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 8051–8100 of 10580 papers

TitleStatusHype
Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots—0
Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning—0
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks—0
Molecular Representation Learning by Leveraging Chemical Information—0
XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition—0
Boosting ship detection in SAR images with complementary pretraining techniques—0
Radar Camera Fusion via Representation Learning in Autonomous Driving—0
Optimal Embedding Calibration for Symbolic Music Similarity—0
DeepGroup: Representation Learning for Group Recommendation with Implicit FeedbackCode0
Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds—0
BCFNet: A Balanced Collaborative Filtering Network with Attention MechanismCode0
RL-CSDia: Representation Learning of Computer Science Diagrams—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks—0
Disentangled Representation Learning for Astronomical Chemical TaggingCode0
Variable-rate discrete representation learning—0
u-cf2vec: Representation Learning for Personalized Algorithm Selection in Recommender Systems—0
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning—0
Contrastive Semi-supervised Learning for ASR—0
Wav2vec-C: A Self-supervised Model for Speech Representation Learning—0
Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning—0
Bio-Inspired Representation Learning for Visual Attention Prediction—0
Self-supervised representation learning on manifoldsCode0
Persistent Homology and Graphs Representation Learning—0
Nearest Neighbor Search Under Uncertainty—0
Simplicial Complex Representation Learning—0
Deeply Unsupervised Patch Re-Identification for Pre-training Object Detectors—0
Bootstrapped Representation Learning on Graphs—0
Network Representation Learning: From Traditional Feature Learning to Deep Learning—0
Multimodal VAE Active Inference ControllerCode0
Learning a State Representation and Navigation in Cluttered and Dynamic Environments—0
Bio-JOIE: Joint Representation Learning of Biological Knowledge BasesCode0
Fairness in TabNet Model by Disentangled Representation for the Prediction of Hospital No-Show—0
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations—0
Simplicial Complex Representation Learning—0
Unsupervised Motion Representation Enhanced Network for Action Recognition—0
Set Representation Learning with Generalized Sliced-Wasserstein Embeddings—0
IACN: Influence-aware and Attention-based Co-evolutionary Network for RecommendationCode0
Hybrid Mutual Information Lower-bound Estimators for Representation Learning—0
Successor Feature Sets: Generalizing Successor Representations Across Policies—0
Deep Clustering by Semantic Contrastive Learning—0
Generalizing to Unseen Domains: A Survey on Domain Generalization—0
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine LearningCode0
Semantic Data Set Construction from Human Clustering and Spatial Arrangement—0
Early-Bird GCNs: Graph-Network Co-Optimization Towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery TicketsCode0
Contrastive Separative Coding for Self-supervised Representation Learning—0
On the Fairness of Generative Adversarial Networks (GANs)—0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
A survey on Variational Autoencoders from a GreenAI perspectiveCode0
A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis—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