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 4901–4950 of 10580 papers

TitleStatusHype
Interpretable Node Representation with Attribute Decoding—0
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography—0
Learning Disentangled Audio Representations through Controlled Synthesis—0
Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification—0
Interpretable Load Forecasting via Representation Learning of Geo-distributed Meteorological Factors—0
Interpretable Learning Dynamics in Unsupervised Reinforcement Learning—0
Invariant Causal Representation Learning—0
Invariant Causal Representation Learning for Out-of-Distribution Generalization—0
Detection and Description of Change in Visual Streams—0
Invariant-equivariant representation learning for multi-class data—0
InvariantOODG: Learning Invariant Features of Point Clouds for Out-of-Distribution Generalization—0
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging—0
Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation—0
Invariant representation learning for sequential recommendation—0
Invariant Representation via Decoupling Style and Spurious Features from Images—0
Invariant Representations for Reinforcement Learning without Reconstruction—0
Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning—0
Byzantine Resilient Federated Multi-Task Representation Learning—0
Invariant Representations with Stochastically Quantized Neural Networks—0
Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image—0
Interpretable Foreground Object Search As Knowledge Distillation—0
Inverse Feature Learning: Feature learning based on Representation Learning of Error—0
Budgeted Embedding Table For Recommender Systems—0
Learning Discriminative Representation with Signed Laplacian Restricted Boltzmann Machine—0
Interpretable Deep Learning Paradigm for Airborne Transient Electromagnetic Inversion—0
Detecting Misinformation in Multimedia Content through Cross-Modal Entity Consistency: A Dual Learning Approach—0
BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification—0
Interpretable Causal Representation Learning for Biological Data in the Pathway Space—0
DialogSum Challenge: Summarizing Real-Life Scenario Dialogues—0
Investigating Power laws in Deep Representation Learning—0
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots—0
Investigating Speaker Embedding Disentanglement on Natural Read Speech—0
Investigating the Benefits of Projection Head for Representation Learning—0
Investigating the Properties of Neural Network Representations in Reinforcement Learning—0
A Novel Self-Knowledge Distillation Approach with Siamese Representation Learning for Action Recognition—0
Investigation into Target Speaking Rate Adaptation for Voice Conversion—0
Contrastive Video Representation Learning via Adversarial Perturbations—0
InvGAN: Invertible GANs—0
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization—0
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning—0
iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability—0
Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning—0
Learning dissection trajectories from expert surgical videos via imitation learning with equivariant diffusion—0
Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning—0
DICT-MLM: Improved Multilingual Pre-Training using Bilingual Dictionaries—0
Learning Flexible Visual Representations via Interactive Gameplay—0
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior—0
Interpretable Anomaly Detection in Cellular Networks by Learning Concepts in Variational Autoencoders—0
Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning—0
Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theory—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