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

Showing 69517000 of 10580 papers

TitleStatusHype
Learning Music Sequence Representation from Text Supervision0
Does Corpus Quality Really Matter for Low-Resource Languages?0
On Statistical Estimation of Edge-Reinforced Random Walks0
On the Behavior of Convolutional Nets for Feature Extraction0
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge0
Learning multi-scale functional representations of proteins from single-cell microscopy data0
Do DALL-E and Flamingo Understand Each Other?0
On the combination of graph data for assessing thin-file borrowers' creditworthiness0
Central Similarity Multi-View Hashing for Multimedia Retrieval0
Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization0
Action-Sufficient State Representation Learning for Control with Structural Constraints0
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm0
Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization0
Learning Multi-Manifold Embedding for Out-Of-Distribution Detection0
Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l1-Norm Distances0
Document Representation Learning for Patient History Visualization0
Learning Monolingual Compositional Representations via Bilingual Supervision0
On The Effects of Learning Views on Neural Representations in Self-Supervised Learning0
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning0
On the Forward Invariance of Neural ODEs0
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning0
Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection0
Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment0
Center-wise Local Image Mixture For Contrastive Representation Learning0
Learning Low-dimensional Manifolds for Scoring of Tissue Microarray Images0
Recommendation with Attribute-aware Product Networks: A Representation Learning Model0
Learning List-Level Domain-Invariant Representations for Ranking0
On the Importance of Distraction-Robust Representations for Robot Learning0
CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar0
A Role of Environmental Complexity on Representation Learning in Deep Reinforcement Learning Agents0
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups0
On the Importance of Looking at the Manifold0
Learning Less-Overlapping Representations0
On the Importance of Text Preprocessing for Multimodal Representation Learning and Pathology Report Generation0
Learning Latent Word Representations for Domain Adaptation using Supervised Word Clustering0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images0
CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation0
Learning Latent Topology for Graph Matching0
Learning latent state representation for speeding up exploration0
Learning latent representations for operational nitrogen response rate prediction0
DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation0
On the Latent Space of Wasserstein Auto-Encoders0
Census-Independent Population Estimation using Representation Learning0
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning0
Learning Language Representations with Logical Inductive Bias0
On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification0
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding0
Learning Job Titles Similarity from Noisy Skill Labels0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SciNCLAvg.81.8Unverified
2SPECTERAvg.80Unverified
3CiteomaticAvg.76Unverified
4Sci-DeCLUTRAvg.66.6Unverified
5SciBERTAvg.59.6Unverified
6BioBERTAvg.58.8Unverified
7CiteBERTAvg.58.8Unverified
#ModelMetricClaimedVerifiedStatus
1top_model_weights_with_3d_21:1 Accuracy0.75Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet 18Accuracy (%)97.05Unverified
#ModelMetricClaimedVerifiedStatus
1Morphological NetworkAccuracy97.3Unverified
#ModelMetricClaimedVerifiedStatus
1Max Margin ContrastiveSilhouette Score0.56Unverified