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 6251–6300 of 10580 papers

TitleStatusHype
Multi-View Node Pruning for Accurate Graph Representation—0
Molecular Joint Representation Learning via Multi-modal Information—0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
Molecular Property Prediction by Semantic-invariant Contrastive Learning—0
Molecular Representation Learning by Leveraging Chemical Information—0
Multiview Pseudo-Labeling for Semi-supervised Learning from Video—0
End-to-End Graph-Sequential Representation Learning for Accurate Recommendations—0
A Causal Inference Approach for Quantifying Research Impact—0
MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts—0
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability—0
Nearest Neighbor Search Under Uncertainty—0
Neural-FacTOR: Neural Representation Learning for Website Fingerprinting Attack over TOR Anonymity—0
Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text—0
NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification—0
Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units—0
MOMA:Distill from Self-Supervised Teachers—0
MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation—0
Impact of Strategic Sampling and Supervision Policies on Semi-supervised Learning—0
Momentum Contrastive Autoencoder—0
Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE—0
Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classification—0
Deep Representation Learning and Clustering of Traffic Scenarios—0
MoNet: Deep Motion Exploitation for Video Object Segmentation—0
Monolingual Word Sense Alignment as a Classification Problem—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification—0
End-to-end representation learning for Correlation Filter based tracking—0
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning—0
IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making—0
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style—0
MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning—0
More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning—0
Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-supervised Speaker Verification—0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study—0
Morphological Classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs—0
Morphological Profiling for Drug Discovery in the Era of Deep Learning—0
Deep Reinforcement Learning with Decorrelation—0
IMB-NAS: Neural Architecture Search for Imbalanced Datasets—0
Multi-View Document Representation Learning for Open-Domain Dense Retrieval—0
Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis—0
Multi-view Graph Convolutional Networks with Differentiable Node Selection—0
MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading—0
motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks—0
Imbalanced Large Graph Learning Framework for FPGA Logic Elements Packing Prediction—0
Deep reinforcement learning guided graph neural networks for brain network analysis—0
English-Twi Parallel Corpus for Machine Translation—0
Enhanced Bilevel Optimization via Bregman Distance—0
Probing Contextual Language Models for Common Ground with Visual Representations—0
Deep Reinforcement Learning for NLP—0
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations—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