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 6901–6950 of 10580 papers

TitleStatusHype
Learning Generalizable Visual Representations via Interactive Gameplay—0
Learning Nuclei Representations with Masked Image Modelling—0
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations—0
On GNN explanability with activation rules—0
On Invariance and Selectivity in Representation Learning—0
Learning Degradation-Independent Representations for Camera ISP Pipelines—0
Learning Noise-Invariant Representations for Robust Speech Recognition—0
Does It Happen? Multi-hop Path Structures for Event Factuality Prediction with Graph Transformer Networks—0
cf2vec: Collaborative Filtering algorithm selection using graph distributed representations—0
Learning Noise-Invariant Representations for Robust Speech Recognition—0
Learning node embeddings via summary graphs: a brief theoretical analysis—0
Online Adversarial Purification based on Self-supervised Learning—0
Learning Neural Representation for CLIR with Adversarial Framework—0
On Linear Identifiability of Learned Representations—0
CETransformer: Casual Effect Estimation via Transformer Based Representation Learning—0
Learning Neural Ranking Models Online from Implicit User Feedback—0
Learning Natural Consistency Representation for Face Forgery Video Detection—0
Learning Music Sequence Representation from Text Supervision—0
Does Corpus Quality Really Matter for Low-Resource Languages?—0
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge—0
Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks—0
Learning multi-scale functional representations of proteins from single-cell microscopy data—0
Do DALL-E and Flamingo Understand Each Other?—0
Central Similarity Multi-View Hashing for Multimedia Retrieval—0
Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization—0
Online Limited Memory Neural-Linear Bandits—0
Action-Sufficient State Representation Learning for Control with Structural Constraints—0
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm—0
Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization—0
Online Representation Learning in Recurrent Neural Language Models—0
Learning Multi-Manifold Embedding for Out-Of-Distribution Detection—0
Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l1-Norm Distances—0
Document Representation Learning for Patient History Visualization—0
Learning Monolingual Compositional Representations via Bilingual Supervision—0
Document-Level Relation Extraction via Pair-Aware and Entity-Enhanced Representation Learning—0
On minimal variations for unsupervised representation learning—0
On Modeling Sense Relatedness in Multi-prototype Word Embedding—0
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level—0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning—0
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings—0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning—0
Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection—0
Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment—0
A Distribution-Dependent Analysis of Meta-Learning—0
On the Arrow of Inference—0
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective"—0
Center-wise Local Image Mixture For Contrastive Representation Learning—0
Learning Low-dimensional Manifolds for Scoring of Tissue Microarray Images—0
Recommendation with Attribute-aware Product Networks: A Representation Learning Model—0
Learning List-Level Domain-Invariant Representations for Ranking—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