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 7401–7425 of 10580 papers

TitleStatusHype
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?—0
Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach—0
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships—0
Privacy-preserving design of graph neural networks with applications to vertical federated learning—0
Privacy-Preserving Graph Convolutional Networks for Text Classification—0
Privacy-Preserving Machine Learning for Collaborative Data Sharing via Auto-encoder Latent Space Embeddings—0
Hierarchical Uncertainty-Aware Graph Neural Network—0
Quantum Architecture Search with Unsupervised Representation Learning—0
Hierarchical Transformer for Scalable Graph Learning—0
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective—0
Privacy-Preserving Speech Representation Learning using Vector Quantization—0
Privacy-preserving Voice Analysis via Disentangled Representations—0
Understanding Deep Contrastive Learning via Coordinate-wise Optimization—0
Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations—0
Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations—0
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models—0
Quaternion-Based Graph Convolution Network for Recommendation—0
Bilingual Distributed Word Representations from Document-Aligned Comparable Data—0
Hierarchical Structured Neural Network: Efficient Retrieval Scaling for Large Scale Recommendation—0
Probabilistic Lexical Manifold Construction in Large Language Models via Hierarchical Vector Field Interpolation—0
Secure Embedding Aggregation for Federated Representation Learning—0
Probabilistic Multimodal Representation Learning—0
Deep Contextual Recurrent Residual Networks for Scene Labeling—0
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning—0
Quaternion Collaborative Filtering for Recommendation—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