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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3401–3450 of 10580 papers

TitleStatusHype
Exploring wav2vec 2.0 on speaker verification and language identification—0
Exponential Family Graph Embeddings—0
End-to-End Efficient Representation Learning via Cascading Combinatorial Optimization—0
End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection—0
Combining Representation Learning with Tensor Factorization for Risk Factor Analysis - an application to Epilepsy and Alzheimer's disease—0
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review—0
End-to-end Binary Representation Learning via Direct Binary Embedding—0
Combining Representation Learning with Logic for Language Processing—0
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection—0
Extending Multilingual Speech Synthesis to 100+ Languages without Transcribed Data—0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining—0
Contextual Gradient Flow Modeling for Large Language Model Generalization in Multi-Scale Feature Spaces—0
Extracting Visual Knowledge from the Internet: Making Sense of Image Data—0
Graph Contrastive Learning with Generative Adversarial Network—0
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization—0
Combining graph and sequence information to learn protein representations—0
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning—0
F4D: Factorized 4D Convolutional Neural Network for Efficient Video-level Representation Learning—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
Face Anti-Spoofing Via Disentangled Representation Learning—0
Combining expert knowledge and neural networks to model environmental stresses in agriculture—0
Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring—0
A Survey on Temporal Knowledge Graph: Representation Learning and Applications—0
GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction—0
Facial Expression Representation Learning by Synthesizing Expression Images—0
Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling—0
Factor Graph Neural Networks—0
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning—0
Empowering Vision Transformers with Multi-Scale Causal Intervention for Long-Tailed Image Classification—0
Adaptation of Autoencoder for Sparsity Reduction From Clinical Notes Representation Learning—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Factorized linear discriminant analysis for phenotype-guided representation learning of neuronal gene expression data—0
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems—0
Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings—0
Factors of Transferability for a Generic ConvNet Representation—0
Contextures: The Mechanism of Representation Learning—0
Empowering Next POI Recommendation with Multi-Relational Modeling—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs—0
A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities—0
Continual Causal Inference with Incremental Observational Data—0
Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines—0
Fair Inference for Discrete Latent Variable Models—0
Fair Interpretable Learning via Correction Vectors—0
Fair Interpretable Representation Learning with Correction Vectors—0
Co-manifold learning with missing data—0
FairMixRep : Self-supervised Robust Representation Learning for Heterogeneous Data with Fairness constraints—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey—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