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 5551–5600 of 10580 papers

TitleStatusHype
ETA Prediction with Graph Neural Networks in Google Maps—0
Compressed Video Contrastive Learning—0
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression—0
Estimating Galactic Distances From Images Using Self-supervised Representation Learning—0
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—0
Comprehensive Multi-Dataset Evaluation of Reading Comprehension—0
A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval—0
Adaptive Discovering and Merging for Incremental Novel Class Discovery—0
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning—0
ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation—0
Guiding Graph Embeddings using Path-Ranking Methods for Error Detection innoisy Knowledge Graphs—0
Error Analysis on Graph Laplacian Regularized Estimator—0
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems—0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
A Transformer Based Handwriting Recognition System Jointly Using Online and Offline Features—0
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings—0
ERL-Net: Entangled Representation Learning for Single Image De-Raining—0
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning—0
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—0
Compositional Scene Representation Learning via Reconstruction: A Survey—0
Equivariant Representation Learning for Symmetry-Aware Inference with Guarantees—0
Compositional Representation Learning for Brain Tumour Segmentation—0
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets—0
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models—0
Adaptive Contextual Embedding for Robust Far-View Borehole Detection—0
Equivariant Representation Learning for Augmentation-based Self-Supervised Learning via Image Reconstruction—0
Equivariant Quantum Graph Circuits—0
Compositional Network Embedding—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Equivariant Hamiltonian Flows—0
Compositional Mixture Representations for Vision and Text—0
Compositionally Equivariant Representation Learning—0
Compositionality and Generalization in Emergent Languages—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning—0
Episodes Discovery Recommendation with Multi-Source Augmentations—0
Environment Predictive Coding for Visual Navigation—0
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language—0
Entity Profiling in Knowledge Graphs—0
Entity-level Cross-modal Learning Improves Multi-modal Machine Translation—0
Composable Generative Models—0
Composable Augmentation Encoding for Video Representation Learning—0
Dynamic Latent Separation for Deep Learning—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Component Analysis for Visual Question Answering Architectures—0
Entangled Residual Mappings—0
EnSiam: Self-Supervised Learning With Ensemble Representations—0
Complete-to-Partial 4D Distillation for Self-Supervised Point Cloud Sequence Representation Learning—0
Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement 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
6CiteBERTAvg.58.8—Unverified
7BioBERTAvg.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