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 5501–5550 of 10580 papers

TitleStatusHype
Learning Policy Representations in Multiagent Systems—0
Learning Private Representations with Focal Entropy—0
Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Defeats GAN: A Simpler Model Outperforms in Knowledge Representation Learning—0
Adversarial Deep Learning in EEG Biometrics—0
Machine Learning for Molecular Dynamics on Long Timescales—0
Learning Rare Category Classifiers on a Tight Labeling Budget—0
MARNet: Multi-Abstraction Refinement Network for 3D Point Cloud Analysis—0
Learning Relational Representations with Auto-encoding Logic Programs—0
Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism—0
Challenging Assumptions in Learning Generic Text Style Embeddings—0
Learning Representations by Contrasting Clusters While Bootstrapping Instances—0
Learning Representations by Humans, for Humans—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning—0
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens—0
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation—0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
Learning Representations for Incomplete Time Series Clustering—0
Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI—0
Deep Visual-Semantic Quantization for Efficient Image Retrieval—0
Domain-Adversarial and Conditional State Space Model for Imitation Learning—0
Learning Representations from Audio-Visual Spatial Alignment—0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
Learning Representations in Reinforcement Learning:An Information Bottleneck Approach—0
Incidence Networks for Geometric Deep Learning—0
Learning Representations of Affect from Speech—0
In-bed Pressure-based Pose Estimation using Image Space Representation Learning—0
Learning Representations of Hierarchical Slates in Collaborative Filtering—0
Domain-Agnostic Clustering with Self-Distillation—0
Learning Representations of Missing Data for Predicting Patient Outcomes—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
Deep video representation learning: a survey—0
Learning Representations Robust to Group Shifts and Adversarial Examples—0
Learning Representations Using Complex-Valued Nets—0
Domain-aware Self-supervised Pre-training for Weakly-supervised Meme Analysis—0
Learning Retrospective Knowledge with Reverse Reinforcement Learning—0
Domain-aware Self-supervised Pre-training for Label-Efficient Meme Analysis—0
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition—0
M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation—0
Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning—0
Brain-aligning of semantic vectors improves neural decoding of visual stimuli—0
M^33D: Learning 3D priors using Multi-Modal Masked Autoencoders for 2D image and video understanding—0
Learning Robust Representations for Computer Vision—0
Improving Video Model Transfer With Dynamic Representation Learning—0
Learning Robust Representations with Graph Denoising Policy Network—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—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