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 5201–5250 of 10580 papers

TitleStatusHype
Addressing Feature Suppression in Unsupervised Visual Representations—0
Information Potential Auto-Encoders—0
Entropy Minimization In Emergent Languages—0
LARGE SCALE REPRESENTATION LEARNING FROM TRIPLET COMPARISONS—0
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation—0
Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report—0
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning—0
Large-Scale Unsupervised Deep Representation Learning for Brain Structure—0
Bridging CNNs, RNNs, and Weighted Finite-State Machines—0
Large Scale Video Representation Learning via Relational Graph Clustering—0
Large Scale Visual Food Recognition—0
Large Sequence Representation Learning via Multi-Stage Latent Transformers—0
Disentangled Representation Learning—0
An Improved Semi-Supervised VAE for Learning Disentangled Representations—0
Learning to Disentangle Interleaved Conversational Threads with a Siamese Hierarchical Network and Similarity Ranking—0
Denoising Autoregressive Representation Learning—0
Last layer state space model for representation learning and uncertainty quantification—0
Late Meta-learning Fusion Using Representation Learning for Time Series Forecasting—0
Information Bottleneck Inspired Method For Chat Text Segmentation—0
Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding—0
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities—0
Disentangled Representation Learning for Unsupervised Neural Quantization—0
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning—0
Information-based Disentangled Representation Learning for Unsupervised MR Harmonization—0
Disentangled Representation Learning for Controllable Person Image Generation—0
LatentPINNs: Generative physics-informed neural networks via a latent representation learning—0
Latent Representation Learning for Multimodal Brain Activity Translation—0
Disentangled Representation Learning for Parametric Partial Differential Equations—0
LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping—0
Latent Space Characterization of Autoencoder Variants—0
On the Implicit Bias Towards Minimal Depth of Deep Neural Networks—0
Learning to Distill: The Essence Vector Modeling Framework—0
Information-Aware Time Series Meta-Contrastive Learning—0
InfoGCL: Information-Aware Graph Contrastive Learning—0
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models—0
Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization—0
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression—0
LaT: Latent Translation with Cycle-Consistency for Video-Text Retrieval—0
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases—0
Lattice Representation Learning—0
Lattice Representation Learning—0
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—0
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning—0
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models—0
Layer-stacked Attention for Heterogeneous Network Embedding—0
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation—0
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs—0
Adversarial Domain Adaptation for Machine Reading Comprehension—0
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
Bridge: A Unified Framework to Knowledge Graph Completion via Language Models and Knowledge Representation—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