership.
method Introduced a edge weight distribution matrix to the K-Block Stochastic Block Model for weighted networks. Developed classification procedures based on spectral embedding of the unweighted adjacency matrix under two assumptions on edge weight distributions.
result Proposed classifiers outperform quadratic discriminant analysis on transformed weighted networks.
An embedding of the m-times punctured disc into the n-times punctured disc, for n>m, yields an embedding of the braid group on m strands B_m into the braid group on n strands B_n, called a geometric embedding. The main example consists of adding n-m trivial strands to the right of each braid on m strands. We show that …
FedGTEA learns new tasks in federated learning with task embeddings and alignment.
problem Federated class-incremental learning with task-specific knowledge and model uncertainty.
method Cardinality-Agnostic Task Encoder (CATE) for Gaussian task embeddings, 2-Wasserstein distance for inter-task alignment.
result FedGTEA achieves superior classification performance and mitigates forgetting.
Study on embedding tree products into groups, distinguishing them.
problem Quasi-isometric embedding of tree products into various groups.
method Using coarse embeddings of products of bushy trees into hierarchically hyperbolic spaces.
result Quasi-isometrically distinguish and rule out embeddings between groups.
Study on embeddings of surfaces in 4-manifolds and their mapping classes.
problem Characterizing and understanding embeddings of surfaces in 4-manifolds and their mapping classes.
method Analyzing smooth proper embeddings and mapping classes induced by diffeomorphisms of 4-manifolds.
result Most surfaces do not admit flexible embeddings in 4-manifolds with specific homology types.
This paper proposes a new method for embedding sequences using Wasserstein distances.
problem Embedding sequences in a metric space for better pattern recognition.
method Develops a deep learning model that embeds sequences as distributions and uses Wasserstein distances for comparison.
result Distributional embeddings using Wasserstein distances outperform traditional vector embeddings.
Two novel methods improve network embedding for completely-imbalanced labels.
problem Improving network embedding for networks with completely-imbalanced labels.
method Two novel semi-supervised network embedding methods: RSDNE and RECT.
result Experimental results show the superiority of the proposed methods.
Improves code2vec for Java classes by obfuscating variable names.
problem Code2vec's reliance on variable names makes it vulnerable to typos and attacks.
method Obfuscate variable names during code2vec training and aggregate method embeddings for class-level predictions.
result Obfuscated variable names improve model's robustness and accuracy.
New examples show immersions not homologous to embeddings.
problem Tackles the question of immersions vs. embeddings in manifolds.
method Provides examples showing immersions not homologous to embeddings.
result Double points represent non-trivial homology classes.
Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -…
Investigates neural codes and their embeddings, proving conjectures and introducing new code types.
problem Analyzing neural codes and their embedding dimensions.
method Combinatorial, topological, and algebraic analysis; proving conjectures; introducing new neural code types.
result Proves conjectures about neural codes and their embeddings, introduces new code types.
Study of pseudoconvex 3-manifolds in complex surfaces.
problem Understanding pseudoconvex 3-manifolds in complex surfaces.
method Develop tools for constructing topologically pseudoconvex embeddings and classify almost-complex structures.
result Every closed, oriented 3-manifold can be embedded in a compact complex surface realizing any homotopy class of almost-complex structures.
Word embeddings may not be uniquely defined due to incompatibility between invariant classes of transformations.
problem Incompatibility between word embeddings and evaluation functions leads to performance discrepancies.
method Formal treatment of identifiability issue, numerical examples, and proposed resolutions.
result Word embeddings are not unique and performance differences may be due to arbitrary elements.
Study on exotic smooth embeddings of surfaces in 4-manifolds, revealing different properties and complexities.
problem Understanding exotic smooth embeddings of surfaces in 4-manifolds.
method Analyzing smooth, proper embeddings of noncompact surfaces in 4-manifolds, focusing on exotic planes and annuli.
result Exotic planes and annuli exhibit radically different properties, with one class being simple enough to draw explicit level diagrams.
Nonorientable surface mapping class group embeds quasi-isometrically in its orientable cover.
problem Embedding nonorientable surface mapping class group in orientable surface mapping class group.
method Utilized semihyperbolicity of orientable surface mapping class group and orientation double covering properties.
result Injective homomorphism is a quasi-isometric embedding.
