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 9101–9150 of 10580 papers

TitleStatusHype
Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding—0
Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted GraphsCode0
Representation Learning with Statistical Independence to Mitigate BiasCode1
Deep Network Classification by Scattering and Homotopy Dictionary LearningCode0
On the Interpretability and Evaluation of Graph Representation Learning—0
Representation Learning of EHR Data via Graph-Based Medical Entity Embedding—0
Rethinking Kernel Methods for Node Representation Learning on GraphsCode0
SCALOR: Generative World Models with Scalable Object RepresentationsCode0
Neural Multisensory Scene InferenceCode0
High Mutual Information in Representation Learning with Symmetric Variational Inference—0
Stacked Wasserstein Autoencoder—0
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector—0
Learning Robust Representations with Graph Denoising Policy Network—0
DPSOM: Deep Probabilistic Clustering with Self-Organizing MapsCode0
Animating Face using Disentangled Audio Representations—0
Improving Sample Efficiency in Model-Free Reinforcement Learning from ImagesCode1
Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph EmbeddingCode0
Joint Syntax Representation Learning and Visual Cue Translation for Video Captioning—0
ERL-Net: Entangled Representation Learning for Single Image De-Raining—0
Self-Supervised Representation Learning From Multi-Domain Data—0
A Large-scale Study of Representation Learning with the Visual Task Adaptation BenchmarkCode0
Predicting materials properties without crystal structure: Deep representation learning from stoichiometryCode1
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced DataCode0
Equivariant Hamiltonian Flows—0
Spread-gram: A spreading-activation schema of network structural learning—0
Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification—0
Facial Expression Recognition Using Disentangled Adversarial Learning—0
Learning Robust Data Representation: A Knowledge Flow Perspective—0
Federated User Representation Learning—0
Identifying Sparse Low-Dimensional Structures in Markov Chains: A Nonnegative Matrix Factorization Approach—0
BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization—0
RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image SynthesisCode0
Identifying through Flows for Recovering Latent RepresentationsCode0
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network—0
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages—0
Representation Learning with Ordered Relation Paths for Knowledge Graph CompletionCode0
Dimensionwise Separable 2-D Graph Convolution for Unsupervised and Semi-Supervised Learning on GraphsCode0
Privacy-preserving Representation Learning by Disentanglement—0
On unsupervised-supervised risk and one-class neural networks—0
Efficient High-Dimensional Data Representation Learning via Semi-Stochastic Block Coordinate Descent Methods—0
On the Unintended Social Bias of Training Language Generation Models with News Articles—0
MULTI-LABEL METRIC LEARNING WITH BIDIRECTIONAL REPRESENTATION DEEP NEURAL NETWORKS—0
Hierarchical Disentangle Network for Object Representation Learning—0
RISE and DISE: Two Frameworks for Learning from Time Series with Missing Data—0
Discriminability Distillation in Group Representation Learning—0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images—0
LARGE SCALE REPRESENTATION LEARNING FROM TRIPLET COMPARISONS—0
DeepPCM: Predicting Protein-Ligand Binding using Unsupervised Learned Representations—0
Improved Mutual Information Estimation—0
Attributed Graph Learning with 2-D Graph Convolution—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