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 9351–9400 of 10580 papers

TitleStatusHype
Improving Unsupervised Subword Modeling via Disentangled Speech Representation Learning and Transformation—0
Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling—0
Anomaly Detection with Joint Representation Learning of Content and Connection—0
PredNet and Predictive Coding: A Critical ReviewCode0
Learning Video Representations using Contrastive Bidirectional Transformer—0
Learning Spatio-Temporal Representation with Local and Global DiffusionCode0
Identifying Illicit Accounts in Large Scale E-payment Networks -- A Graph Representation Learning Approach—0
Real-time Attention Based Look-alike Model for Recommender SystemCode0
Representation Learning for Words and Entities—0
Multi-local Collaborative AutoEncoder—0
Warping Resilient Scalable Anomaly Detection in Time Series—0
Explicit Disentanglement of Appearance and Perspective in Generative ModelsCode0
Representation Learning-Assisted Click-Through Rate PredictionCode0
Relationship-Embedded Representation Learning for Grounding Referring ExpressionsCode1
Probabilistic Forecasting with Temporal Convolutional Neural NetworkCode3
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional TransformerCode1
Deep Learning for Spatio-Temporal Data Mining: A Survey—0
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer—0
Deep Visual Re-Identification with ConfidenceCode0
Autonomous Goal Exploration using Learned Goal Spaces for Visuomotor Skill Acquisition in Robots—0
A Survey of Reinforcement Learning Informed by Natural Language—0
Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D MappingCode0
Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text ClassificationCode0
Dynamic Network Embedding via Incremental Skip-gram with Negative SamplingCode0
Strategies to architect AI Safety: Defense to guard AI from Adversaries—0
Sentence Centrality Revisited for Unsupervised SummarizationCode0
Unsupervised Representation Learning of DNA Sequences—0
Shared-Private Bilingual Word Embeddings for Neural Machine Translation—0
Extracting Visual Knowledge from the Internet: Making Sense of Image Data—0
Evolving Losses for Unlabeled Video Representation Learning—0
Data-to-text Generation with Entity ModelingCode0
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement DatasetCode0
Knowledge-Aware Deep Dual Networks for Text-Based Mortality Prediction—0
DeepMDP: Learning Continuous Latent Space Models for Representation Learning—0
Cross-Modal Interaction Networks for Query-Based Moment Retrieval in VideosCode0
Quaternion Collaborative Filtering for Recommendation—0
Flexibly Fair Representation Learning by Disentanglement—0
Efficient Codebook and Factorization for Second Order Representation Learning—0
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph ClassificationCode0
Pykg2vec: A Python Library for Knowledge Graph Embedding—0
KERMIT: Generative Insertion-Based Modeling for Sequences—0
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies—0
Learning Representations by Maximizing Mutual Information Across ViewsCode0
Controllable Paraphrase Generation with a Syntactic Exemplar—0
Pretraining Methods for Dialog Context Representation Learning—0
Pre-training of Graph Augmented Transformers for Medication RecommendationCode0
Bayesian Learning of Latent Representations of Language Structures—0
Self-Discriminative Learning for Unsupervised Document Embedding—0
Document-Level N-ary Relation Extraction with Multiscale Representation Learning—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—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