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 1051–1100 of 10580 papers

TitleStatusHype
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds—0
Learning Global Object-Centric Representations via Disentangled Slot Attention—0
TopoQA: a topological deep learning-based approach for protein complex structure interface quality assessment—0
Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive LearningCode0
Towards Active Participant-Centric Vertical Federated Learning: Some Representations May Be All You Need—0
Rethinking Positive Pairs in Contrastive Learning—0
Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder for Model Order Reduction—0
ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language TuningCode1
IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age TransformationCode0
Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive LossCode3
TIPS: Text-Image Pretraining with Spatial AwarenessCode2
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
Visual Representation Learning Guided By Multi-modal Prior Knowledge—0
A Two-Stage Learning-to-Defer Approach for Multi-Task Learning—0
Learning from Neighbors: Category Extrapolation for Long-Tail Learning—0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification—0
Exploring Stronger Transformer Representation Learning for Occluded Person Re-Identification—0
MI-VisionShot: Few-shot adaptation of vision-language models for slide-level classification of histopathological imagesCode0
log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction ModelingCode0
Structural Causality-based Generalizable Concept Discovery Models—0
Dynamic Contrastive Learning for Time Series RepresentationCode0
TAGExplainer: Narrating Graph Explanations for Text-Attributed Graph Learning Models—0
BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation—0
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning—0
G-NeuroDAVIS: A Neural Network model for generalized embedding, data visualization and sample generationCode0
DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in Conversational SearchCode0
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial ScenariosCode2
Self-supervised contrastive learning performs non-linear system identificationCode1
Learning Metadata-Agnostic Representations for Text-to-SQL In-Context Example Selection—0
Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement LearningCode1
Normalizing self-supervised learning for provably reliable Change Point Detection—0
Representation Learning of Structured Data for Medical Foundation Models—0
EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation LearningCode1
SemSim: Revisiting Weak-to-Strong Consistency from a Semantic Similarity Perspective for Semi-supervised Medical Image Segmentation—0
Context-Enhanced Multi-View Trajectory Representation Learning: Bridging the Gap through Self-Supervised Models—0
GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation—0
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems—0
Mitigating Dual Latent Confounding Biases in Recommender Systems—0
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs—0
Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series—0
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation LearningCode2
Just-In-Time Software Defect Prediction via Bi-modal Change Representation LearningCode0
SOE: SO(3)-Equivariant 3D MRI EncodingCode0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning—0
Guarantees for Nonlinear Representation Learning: Non-identical Covariates, Dependent Data, Fewer Samples—0
Multiview Scene GraphCode2
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model DisentanglementCode2
Network Representation Learning for Biophysical Neural Network Analysis—0
Towards Fair Graph Representation Learning in Social 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