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 9901–9950 of 10580 papers

TitleStatusHype
Fair Sufficient Representation Learning—0
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
FAM: Visual Explanations for the Feature Representations From Deep Convolutional Networks—0
PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System—0
PTab: Using the Pre-trained Language Model for Modeling Tabular Data—0
FARM: Functional Group-Aware Representations for Small Molecules—0
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification—0
Fast Adaptive Federated Bilevel Optimization—0
Fast and Accurate Power Load Data Completion via Regularization-optimized Low-Rank Factorization—0
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning—0
Bailing-TTS: Chinese Dialectal Speech Synthesis Towards Human-like Spontaneous Representation—0
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes—0
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications—0
Purposer: Putting Human Motion Generation in Context—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction—0
FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE—0
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models—0
FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis—0
Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability—0
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification—0
FEATURE-AUGMENTED HYPERGRAPH NEURAL NETWORKS—0
Feature-Based Lie Group Transformer for Real-World Applications—0
Feature-based Neural Language Model and Chinese Word Segmentation—0
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition—0
Feature Disentanglement of Robot Trajectories—0
Pushing the Limits of 3D Shape Generation at Scale—0
Feature Forgetting in Continual Representation Learning—0
Feature-guided Neural Model Training for Supervised Document Representation Learning—0
Feature Imitating Networks—0
Feature Incay for Representation Regularization—0
Feature Interactive Representation for Point Cloud Registration—0
Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
Feature Projection for Improved Text Classification—0
Feature Propagation on Graph: A New Perspective to Graph Representation Learning—0
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives—0
Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling—0
Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition—0
Feature Transformers: A Unified Representation Learning Framework for Lifelong Learning—0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning—0
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features—0
Self-supervised On-device Federated Learning from Unlabeled Streams—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
FedDAR: Federated Domain-Aware Representation Learning—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