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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8301–8350 of 10580 papers

TitleStatusHype
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models—0
A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024—0
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields—0
Self-Distilled Representation Learning for Time Series—0
SceneRec: Scene-Based Graph Neural Networks for Recommender Systems—0
SelfDoc: Self-Supervised Document Representation Learning—0
Scene Graph Contrastive Learning for Embodied Navigation—0
Graph Representation Learning for Popularity Prediction Problem: A Survey—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Scene-Aware Feature Matching—0
Self-Labeling Refinement for Robust Representation Learning with Bootstrap Your Own Latent—0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs—0
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data—0
Graph Representation Learning for Interactive Biomolecule Systems—0
BERT Meets Relational DB: Contextual Representations of Relational Databases—0
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization—0
Graph Representation Learning for Infrared and Visible Image Fusion—0
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption—0
Self-supervised 3D Semantic Representation Learning for Vision-and-Language Navigation—0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity—0
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain—0
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
A Differential Topological View of Challenges in Learning with Feedforward Neural Networks—0
Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding—0
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations—0
Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta—0
Graph Representation learning for Audio & Music genre Classification—0
Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures—0
Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Scaling Law for Recommendation Models: Towards General-purpose User Representations—0
Grounding-MD: Grounded Video-language Pre-training for Open-World Moment Detection—0
Self-supervised Context-aware Style Representation for Expressive Speech Synthesis—0
Scaling Experiments in Self-Supervised Cross-Table Representation Learning—0
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering—0
DAS-MAE: A self-supervised pre-training framework for universal and high-performance representation learning of distributed fiber-optic acoustic sensing—0
BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer—0
An analysis on the effects of speaker embedding choice in non auto-regressive TTS—0
Self-Supervised Contrastive Pre-Training for Multivariate Point Processes—0
Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train—0
Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning—0
ScaleNet: An Unsupervised Representation Learning Method for Limited Information—0
DARI: Distance metric And Representation Integration for Person Verification—0
Show:102550
← PrevPage 167 of 212Next →
All datasetsSciDocsAnimals-10CIFAR10Circle DataSports10

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