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 7901–7950 of 10580 papers

TitleStatusHype
CARL: Aggregated Search with Context-Aware Module Embedding Learning—0
Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study—0
A Prior Guided Adversarial Representation Learning and Hypergraph Perceptual Network for Predicting Abnormal Connections of Alzheimer's Disease—0
Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework—0
Label Noise Robust Image Representation Learning based on Supervised Variational Autoencoders in Remote Sensing—0
Discrimination-Aware Mechanism for Fine-Grained Representation Learning—0
Label-guided Learning for Text Classification—0
Label-efficient Time Series Representation Learning: A Review—0
Discriminability-enforcing loss to improve representation learning—0
Label Distribution Learning via Implicit Distribution Representation—0
Label Distribution Learning Forests—0
Representation Learning With Hidden Unit Clustering For Low Resource Speech Applications—0
Representation Learning with Information Theory for COVID-19 Detection—0
Discriminability Distillation in Group Representation Learning—0
Representation Learning with Multisets—0
Discriminability Distillation in Group Representation Learning—0
Label Consistent Transform Learning for Hyperspectral Image Classification—0
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification—0
Label Aware Speech Representation Learning For Language Identification—0
Representation Learning with Video Deep InfoMax—0
Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck—0
Label-Aware Graph Convolutional Networks—0
Discrete Infomax Codes for Supervised Representation Learning—0
Representations for Stable Off-Policy Reinforcement Learning—0
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes—0
Representation Transfer for Differentially Private Drug Sensitivity Prediction—0
A Preliminary Study of Disentanglement With Insights on the Inadequacy of Metrics—0
Kriformer: A Novel Spatiotemporal Kriging Approach Based on Graph Transformers—0
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Reproducible, incremental representation learning with Rosetta VAE—0
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning—0
Reprogramming Language Models for Molecular Representation Learning—0
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning—0
Repurposing Foundation Model for Generalizable Medical Time Series Classification—0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition—0
KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding—0
Koopman-Equivariant Gaussian Processes—0
Research on Domain Information Mining and Theme Evolution of Scientific Papers—0
K-ON: Stacking Knowledge On the Head Layer of Large Language Model—0
Research Team Identification Based on Representation Learning of Academic Heterogeneous Information Network—0
Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning—0
Residual Connections Encourage Iterative Inference—0
Career Path Prediction using Resume Representation Learning and Skill-based Matching—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals—0
Knowledge Router: Learning Disentangled Representations for Knowledge Graphs—0
Residual Relaxation for Multi-view Representation Learning—0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
Discovery and Separation of Features for Invariant Representation Learning—0
Knowledge Representation with Conceptual Spaces—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