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 8101–8150 of 10580 papers

TitleStatusHype
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models—0
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information BottleneckCode0
Consistent Assignment for Representation Learning—0
Persistent Homology and Graphs Representation Learning—0
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification—0
Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data—0
Nonlinear Invariant Risk Minimization: A Causal Approach—0
Theoretical Understandings of Product Embedding for E-commerce Machine Learning—0
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates—0
Return-Based Contrastive Representation Learning for Reinforcement Learning—0
Learning Low-dimensional Manifolds for Scoring of Tissue Microarray Images—0
Towards Causal Representation Learning—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
Self-Supervised Learning via multi-Transformation Classification for Action Recognition—0
Contrastive Pre-training for Imbalanced Corporate Credit Ratings—0
Composable Generative Models—0
Switch Spaces: Learning Product Spaces with Sparse Gating—0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks—0
TCN: Table Convolutional Network for Web Table InterpretationCode0
Training Stacked Denoising Autoencoders for Representation Learning—0
Meta-Path-Free Representation Learning on Heterogeneous Networks—0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents—0
Boosting Deep Transfer Learning for COVID-19 Classification—0
Training Larger Networks for Deep Reinforcement Learning—0
DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning—0
Information flows of diverse autoencodersCode0
A Hidden Challenge of Link Prediction: Which Pairs to Check?Code0
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
Model-free Representation Learning and Exploration in Low-rank MDPs—0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
Understanding Negative Samples in Instance Discriminative Self-supervised Representation LearningCode0
Contrastive Unsupervised Learning for Speech Emotion Recognition—0
SceneRec: Scene-Based Graph Neural Networks for Recommender Systems—0
Quadric Hypersurface Intersection for Manifold Learning in Feature SpaceCode0
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States—0
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation—0
Privacy-Preserving Graph Convolutional Networks for Text ClassificationCode0
Searching for Alignment in Face Recognition—0
Spherical Message Passing for 3D Graph Networks—0
COLOGNE: Coordinated Local Graph Neighborhood SamplingCode0
Learning State Representations from Random Deep Action-conditional PredictionsCode0
Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning—0
A Provably Convergent Information Bottleneck Solution via ADMMCode0
Benchmarks, Algorithms, and Metrics for Hierarchical DisentanglementCode0
Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal InferenceCode0
Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds—0
Near-optimal Representation Learning for Linear Bandits and Linear RL—0
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable LearningCode0
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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