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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9801–9850 of 10580 papers

TitleStatusHype
Explaining Knowledge Graph Embedding via Latent Rule Learning—0
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?—0
Action-Affect Classification and Morphing using Multi-Task Representation Learning—0
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization—0
Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks—0
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning—0
Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution—0
Provable Benefit of Multitask Representation Learning in Reinforcement Learning—0
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study—0
Exploiting Invertible Decoders for Unsupervised Sentence Representation Learning—0
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning—0
Graph Inference Representation: Learning Graph Positional Embeddings with Anchor Path Encoding—0
Exploiting Group-level Behavior Pattern forSession-based Recommendation—0
Self-supervised learning for hotspot detection and isolation from thermal images—0
Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients—0
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding—0
Provable benefits of representation learning—0
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning—0
Exploiting the Distortion-Semantic Interaction in Fisheye Data—0
Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation—0
Exploration-Driven Representation Learning in Reinforcement Learning—0
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles—0
Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning—0
Exploring Balanced Feature Spaces for Representation Learning—0
Impact of Representation Learning in Linear Bandits—0
Exploring Deep Models for Practical Gait Recognition—0
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization—0
Provable General Function Class Representation Learning in Multitask Bandits and MDPs—0
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection—0
Pre-Trained Models for Heterogeneous Information Networks—0
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks—0
Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning—0
Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers—0
Provable Representation Learning for Imitation with Contrastive Fourier Features—0
Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling—0
Exploring representation learning for flexible few-shot tasks—0
Exploring Representation Learning for Small-Footprint Keyword Spotting—0
Provable Representation Learning for Imitation Learning via Bi-level Optimization—0
Exploring Set Similarity for Dense Self-supervised Representation Learning—0
Provable RL with Exogenous Distractors via Multistep Inverse Dynamics—0
Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification—0
Structural Inductive Biases in Emergent Communication—0
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Exploring Temporal Granularity in Self-Supervised Video Representation Learning—0
Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal—0
Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings—0
Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models—0
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning—0
Provably Efficient CVaR RL in Low-rank MDPs—0
Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning—0
Show:102550
← PrevPage 197 of 212Next →
All datasetsSciDocsAnimals-10CIFAR10Circle DataSports10

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