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 7751–7800 of 10580 papers

TitleStatusHype
Deep Adversarial Transition Learning using Cross-Grafted Generative Stacks—0
BG-Triangle: Bézier Gaussian Triangle for 3D Vectorization and Rendering—0
Deep Adversarial Subspace Clustering—0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Self-Supervised Tracking via Target-Aware Data Synthesis—0
Representation Learning: A Statistical Perspective—0
Representation Learning based and Interpretable Reactor System Diagnosis Using Denoising Padded Autoencoder—0
Representation Learning Beyond Linear Prediction Functions—0
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond—0
Representation Learning by Ranking under multiple tasks—0
Representation Learning by Reconstructing Neighborhoods—0
Representation Learning by Rotating Your Faces—0
Heterophily-Aware Graph Attention Network—0
Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning—0
Representation Learning for Aspect Category Detection in Online Reviews—0
Representation Learning for Audio Privacy Preservation using Source Separation and Robust Adversarial Learning—0
Heterophilous Distribution Propagation for Graph Neural Networks—0
Representation Learning for Clustering: A Statistical Framework—0
Representation Learning via Non-Contrastive Mutual Information—0
Representation Learning for cold-start recommendation—0
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement—0
Representation Learning with Autoencoders for Electronic Health Records: A Comparative Study—0
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs—0
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning—0
Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Representation Learning for Discovering Phonemic Tone Contours—0
Representation Learning for Distributional Perturbation Extrapolation—0
Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting—0
Representation Learning for Efficient and Effective Similarity Search and Recommendation—0
Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking—0
Representation Learning for Electronic Health Records—0
Representation Learning via Cauchy Convolutional Sparse Coding—0
Representation Learning for Frequent Subgraph Mining—0
Representation Learning for General-sum Low-rank Markov Games—0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
Heterogeneous Skeleton-Based Action Representation Learning—0
Heterogeneous Representation Learning: A Review—0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Representation Learning via Adversarially-Contrastive Optimal Transport—0
Heterogeneous Graph Sparsification for Efficient Representation Learning—0
Representation Learning for Natural Language Processing—0
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach—0
Heterogeneous Graph Neural Network with Multi-view Representation Learning—0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Representation Learning for Online and Offline RL in Low-rank MDPs—0
Beyond Spatial Pooling: Fine-Grained Representation Learning in Multiple Domains—0
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach—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