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 7401–7450 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
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD—0
Hi-Gen: Generative Retrieval For Large-Scale Personalized E-commerce Search—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
HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks—0
Privacy-preserving Representation Learning for Speech Understanding—0
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information—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
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection—0
Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations—0
Deep Convolutional Transform Learning -- Extended version—0
Rare Event Detection using Disentangled Representation Learning—0
README: REpresentation learning by fairness-Aware Disentangling MEthod—0
Reasoning over Multi-view Knowledge Graphs—0
Recurrent Network Models for Human Dynamics—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
Hierarchical Uncertainty-Aware Graph Neural Network—0
Hierarchical Transformer for Scalable Graph Learning—0
Understanding Deep Contrastive Learning via Coordinate-wise Optimization—0
Probing Contextual Language Models for Common Ground with Visual Representations—0
Probing the Robustness of Independent Mechanism Analysis for Representation Learning—0
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning—0
Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation—0
Procedural Generalization by Planning with Self-Supervised World Models—0
Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations—0
Bilingual Distributed Word Representations from Document-Aligned Comparable Data—0
Proceedings of the 1st Workshop on Representation Learning for NLP—0
Proceedings of the 2nd Workshop on Representation Learning for NLP—0
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)—0
Proceedings of the 5th Workshop on Representation Learning for NLP—0
Proceedings of The Third Workshop on Representation Learning for NLP—0
Function space analysis of deep learning representation layers—0
Hierarchical Structured Neural Network: Efficient Retrieval Scaling for Large Scale Recommendation—0
Product Knowledge Graph Embedding for E-commerce—0
Fundamental Limits and Tradeoffs in Invariant Representation Learning—0
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning—0
Deep Contextual Recurrent Residual Networks for Scene Labeling—0
PROFIT: A Specialized Optimizer for Deep Fine Tuning—0
Programming knowledge tracing based on heterogeneous graph representation—0
Bilinear Supervised Hashing Based on 2D Image Features—0
Random vector functional link neural network based ensemble deep learning for short-term load forecasting—0
Deep Concept Identification for Generative Design—0
Progressive growing of self-organized hierarchical representations for exploration—0
Hierarchical Sparse Coding With Geometric Prior For Visual Geo-Location—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