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 4051–4100 of 10580 papers

TitleStatusHype
ComFace: Facial Representation Learning with Synthetic Data for Comparing Faces—0
RoboArm-NMP: a Learning Environment for Neural Motion Planning—0
BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection—0
Brain3D: Generating 3D Objects from fMRICode0
On the Identification of Temporally Causal Representation with Instantaneous Dependence—0
Modally Reduced Representation Learning of Multi-Lead ECG Signals through Simultaneous Alignment and Reconstruction—0
ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning—0
ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow NetworksCode0
Disease-informed Adaptation of Vision-Language ModelsCode0
Graphlets correct for the topological information missed by random walks—0
Learning Geospatial Region Embedding with Heterogeneous Graph—0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval—0
Towards Cross-modal Backward-compatible Representation Learning for Vision-Language Models—0
Nuclear Norm Regularization for Deep Learning—0
Marrying Causal Representation Learning with Dynamical Systems for ScienceCode0
AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning—0
Maximum Manifold Capacity Representations in State Representation Learning—0
RemoCap: Disentangled Representation Learning for Motion Capture—0
Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation—0
BIMM: Brain Inspired Masked Modeling for Video Representation LearningCode0
Vertical Federated Learning Hybrid Local Pre-training—0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning—0
Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label SemanticCode0
Action Controlled ParaphrasingCode0
The Power of Active Multi-Task Learning in Reinforcement Learning from Human Feedback—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings—0
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations—0
Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance—0
UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning—0
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning—0
Motion Keyframe Interpolation for Any Human Skeleton via Temporally Consistent Point Cloud Sampling and Reconstruction—0
FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival—0
Towards Subgraph Isomorphism Counting with Graph Kernels—0
Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning—0
CTRL: Continuous-Time Representation Learning on Temporal Heterogeneous Information Network—0
Fair Graph Representation Learning via Sensitive Attribute DisentanglementCode0
Non-stationary Domain Generalization: Theory and Algorithm—0
MaskMatch: Boosting Semi-Supervised Learning Through Mask Autoencoder-Driven Feature Learning—0
Deep video representation learning: a survey—0
Learned feature representations are biased by complexity, learning order, position, and more—0
TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction—0
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences—0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
The Curse of Diversity in Ensemble-Based ExplorationCode0
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships—0
Representation Learning of Daily Movement Data Using Text EncodersCode0
WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancerCode0
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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