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 201–250 of 10580 papers

TitleStatusHype
Random Client Selection on Contrastive Federated Learning for Tabular Data—0
Less is More: Multimodal Region Representation via Pairwise Inter-view LearningCode0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
Learning Repetition-Invariant Representations for Polymer Informatics—0
MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language ModelsCode2
A Representation Learning Approach to Feature Drift Detection in Wireless Networks—0
TARGET: Benchmarking Table Retrieval for Generative Tasks—0
Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease DetectionCode0
Stable and Convexified Information Bottleneck Optimization via Symbolic Continuation and Entropy-Regularized TrajectoriesCode0
Unsupervised Multiview Contrastive Language-Image Joint Learning with Pseudo-Labeled Prompts Via Vision-Language Model for 3D/4D Facial Expression Recognition—0
A Multi-Task Foundation Model for Wireless Channel Representation Using Contrastive and Masked Autoencoder Learning—0
Unlocking Location Intelligence: A Survey from Deep Learning to The LLM EraCode0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
A Multi-scale Representation Learning Framework for Long-Term Time Series Forecasting—0
Empowering Vision Transformers with Multi-Scale Causal Intervention for Long-Tailed Image Classification—0
Large Language Model Enhancers for Graph Neural Networks: An Analysis from the Perspective of Causal Mechanism Identification—0
Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning—0
OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed DomainCode3
Representation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous NetworksCode0
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic—0
RealRep: Generalized SDR-to-HDR Conversion with Style Disentangled Representation Learning—0
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection—0
Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures—0
SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images—0
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics—0
Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph LearningCode0
From Millions of Tweets to Actionable Insights: Leveraging LLMs for User Profiling—0
Modeling Multi-Hop Semantic Paths for Recommendation in Heterogeneous Information Networks—0
Multi-Modal Molecular Representation Learning via Structure Awareness—0
Automated Learning of Semantic Embedding Representations for Diffusion Models—0
Adaptive Contextual Embedding for Robust Far-View Borehole Detection—0
Generalization Analysis for Contrastive Representation Learning under Non-IID Settings—0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation LearningCode0
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelsCode1
OWT: A Foundational Organ-Wise Tokenization Framework for Medical ImagingCode0
Stochastic Variational Propagation: Local, Scalable and Efficient Alternative to Backpropagation—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Clustering with Communication: A Variational Framework for Single Cell Representation Learning—0
IIKL: Isometric Immersion Kernel Learning with Riemannian Manifold for Geometric Preservation—0
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network—0
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities—0
Interpretable Learning Dynamics in Unsupervised Reinforcement Learning—0
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
Gene finding revisited: improved robustness through structured decoding from learned embeddings—0
CoGenAV: Versatile Audio-Visual Representation Learning via Contrastive-Generative SynchronizationCode2
Fill the Gap: Quantifying and Reducing the Modality Gap in Image-Text Representation Learning—0
fastabx: A library for efficient computation of ABX discriminabilityCode1
GeoERM: Geometry-Aware Multi-Task Representation Learning on Riemannian ManifoldsCode0
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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