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 10301–10350 of 10580 papers

TitleStatusHype
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer—0
Recurring the Transformer for Video Action Recognition—0
Recursive Disentanglement Network—0
Recursive input and state estimation: A general framework for learning from time series with missing data—0
Graph Contrastive Learning with Generative Adversarial Network—0
Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search—0
Graph Contrastive Pre-training for Effective Theorem Reasoning—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Graph Convolutional Networks via Adaptive Filter Banks—0
Self-Supervised Learning Using Nonlinear Dependence—0
Recursive Neighborhood Pooling for Graph Representation Learning—0
Recursive Neural Language Architecture for Tag Prediction—0
Recursive Tree Attention: Improving Semantic Representations with Syntactic Tree Structured Attention Mechanism—0
A Comparative Study for Unsupervised Network Representation Learning—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization—0
Graph Embedding with Rich Information through Heterogeneous Network—0
Graph Enabled Cross-Domain Knowledge Transfer—0
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment—0
Graph Enhanced Representation Learning for News Recommendation—0
RedCore: Relative Advantage Aware Cross-modal Representation Learning for Missing Modalities with Imbalanced Missing Rates—0
RED: Effective Trajectory Representation Learning with Comprehensive Information—0
Redefining DDoS Attack Detection Using A Dual-Space Prototypical Network-Based Approach—0
Reduce, Reuse, Recycle: Is Perturbed Data better than Other Language augmentation for Low Resource Self-Supervised Speech Models—0
Relational Object-Centric Actor-Critic—0
Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices—0
Reference Product Search—0
Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation—0
Graph Learning and Its Advancements on Large Language Models: A Holistic Survey—0
Graph Learning with Localized Neighborhood Fairness—0
Self-Supervised Learning via multi-Transformation Classification for Action Recognition—0
Graphlets correct for the topological information missed by random walks—0
Graph-Level Embedding for Time-Evolving Graphs—0
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition—0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
Graph Neural Network Based VC Investment Success Prediction—0
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild—0
Graph Neural Networks for Binary Programming—0
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks—0
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview—0
A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning—0
Graph Neural Networks Including Sparse Interpretability—0
Regeneration Learning: A Learning Paradigm for Data Generation—0
Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective—0
Graph Neural Networks with Feature and Structure Aware Random Walk—0
Feature Interaction-aware Graph Neural Networks—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