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 7551–7600 of 10580 papers

TitleStatusHype
Contrastive Separative Coding for Self-supervised Representation Learning—0
On the Fairness of Generative Adversarial Networks (GANs)—0
A survey on Variational Autoencoders from a GreenAI perspectiveCode0
CogDL: A Comprehensive Library for Graph Deep LearningCode2
Representation Learning for Event-based Visuomotor PoliciesCode1
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-LearningCode1
A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis—0
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models—0
Consistent Assignment for Representation Learning—0
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information BottleneckCode0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
Persistent Homology and Graphs Representation Learning—0
How to represent part-whole hierarchies in a neural networkCode1
Towards a Unified Framework for Fair and Stable Graph Representation LearningCode1
Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data—0
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates—0
Theoretical Understandings of Product Embedding for E-commerce Machine Learning—0
Nonlinear Invariant Risk Minimization: A Causal Approach—0
SeqNet: Learning Descriptors for Sequence-based Hierarchical Place RecognitionCode1
Clustering Aware Classification for Risk Prediction and Subtyping in Clinical DataCode1
Learning Low-dimensional Manifolds for Scoring of Tissue Microarray Images—0
Return-Based Contrastive Representation Learning for Reinforcement Learning—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
Towards Causal Representation Learning—0
Reinforcement Learning with Prototypical RepresentationsCode1
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning ViewCode1
Self-Supervised Learning via multi-Transformation Classification for Action Recognition—0
Towards Building A Group-based Unsupervised Representation Disentanglement FrameworkCode1
E(n) Equivariant Graph Neural NetworksCode1
Contrastive Pre-training for Imbalanced Corporate Credit Ratings—0
Composable Generative Models—0
Switch Spaces: Learning Product Spaces with Sparse Gating—0
Fast Graph Learning with Unique Optimal SolutionsCode1
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks—0
TCN: Table Convolutional Network for Web Table InterpretationCode0
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n ParametersCode1
Training Stacked Denoising Autoencoders for Representation Learning—0
Meta-Path-Free Representation Learning on Heterogeneous Networks—0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents—0
Boosting Deep Transfer Learning for COVID-19 Classification—0
Training Larger Networks for Deep Reinforcement Learning—0
A Hidden Challenge of Link Prediction: Which Pairs to Check?Code0
HDMI: High-order Deep Multiplex InfomaxCode1
DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning—0
Information flows of diverse autoencodersCode0
Exploiting Shared Representations for Personalized Federated LearningCode1
Model-free Representation Learning and Exploration in Low-rank MDPs—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