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 6951–7000 of 10580 papers

TitleStatusHype
On Robustness in Multimodal Learning—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
On Statistical Estimation of Edge-Reinforced Random Walks—0
On the Behavior of Convolutional Nets for Feature Extraction—0
Hybrid Gradient Boosting Trees and Neural Networks for Forecasting Operating Room Data—0
Bitcoin Transaction Forecasting with Deep Network Representation Learning—0
10 Years of Fair Representations: Challenges and Opportunities—0
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners—0
On the Complexity of Representation Learning in Contextual Linear Bandits—0
Hybrid deep learning methods for phenotype prediction from clinical notes—0
On The Concurrence of Layer-wise Preconditioning Methods and Provable Feature Learning—0
Deep Inverse Feature Learning: A Representation Learning of Error—0
Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification—0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning—0
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications—0
On The Effects of Learning Views on Neural Representations in Self-Supervised Learning—0
Hybrid Active Inference—0
Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs—0
Advancing Volumetric Medical Image Segmentation via Global-Local Masked Autoencoder—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
Persistent Homology and Graphs Representation Learning—0
On the Generalization of Learned Structured Representations—0
Human Semantic Parsing for Person Re-identification—0
On the Identifiability of Causal Abstractions—0
On the Identification of Temporally Causal Representation with Instantaneous Dependence—0
Human-oriented Representation Learning for Robotic Manipulation—0
Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks—0
On the Importance of Distraction-Robust Representations for Robot Learning—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
An Identity-Preserved Framework for Human Motion Transfer—0
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations—0
On the Importance of Looking at the Manifold—0
Human Gaze Boosts Object-Centered Representation Learning—0
On the Importance of Text Preprocessing for Multimodal Representation Learning and Pathology Report Generation—0
HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning—0
Deep Hyperspherical Learning—0
Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training—0
On the Informativeness of Supervision Signals—0
Experiments on Turkish ASR with Self-Supervised Speech Representation Learning—0
BiRRE: Learning Bidirectional Residual Relation Embeddings for Supervised Hypernymy Detection—0
On the interventional consistency of autoencoders—0
On the Latent Space of Wasserstein Auto-Encoders—0
A Complex-valued SAR Foundation Model Based on Physically Inspired Representation Learning—0
On the Mechanisms of Adversarial Data Augmentation for Robust and Adaptive Transfer Learning—0
Personalizing Pre-trained Models—0
On The Performance of Time-Pooling Strategies for End-to-End Spoken Language Identification—0
HuBERTopic: Enhancing Semantic Representation of HuBERT through Self-supervision Utilizing Topic Model—0
Deep Hypergraph Structure Learning—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