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 3551–3600 of 10580 papers

TitleStatusHype
Group-Connected Multilayer Perceptron Networks—0
Growing ecosystem of deep learning methods for modeling proteinx2013protein interactions—0
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion—0
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification—0
EMP: Effective Multidimensional Persistence for Graph Representation Learning—0
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models—0
EmotionX-JTML: Detecting emotions with Attention—0
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval—0
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems—0
Fermi-Bose Machine achieves both generalization and adversarial robustness—0
Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning—0
A Survey on Spectral Graph Neural Networks—0
Few-shot Classification with Hypersphere Modeling of Prototypes—0
Emotion Dynamics Modeling via BERT—0
Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis—0
AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer—0
Few-Shot Learning via Learning the Representation, Provably—0
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images—0
Grounding-MD: Grounded Video-language Pre-training for Open-World Moment Detection—0
CoLLAP: Contrastive Long-form Language-Audio Pretraining with Musical Temporal Structure Augmentation—0
Grid Jigsaw Representation with CLIP: A New Perspective on Image Clustering—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
GridMind: A Multi-Agent NLP Framework for Unified, Cross-Modal NFL Data Insights—0
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion—0
Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies—0
A survey on Self Supervised learning approaches for improving Multimodal representation learning—0
EMCNet : Graph-Nets for Electron Micrographs Classification—0
Aggregation Schemes for Single-Vector WSI Representation Learning in Digital Pathology—0
Collaboratively Self-supervised Video Representation Learning for Action Recognition—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
Spatiotemporal Adaptive Neural Network for Long-term Forecasting of Financial Time Series—0
Finding Similar Exercises in Retrieval Manner—0
Green Learning: Introduction, Examples and Outlook—0
Finding the Trigger: Causal Abductive Reasoning on Video Events—0
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency—0
FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction—0
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation—0
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives—0
A Causal Disentangled Multi-Granularity Graph Classification Method—0
Embedding Shift Dissection on CLIP: Effects of Augmentations on VLM's Representation Learning—0
Embeddings and Representation Learning for Structured Data—0
Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning—0
A Survey on Malware Detection with Graph Representation Learning—0
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning—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