SOTAVerified

Representation Learning

Representation Learning is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be used for transfer learning. They are valuable across many fundamental machine learning tasks like image classification and retrieval.

Deep neural networks can be considered representation learning models that typically encode information which is projected into a different subspace. These representations are then usually passed on to a linear classifier to, for instance, train a classifier.

Representation learning can be divided into:

  • Supervised representation learning: learning representations on task A using annotated data and used to solve task B
  • Unsupervised representation learning: learning representations on a task in an unsupervised way (label-free data). These are then used to address downstream tasks and reducing the need for annotated data when learning news tasks. Powerful models like GPT and BERT leverage unsupervised representation learning to tackle language tasks.

More recently, self-supervised learning (SSL) is one of the main drivers behind unsupervised representation learning in fields like computer vision and NLP.

Here are some additional readings to go deeper on the task:

( Image credit: Visualizing and Understanding Convolutional Networks )

Papers

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Showing 3451–3500 of 10580 papers

TitleStatusHype
End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection—0
Combining Representation Learning with Tensor Factorization for Risk Factor Analysis - an application to Epilepsy and Alzheimer's disease—0
Continual Learning of Nonlinear Independent Representations—0
Fair Node Representation Learning via Adaptive Data Augmentation—0
End-to-end Binary Representation Learning via Direct Binary Embedding—0
Combining Representation Learning with Logic for Language Processing—0
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection—0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
Fair Representation Learning through Implicit Path Alignment—0
Fair Representation Learning using Interpolation Enabled Disentanglement—0
Fair Sufficient Representation Learning—0
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
Graph Neural Networks for Binary Programming—0
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization—0
Combining graph and sequence information to learn protein representations—0
Combining expert knowledge and neural networks to model environmental stresses in agriculture—0
Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring—0
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification—0
A Survey on Temporal Knowledge Graph: Representation Learning and Applications—0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling—0
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
Empowering Vision Transformers with Multi-Scale Causal Intervention for Long-Tailed Image Classification—0
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning—0
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings—0
Empowering Next POI Recommendation with Multi-Relational Modeling—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications—0
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines—0
Co-manifold learning with missing data—0
FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey—0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin—0
Graph Neural Network Based VC Investment Success Prediction—0
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective—0
EMP: Effective Multidimensional Persistence for Graph Representation Learning—0
EmotionX-JTML: Detecting emotions with Attention—0
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval—0
Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies—0
Feature-Based Lie Group Transformer for Real-World Applications—0
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning—0
A Survey on Spectral Graph Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SciNCLAvg.81.8—Unverified
2SPECTERAvg.80—Unverified
3CiteomaticAvg.76—Unverified
4Sci-DeCLUTRAvg.66.6—Unverified
5SciBERTAvg.59.6—Unverified
6BioBERTAvg.58.8—Unverified
7CiteBERTAvg.58.8—Unverified
#ModelMetricClaimedVerifiedStatus
1top_model_weights_with_3d_21:1 Accuracy0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet 18Accuracy (%)97.05—Unverified
#ModelMetricClaimedVerifiedStatus
1Morphological NetworkAccuracy97.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Max Margin ContrastiveSilhouette Score0.56—Unverified