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 3401–3450 of 10580 papers

TitleStatusHype
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds—0
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning—0
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?—0
Interpretable Representation Learning from Videos using Nonlinear Priors—0
Bio2Token: All-atom tokenization of any biomolecular structure with Mamba—0
Rethinking Positive Pairs in Contrastive Learning—0
TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment—0
Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive LearningCode0
Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder for Model Order Reduction—0
Towards Active Participant-Centric Vertical Federated Learning: Some Representations May Be All You Need—0
IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age TransformationCode0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification—0
Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification—0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
A Two-Stage Learning-to-Defer Approach for Multi-Task Learning—0
MI-VisionShot: Few-shot adaptation of vision-language models for slide-level classification of histopathological imagesCode0
Visual Representation Learning Guided By Multi-modal Prior Knowledge—0
Learning from Neighbors: Category Extrapolation for Long-Tail Learning—0
log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction ModelingCode0
Structural Causality-based Generalizable Concept Discovery Models—0
Dynamic Contrastive Learning for Time Series RepresentationCode0
TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models—0
BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation—0
DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational SearchCode0
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning—0
G-NeuroDAVIS: A Neural Network model for generalized embedding, data visualization and sample generationCode0
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation—0
Context-Enhanced Multi-View Trajectory Representation Learning: Bridging the Gap through Self-Supervised Models—0
Normalizing self-supervised learning for provably reliable Change Point Detection—0
Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection—0
SemSim: Revisiting Weak-to-Strong Consistency from a Semantic Similarity Perspective for Semi-supervised Medical Image Segmentation—0
Representation Learning of Structured Data for Medical Foundation Models—0
Mitigating Dual Latent Confounding Biases in Recommender Systems—0
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs—0
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems—0
Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series—0
SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning—0
SOE: SO(3)-Equivariant 3D MRI EncodingCode0
Just-In-Time Software Defect Prediction via Bi-modal Change Representation LearningCode0
FedCCRL: Federated Domain Generalization with Cross-Client Representation LearningCode0
Network Representation Learning for Biophysical Neural Network Analysis—0
Towards Fair Graph Representation Learning in Social Networks—0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples—0
Representation Learning for Regime detection in Block Hierarchical Financial Markets—0
Enhancing JEPAs with Spatial Conditioning: Robust and Efficient Representation Learning—0
DiRW: Path-Aware Digraph Learning for Heterophily—0
JOOCI: a Framework for Learning Comprehensive Speech Representations—0
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal ContrastCode0
Information propagation dynamics in Deep Graph Networks—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