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 3001–3050 of 10580 papers

TitleStatusHype
Unsupervised categorization of similarity measures—0
Mixture of Decoupled Message Passing Experts with Entropy Constraint for General Node Classification—0
Rethinking Tokenized Graph Transformers for Node Classification—0
Mesh2SSM++: A Probabilistic Framework for Unsupervised Learning of Statistical Shape Model of Anatomies from Surface MeshesCode0
A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision—0
HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell DataCode0
Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding—0
Structure-preserving contrastive learning for spatial time seriesCode0
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection—0
K-ON: Stacking Knowledge On the Head Layer of Large Language Model—0
Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-IdentificationCode0
From Image to Video: An Empirical Study of Diffusion Representations—0
Koopman-Equivariant Gaussian Processes—0
Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling—0
CrossVideoMAE: Contrastive Spatiotemporal and Semantic Representation Learning from Videos and Images with Masked Autoencoders—0
Learning Street View Representations with Spatiotemporal ContrastCode0
PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational AutoencodersCode0
A Foundational Brain Dynamics Model via Stochastic Optimal Control—0
Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting—0
Graph Contrastive Learning for Connectome ClassificationCode0
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning—0
Contextual Gradient Flow Modeling for Large Language Model Generalization in Multi-Scale Feature Spaces—0
Orthogonal Representation Learning for Estimating Causal Quantities—0
Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models—0
TopoCL: Topological Contrastive Learning for Time Series—0
OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Prediction—0
PH-VAE: A Polynomial Hierarchical Variational Autoencoder Towards Disentangled Representation Learning—0
RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning—0
Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences—0
Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution TasksCode0
Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier CalibrationCode0
DeepCell: Multiview Representation Learning for Post-Mapping Netlists—0
Multi-level Supervised Contrastive Learning—0
EdgeGFL: Rethinking Edge Information in Graph Feature Preference Learning—0
A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation—0
Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation—0
Particle Trajectory Representation Learning with Masked Point Modeling—0
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning—0
PolyhedronNet: Representation Learning for Polyhedra with Surface-attributed GraphCode0
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
Spectro-Riemannian Graph Neural Networks—0
Generic Multimodal Spatially Graph Network for Spatially Embedded Network Representation Learning—0
SSRepL-ADHD: Adaptive Complex Representation Learning Framework for ADHD Detection from Visual Attention Tasks—0
What is causal about causal models and representations?—0
Structural Embedding Projection for Contextual Large Language Model Inference—0
Self-Supervised Learning Using Nonlinear Dependence—0
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning—0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
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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