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 10451–10500 of 10580 papers

TitleStatusHype
Integrated Sequence Tagging for Medieval Latin Using Deep Representation LearningCode0
Decision Forests, Convolutional Networks and the Models in-BetweenCode0
Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning—0
Learning to Generate with MemoryCode0
Semi-supervised Clustering for Short Text via Deep Representation Learning—0
Multi-layer Representation Learning for Medical ConceptsCode0
Attentive Pooling NetworksCode0
Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure PredictionCode0
Learning Distributed Representations of Sentences from Unlabelled DataCode0
Disentangled Representations in Neural Models—0
On Deep Multi-View Representation Learning: Objectives and OptimizationCode0
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware—0
Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks—0
Predicting the Co-Evolution of Event and Knowledge Graphs—0
On Deep Representation Learning from Noisy Web Images—0
Window-Object Relationship Guided Representation Learning for Generic Object Detections—0
節錄式語音文件摘要使用表示法學習技術 (Extractive Spoken Document Summarization with Representation Learning Techniques) [In Chinese]—0
Semi-Supervised Zero-Shot Classification With Label Representation Learning—0
Deep Neural Decision Forests—0
Category Enhanced Word Embedding—0
Learning with Memory Embeddings—0
The Variational Gaussian Process—0
Learning Representations Using Complex-Valued Nets—0
Joint Word Representation Learning using a Corpus and a Semantic LexiconCode0
Binding via Reconstruction ClusteringCode0
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial NetworksCode1
Multimodal sparse representation learning and applications—0
Learning Discriminative Representations for Semantic Cross Media Retrieval—0
Nonparametric Canonical Correlation Analysis—0
Learning Representations of Affect from Speech—0
Large-Scale Approximate Kernel Canonical Correlation Analysis—0
DISC: Deep Image Saliency Computing via Progressive Representation Learning—0
Learning Visual Features from Large Weakly Supervised Data—0
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization—0
Bridge Correlational Neural Networks for Multilingual Multimodal Representation LearningCode1
Stochastic Optimization for Deep CCA via Nonlinear Orthogonal Iterations—0
表示法學習技術於節錄式語音文件摘要之研究(A Study on Representation Learning Techniques for Extractive Spoken Document Summarization) [In Chinese]—0
High-order Graph-based Neural Dependency ParsingCode0
Deep Trans-layer Unsupervised Networks for Representation Learning—0
Bilingual Distributed Word Representations from Document-Aligned Comparable Data—0
A Neural Network Model for Low-Resource Universal Dependency Parsing—0
ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation—0
Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations—0
Word Representations, Tree Models and Syntactic FunctionsCode0
Scalable Out-of-Sample Extension of Graph Embeddings Using Deep Neural Networks—0
Online Representation Learning in Recurrent Neural Language Models—0
Type-Constrained Representation Learning in Knowledge Graphs—0
Dropout Training for SVMs with Data Augmentation—0
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text NetworksCode0
Recurrent Network Models for Human Dynamics—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