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 7651–7700 of 10580 papers

TitleStatusHype
Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units—0
Representation Learning for Regime detection in Block Hierarchical Financial Markets—0
Representation Learning for Resource-Constrained Keyphrase Generation—0
Hierarchical Compact Clustering Attention (COCA) for Unsupervised Object-Centric Learning—0
Hierarchical Compact Clustering Attention (COCA) for Unsupervised Object-Centric Learning—0
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer—0
Deep Autoencoder-based Fuzzy C-Means for Topic Detection—0
Recursive Disentanglement Network—0
Hierarchical Banzhaf Interaction for General Video-Language Representation Learning—0
Deep Audio-Visual Learning: A Survey—0
Recursive Neighborhood Pooling for Graph Representation Learning—0
Recursive Neural Language Architecture for Tag Prediction—0
Biased Representation Learning for Domain Adaptation—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
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models—0
Reference Product Search—0
Bidirectional Correlation-Driven Inter-Frame Interaction Transformer for Referring Video Object Segmentation—0
An Efficient NAS-based Approach for Handling Imbalanced Datasets—0
Generating Privacy-Preserving Process Data with Deep Generative Models—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation—0
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild—0
Regeneration Learning: A Learning Paradigm for Data Generation—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE—0
Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood—0
Representation Learning for Recommender Systems with Application to the Scientific Literature—0
Representation Learning for Spatial Graphs—0
Regret Analysis of Multi-task Representation Learning for Linear-Quadratic Adaptive Control—0
Representation Learning in Low-rank Slate-based Recommender Systems—0
Re-Identification with Consistent Attentive Siamese Networks—0
ReIDTracker Sea: the technical report of BoaTrack and SeaDronesSee-MOT challenge at MaCVi of WACV24—0
Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion—0
HiCL: Hierarchical Contrastive Learning of Unsupervised Sentence Embeddings—0
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning—0
BiAdam: Fast Adaptive Bilevel Optimization Methods—0
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning—0
HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing—0
BG-Triangle: Bezier Gaussian Triangle for 3D Vectorization and Rendering—0
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention—0
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment—0
Deep Anomaly Detection in Text—0
Advanced atom-level representations for protein flexibility prediction utilizing graph neural networks—0
Representation Learning for Online and Offline RL in Low-rank MDPs—0
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection—0
Reinforcing Semantic-Symmetry for Document Summarization—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—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