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 9251–9300 of 10580 papers

TitleStatusHype
Expected path length on random manifolds—0
CBOWRA: A Representation Learning Approach for Medication Anomaly Detection—0
Communal Domain Learning for Registration in Drifted Image Spaces—0
TabNet: Attentive Interpretable Tabular LearningCode1
Feature Interaction-aware Graph Neural Networks—0
ChainNet: Learning on Blockchain Graphs with Topological Features—0
Structural Health Monitoring of Cantilever Beam, a Case Study -- Using Bayesian Neural Network AND Deep Learning—0
Recommendation with Attribute-aware Product Networks: A Representation Learning Model—0
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded EmbeddingCode0
HONEM: Learning Embedding for Higher Order Networks—0
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures—0
Domain-adversarial Network AlignmentCode0
Two-stage Federated Phenotyping and Patient Representation Learning—0
Learning Target-oriented Dual Attention for Robust RGB-T Tracking—0
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task DistillationCode0
To Beta or Not To Beta: Information Bottleneck for DigitaL Image Forensics—0
TAPER: Time-Aware Patient EHR RepresentationCode0
Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation—0
Transferable Representation Learning in Vision-and-Language Navigation—0
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation LearningCode0
Sparse hierarchical representation learning on molecular graphs—0
Modeling Event Propagation via Graph Biased Temporal Point Process—0
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation—0
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding ModelsCode0
Unsupervised Learning of Depth and Deep Representation for Visual Odometry from Monocular Videos in a Metric Space—0
Semi-supervised representation learning via dual autoencoders for domain adaptationCode0
CARL: Aggregated Search with Context-Aware Module Embedding Learning—0
Predicting Dynamic Embedding Trajectory in Temporal Interaction NetworksCode1
Hybrid Low-order and Higher-order Graph Convolutional Networks—0
Learning Lightweight Lane Detection CNNs by Self Attention DistillationCode0
ProNE: Fast and Scalable Network Representation LearningCode0
Unsupervised Representation Learning and Anomaly Detection in ECG Sequences—0
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview—0
Arabic Named Entity Recognition: What Works and What's Next—0
MedNorm: A Corpus and Embeddings for Cross-terminology Medical Concept NormalisationCode0
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)—0
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information MaximizationCode1
On Mutual Information Maximization for Representation LearningCode0
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent RepresentationsCode1
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich TasksCode1
Charting the Right Manifold: Manifold Mixup for Few-shot LearningCode1
Multi-task Self-Supervised Learning for Human Activity Detection—0
Self-supervised Domain Adaptation for Computer Vision TasksCode0
Production Ranking Systems: A Review—0
Learning Embedding of 3D models with Quadric LossCode0
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning—0
Shared Generative Latent Representation Learning for Multi-view ClusteringCode0
Hyperlink Regression via Bregman Divergence—0
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning—0
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images—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