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 6651–6700 of 10580 papers

TitleStatusHype
Chemical Property Prediction Under Experimental Biases—0
Learning Task-Relevant Features via Contrastive Input Morphing—0
Learning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors—0
Learning Target-oriented Dual Attention for Robust RGB-T Tracking—0
Learning Target-aware Representation for Visual Tracking via Informative Interactions—0
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data—0
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases—0
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations—0
Learning Successor Features with Distributed Hebbian Temporal Memory—0
Learning Subgoal Representations with Slow Dynamics—0
Do Trajectories Encode Verb Meaning?—0
Neocortical plasticity: an unsupervised cake but no free lunch—0
Learning Structure from the Ground up---Hierarchical Representation Learning by Chunking—0
Learning Structured Representations of Visual Scenes—0
Learning Structure and Knowledge Aware Representation with Large Language Models for Concept Recommendation—0
DORSal: Diffusion for Object-centric Representations of Scenes et al—0
ChebMixer: Efficient Graph Representation Learning with MLP Mixer—0
A Self-enhancement Multitask Framework for Unsupervised Aspect Category Detection—0
Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception—0
Network representation learning: A macro and micro view—0
Learning strange attractors with reservoir systems—0
Joint Debiased Representation Learning and Imbalanced Data Clustering—0
Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength—0
ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?—0
Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning—0
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding—0
Network representation learning systematic review: ancestors and current development state—0
Learning State Representations in Complex Systems with Multimodal Data—0
How Does Naming Affect LLMs on Code Analysis Tasks?—0
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences—0
Learning spatio-temporal representations with temporal squeeze pooling—0
Learning Spatiotemporal-Aware Representation for POI Recommendation—0
Learning Spatial Common Sense with Geometry-Aware Recurrent Networks—0
Neural Attentive Multiview Machines—0
Neural-based Context Representation Learning for Dialog Act Classification—0
Learning Sparse Representations in Reinforcement Learning with Sparse Coding—0
Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex—0
Neural Belief Tracker: Data-Driven Dialogue State Tracking—0
Chat Discrimination for Intelligent Conversational Agents with a Hybrid CNN-LMTGRU Network—0
ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency—0
Affinity-VAE: incorporating prior knowledge in representation learning from scientific images—0
Active Discriminative Text Representation Learning—0
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning—0
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck—0
Learning sound representations using trainable COPE feature extractors—0
Domain Representation for Knowledge Graph Embedding—0
Learning Solving Procedure for Artificial Neural Network—0
Learning Smooth and Fair Representations—0
Towards Unsupervised Domain Generalization—0
Learning Shape Representations for Clothing Variations in Person Re-Identification—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