Paper tackles multi-label zero-shot learning, improving label embedding projection for unseen classes.
problem Challenges in transferring knowledge from seen to unseen classes in multi-label zero-shot learning.
method Proposes a transfer-aware embedding projection approach to project label embeddings into a low-dimensional space for better inter-label relationships and explicit information transfer.
result Demonstrates the efficacy of the proposed approach through experiments on zero-shot multi-label image classification.
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.
A new method embeds labels and group information for efficient multi-label classification.
problem Efficient multi-label classification with label sparsity and group structure.
method Identifies label groups, embeds labels and features in a low-dimensional space preserving sparsity and group structure.
result Our method outperforms state-of-the-art algorithms on benchmark 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.
This work analyzes label embedding for large multiclass classification problems.
problem Label embedding for large multiclass classification problems.
method Analysis of label embedding in extreme multiclass classification, presenting an excess risk bound and showing a trade-off between computational and statistical efficiency.
result The statistical penalty for label embedding vanishes with sufficiently low coherence under the Massart noise condition.
MPVAE learns latent embeddings and label correlations for multi-label classification.
problem Challenging task of predicting multiple targets with label correlations.
method Proposes MPVAE, a novel framework that learns latent embedding spaces and label correlations using a Multivariate Probit model.
result MPVAE outperforms state-of-the-art methods on various application domains and is robust under noisy settings.
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.
The objective in extreme multi-label learning is to train a classifier that can automatically tag a novel data point with the most relevant subset of labels from an extremely large label set. Embedding based approaches make training and prediction tractable by assuming that the training label matrix is low-rank and hen…
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.
A new method improves node classification in graphs with limited labels.
problem Semi-supervised multi-label node classification in attributed graphs.
method Collaborative Graph Walk (Multi-Label-Graph-Walk) using reinforcement learning.
result Significantly better multi-label classification performance compared to state-of-the-art methods.
LNEMLC embeds label network for multi-label classification.
problem Lack of effective adaptation and preservation of generalization abilities for unseen label combinations.
method LNEMLC embeds label network to extend input space for any base multi-label classifier.
result Statistically significant improvements over simple kNN baseline classifier.
A new framework CL embeds features and labels for multi-label classification.
problem Exponential growth of output space in multi-label classification.
method Compact Learning (CL) framework that embeds features and labels simultaneously.
result CMLL maximizes label-feature dependency and minimizes label space loss.
edGNN improves graph embeddings for directed labeled graphs.
problem Improving node and graph embeddings for directed labeled graphs.
method edGNN is a GNN designed for directed labeled graphs, leveraging both topology and labels.
result edGNN is as powerful as the Weisfeiler-Lehman algorithm for graph isomorphism.
Locally-contextual CRFs improve sequence labeling performance.
problem Improving sequence labeling with contextual embeddings.
method Locally-contextual nonlinear CRFs using deep neural networks.
result Consistently outperforms linear chain CRF and previous state of the art.
A method to approximate instance-dependent label noise using instance-confidence embedding.
problem Real-world label noise that depends on individual instances.
method Variational approximation with instance embedding to capture instance-specific label corruption.
result ICE method effectively approximates instance-dependent noise and detects ambiguous instances.
Enhances image classification by integrating semantic hierarchy into CNN models.
problem Limited use of external guidance in image classification.
method Integrates label-hierarchy knowledge into CNN-based classifiers and uses order-preserving embeddings.
result Boosts image classification performance through semantic hierarchy integration.
Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, b…
Enhances medical code predictions for multi-morbidity patients using text classification.
problem Improving accuracy in predicting medical codes for patients with multiple illnesses.
method Used machine learning techniques, including multi-label medical text classification, to enhance predictions.
result High dimensional embeddings pre-trained on health data significantly improve multi-label classification performance.
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.
Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels…
End-to-end deep metric learning tackles multi-label image classification.
problem Multi-label image classification problem.
method Two-way deep distance metric learning in a latent space with a reconstruction module.
result Our method outperforms state-of-the-arts on publicly available image datasets.
Combines foundation models with weak supervision to improve NLP and video tasks.
problem Leveraging weak supervision with foundation models without labeled data.
method Liger, a combination of foundation model embeddings and weak supervision techniques.
result Liger outperforms existing weak supervision methods by 14.1 points on benchmark NLP and video tasks.
Earth observation embeddings can convert discrete biome maps into continuous representations that better capture ecological variation.
problem Biome maps impose categorical boundaries that compress continuous variation in biotic communities.
method Fit a linear classifier on Earth observation embeddings to predict biome labels.
result Continuous biome representation outperforms discrete biome labels for predicting species occurrence.
We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of sequence cl…
Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph…
Recent advances in the field of network embedding have shown the low-dimensional network representation is playing a critical role in network analysis. However, most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly. In this paper, we t…
Paper proposes a progressive ensemble network for zero-shot image recognition.
problem Challenges of zero-shot learning due to lack of labeled data and expanding categories.
method Proposes a progressive ensemble network with multiple projected label embeddings.
result Demonstrates improved zero-shot image recognition performance on multiple datasets.
The paper introduces a method for detecting principal communities and embedding vertices.
problem Detecting and embedding vertices in graphs with community structure.
method Principal graph encoder embedding method that detects principal communities and produces vertex embeddings.
result The method successfully detects principal communities and produces accurate vertex embeddings.
