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 8351–8400 of 10580 papers

TitleStatusHype
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta—0
Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery—0
Graph Representation learning for Audio & Music genre Classification—0
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition—0
Self-supervised Learning: Generative or Contrastive—0
Self-Supervised Disentangled Representation Learning for Third-Person Imitation Learning—0
Scaling Laws and Representation Learning in Simple Hierarchical Languages: Transformers vs. Convolutional Architectures—0
Self-Supervised Disentanglement by Leveraging Structure in Data Augmentations—0
Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Scaling Law for Recommendation Models: Towards General-purpose User Representations—0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Scaling Experiments in Self-Supervised Cross-Table Representation Learning—0
DAS-MAE: A self-supervised pre-training framework for universal and high-performance representation learning of distributed fiber-optic acoustic sensing—0
Group-wise Deep Co-saliency Detection—0
Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets—0
BERTERS: Multimodal Representation Learning for Expert Recommendation System with Transformer—0
Self-Supervised Feature Learning from Partial Point Clouds via Pose Disentanglement—0
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment—0
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences—0
An analysis on the effects of speaker embedding choice in non auto-regressive TTS—0
Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train—0
Growing Representation Learning—0
A Comparative Study of Embedding Models in Predicting the Compositionality of Multiword Expressions—0
ScaleNet: An Unsupervised Representation Learning Method for Limited Information—0
DARI: Distance metric And Representation Integration for Person Verification—0
Self-supervised Learning for Unintentional Action Prediction—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition—0
Graph Reinforcement Learning for Power Grids: A Comprehensive Survey—0
Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery—0
Self-supervised Graph Representation Learning via Bootstrapping—0
Scalable Semi-Supervised Query Classification Using Matrix Sketching—0
Graph Regularized Nonnegative Tensor Ring Decomposition for Multiway Representation Learning—0
Beyond H&E: Unlocking Pathological Insights with Polarization via Self-supervised Learning—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision—0
Self-Supervised Image Representation Learning with Geometric Set Consistency—0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss—0
Graph Regularized and Feature Aware Matrix Factorization for Robust Incomplete Multi-view Clustering—0
Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation—0
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees—0
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples—0
Scalable Representation Learning for Multimodal Tabular Transactions—0
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation—0
Self-supervised Learning for Large-scale Item Recommendations—0
DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains—0
A Named Entity Recognition Shootout for German—0
Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep 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
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