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 8451–8500 of 10580 papers

TitleStatusHype
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments—0
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction—0
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis—0
Embedding Meta-Textual Information for Improved Learning to Rank—0
Measure Inducing Classification and Regression Trees for Functional Data—0
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation—0
AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation LearningCode0
WaveTransform: Crafting Adversarial Examples via Input Decomposition—0
Cycle-Contrast for Self-Supervised Video Representation Learning—0
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG—0
Geometric Scattering Attention NetworksCode0
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment—0
Predicting intubation support requirement of patients using Chest X-ray with Deep Representation LearningCode0
PPG-based singing voice conversion with adversarial representation learning—0
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning—0
On the Transfer of Disentangled Representations in Realistic Settings—0
Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and TagsCode0
Activation Map Adaptation for Effective Knowledge Distillation—0
CLRGaze: Contrastive Learning of Representations for Eye Movement SignalsCode0
XLVIN: eXecuted Latent Value Iteration Nets—0
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling—0
Pairwise Representation Learning for Event Coreference—0
Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference—0
A Comparison of Discrete Latent Variable Models for Speech Representation Learning—0
Counterfactual Representation Learning with Balancing Weights—0
Local dendritic balance enables learning of efficient representations in networks of spiking neurons—0
Representation Learning for High-Dimensional Data Collection under Local Differential Privacy—0
DICT-MLM: Improved Multilingual Pre-Training using Bilingual Dictionaries—0
Similarity Analysis of Self-Supervised Speech Representations—0
Momentum Contrast Speaker Representation Learning—0
Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations—0
Learning Speaker Embedding from Text-to-SpeechCode0
Semantics-Guided Representation Learning with Applications to Visual Synthesis—0
Differentiable Optimal Adversaries for Learning Fair Representations—0
Predicting Chemical Properties using Self-Attention Multi-task Learning based on SMILES RepresentationCode0
Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering—0
Less can be more in contrastive learning—0
Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and BeyondCode0
Graphite: GRAPH-Induced feaTure Extraction for Point Cloud RegistrationCode0
Federated Unsupervised Representation Learning—0
Meta-path Free Semi-supervised Learning for Heterogeneous Networks—0
Variational Capsule Encoder—0
Cross-Lingual Relation Extraction with Transformers—0
For self-supervised learning, Rationality implies generalization, provablyCode0
Distributed Representations of Entities in Open-World Knowledge Graphs—0
Fully Unsupervised Person Re-identification viaSelective Contrastive Learning—0
Double Robust Representation Learning for Counterfactual PredictionCode0
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks—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