Embeddings of surfaces in 5-manifolds are classified by an invariant.
problem Classifying isotopy classes of surface embeddings in 5-manifolds.
method Construction of an invariant defined in terms of homotopy groups.
result Isotopic embeddings of surfaces in 5-manifolds are classified by the invariant.
Researchers embed gravitational instantons in higher-dimensional spaces.
problem Embedding gravitational instantons in higher-dimensional spaces.
method Construct isometric and conformally isometric embeddings of gravitational instantons in R8 and R7. result Embedding class of the Einstein--Maxwell instanton is equal to 3.
We study a class of nonlocal, energy-driven dynamical models that govern the motion of closed, embedded curves from both an energetic and dynamical perspective. Our energetic results provide a variety of ways to understand physically motivated energetic models in terms of more classical, combinatorial measures of compl…
Prototypical Networks improve multi-label classification accuracy.
problem Multi-label classification with nonlinear label dependencies.
method Formulate multi-label learning as class distribution in a non-linear embedding space. For each label, positive and negative embeddings are compactly distributed. Labels are inferred by measuring the distance to prototype positive or negative embeddings.
result Extensive experiments show improved accuracy compared to state-of-the-art algorithms.
Novel loss function improves few-shot classification performance.
problem Few-shot classification struggles with embedding space for new classes.
method Proposes self-compacting softmax loss (SSL) to prevent embedding space fullness.
result SSL leads to significant improvement in state-of-the-art performance.
New subgroups of mapping class groups constructed for infinite-type surfaces.
problem Constructing new subgroups of mapping class groups for infinite-type surfaces.
method Utilization of special homeomorphisms called shift maps and multipush maps.
result Countably (and uncountably in certain cases) many non-conjugate embeddings of subgroups into mapping class groups.
This paper extends boundary embedding results to coarsely convex spaces.
problem Generalizing boundary embedding results to coarsely convex spaces.
method Generalizing Dydak and Virk's work on Gromov hyperbolic spaces to coarsely convex spaces.
result Maps between coarsely convex spaces induce continuous maps between their boundaries.
New method for classifying disk embeddings in 4-manifolds.
problem Classifying smooth isotopy classes of neat embeddings of 2-disks in 4-manifolds.
method Using an invariant going back to Dax, constructing a group structure, and relating to mapping class groups.
result The group structure on isotopy classes of neat embeddings is usually not abelian or finitely generated.
Study improves speaker verification accuracy using angular based embedding learning.
problem Improving discriminative power of embeddings for open-set speaker verification.
method Optimizes angular distance and adds margin penalty, applying various angular margin embedding strategies and proposing inter-class regularization.
result Achieved impressive results with 16.5% improvement in EER and 18.2% improvement in minimum detection cost function.
LETS-GZSL tackles GZSL for time series classification, achieving high accuracy.
problem Recognizing unseen classes from time series data when only seen examples are labeled.
method Embedding-based approach combined with attribute vectors.
result Achieves a harmonic mean of at least 55% on most UCR datasets.
HGNN learns augmented features for deep multi-task learning.
problem Feature learning for deep multi-task learning.
method Hierarchical Graph Neural Network (HGNN) with two levels of graph neural networks.
result Significant performance improvement in classification tasks.
CAGNN learns graph embeddings without labels by clustering and refining graph topology.
problem Learning graph embeddings without labeled data.
method Cluster-aware graph neural network (CAGNN) with self-supervised learning and topology refinement.
result CAGNN achieves significant improvements in node clustering accuracy.
Study reveals significant performance flips in GLOD using repurposed graph classification datasets.
problem Performance discrepancies in graph-level outlier detection using repurposed classification datasets.
method Repurposed binary classification datasets for GLOD; analyzed ROC-AUC performance.
result Performance of GLOD models significantly flips depending on which class is down-sampled.
Proposes QQE for transforming and embedding data distributions.
problem Transforming and embedding data distributions for better representation or visualization.
method Quantile-Quantile Embedding (QQE) using quantile-quantile plot concept.
result QQE allows for better discrimination of classes in some cases.
New method learns dynamic node embeddings from temporal networks.
problem Temporal networks' temporal information is often ignored or approximated.
method Continuous-time dynamic network embeddings (CTDNEs) using temporal walks.
result CTDNEs achieve an average gain of 11.9% in AUC across all methods and graphs.