Efficient label acquisition processes are key to obtaining robust classifiers. However, data labeling is often challenging and subject to high levels of label noise. This can arise even when classification targets are well defined, if instances to be labeled are more difficult than the prototypes used to define the cla…
NLE embeds labels for domain adaptation with neural networks.
problem Adapting deep neural networks with unpaired source and target domain data.
method Distill source-domain knowledge into l-vectors, soft targets for adaptation.
result 14.1% relative word error rate reduction over direct re-training.
MetaTNE tackles few-shot novel labels in graphs, improving node classification.
problem Node classification on graphs with novel labels and limited training data.
method MetaTNE framework with structural, meta-learning, and optimization modules.
result MetaTNE significantly improves node classification over state-of-the-art methods.
Improved acoustic word embeddings using shared decoder in multi-view encoders.
problem Learning discriminative acoustic word embeddings from text labels.
method Combining Siamese multi-view encoders with a shared decoder network to maximize the relationship between acoustic and text embeddings.
result 11.1% relative improvement in average precision on acoustic word discrimination task with WSJ dataset.
Paper proposes M3S training for GCNs on graphs with few labels.
problem Learning graph embeddings with few labeled nodes is challenging.
method Multi-Stage Self-Supervised (M3S) Training Algorithm combining self-supervised learning.
result M3S Training Algorithm improves GCNs' generalization on graphs with few labeled nodes.
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.
A structured prediction method for ranking labels.
problem Solving label ranking problems as structured output regression.
method Two-step approach: regression in feature space followed by pre-image solving.
result Efficiency on real-world datasets for partial and complete rankings.
Proposes a new method for MIR using kernel mean embeddings.
problem Multiple instance regression (MIR) where bags contain multiple instances with a single label.
method Computes kernel mean embeddings of predicted label distributions and learns a regressor from these embeddings.
result Better results than baseline instance-MIR across all datasets, state-of-the-art on two.
The thesis introduces methods to use semantic hierarchy in image classification.
problem Limited work in training image classifiers with non-conventional external guidance.
method Injects label hierarchy knowledge into arbitrary classifiers and uses order-preserving embeddings for image classification.
result Both embedding-based models and CNN-classifiers with hierarchical information outperform a hierarchy-agnostic model.
We present a method for training multi-label, massively multi-class image classification models, that is faster and more accurate than supervision via a sigmoid cross-entropy loss (logistic regression). Our method consists in embedding high-dimensional sparse labels onto a lower-dimensional dense sphere of unit-normed …
A simple baseline for extreme multi-label classification using random projections.
problem Automatically annotating data points with relevant labels from a large label vocabulary.
method On-the-fly global embedding using random projections, with an ensemble of learners.
result Competitive accuracy compared to existing methods, with significant speed-up and model-size reduction.
The set of factorizations of permutations in to m transpositions of some symmetric group Sn is naturally in bijection with the set of graphs of order n and size m with both edges and vertices labeled. We define a notion of duality (the \emph{mind-body duality}) for factorizations and such labeled gra…
E-Stitchup augments pre-trained embeddings for better model performance and confidence calibration.
problem Improving classification accuracy and confidence calibration of models using pre-trained embeddings.
method Data augmentation methods inspired by Mixup combined with label softening.
result Significantly increased classification accuracy and reduced training time.
Unified model combines feature and label propagation for semi-supervised classification.
problem Combining feature and label propagation for effective semi-supervised classification.
method Unified Message Passing Model (UniMP) using Graph Transformer and masked label prediction.
result Obtains new state-of-the-art results in Open Graph Benchmark (OGB).
Improved OOD detection using label smoothing and k-NN density estimates.
problem Detecting out-of-distribution examples in classification models.
method Label smoothing and k-NN density estimate on intermediate activations.
result Label smoothing improves OOD detection performance, both theoretically and empirically.
D2V learns domain-specific embeddings for domain generalization.
problem Learning decision functions across multiple related domains with limited labeled data.
method Proposes a neural network architecture, Domain2Vec (D2V), that learns domain-specific embeddings and uses them for generalization.
result D2V outperforms other algorithms in domain generalization tasks for image classification.
InfoSEM infers gene regulatory networks without GT labels, improving performance.
problem Inferring GRNs from gene expression data with high accuracy and avoiding biases.
method InfoSEM uses deep generative models with informative priors (textual gene embeddings).
result InfoSEM outperforms existing models by 38.5% across four datasets.
A new model embeds word and label hierarchies in hyperbolic space for HMLC.
problem Learning mappings from word hierarchies to label hierarchies in hierarchical multi-label classification.
method Proposes a Hyperbolic Interaction Model (HyperIM) to learn label-aware document representations in hyperbolic space.
result Demonstrates improved performance for HMLC compared to state-of-the-art methods.
No minimal chart of type (4,3) exists in 4-space.
problem Existence of minimal charts of specific type in 4-space.
method Investigation of charts and their properties in 4-space.
result No minimal chart of type (4,3) exists.
This paper studies minimal charts of a specific type to understand embedded surfaces in 4-space.
problem Investigating minimal charts of a specific type to understand embedded surfaces in 4-space.
method Analyzing charts of type (5,2) to find a minimal chart.
result Identified a minimal chart of type (5,2) representing an embedded surface in 4-space.