Image de couverture de livre

What is the relation between Slow Feature Analysis and the Successor Representation?

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Seabrook, Eddie, Wiskott, Laurenz
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Slow Feature Analysis on Markov Chains from Goal-Directed Behavior
par: Schüler, Merlin, et autres
Publié: (2025)

Slow Feature Analysis as Variational Inference Objective
par: Schüler, Merlin, et autres
Publié: (2025)

Classification and Reconstruction Processes in Deep Predictive Coding Networks: Antagonists or Allies?
par: Rathjens, Jan, et autres
Publié: (2024)

Is Hierarchical Quantization Essential for Optimal Reconstruction?
par: Reyhanian, Shirin, et autres
Publié: (2026)

The Course Difficulty Analysis Cookbook
par: Baucks, Frederik, et autres
Publié: (2025)

Balancing Plasticity and Stability with Fast and Slow Successor Features
par: Chua, Raymond, et autres
Publié: (2026)

Exploring the limits of Hierarchical World Models in Reinforcement Learning
par: Schiewer, Robin, et autres
Publié: (2024)

Full-Gradient Successor Feature Representations
par: Shrirao, Ritish, et autres
Publié: (2026)

Interpretable Brain-Inspired Representations Improve RL Performance on Visual Navigation Tasks
par: Lange, Moritz, et autres
Publié: (2024)

Effects of Distributional Biases on Gradient-Based Causal Discovery in the Bivariate Categorical Case
par: Schwabe, Tim, et autres
Publié: (2025)

Probing Length Generalization in Mamba via Image Reconstruction
par: Rathjens, Jan, et autres
Publié: (2026)

$\pi2\text{vec}$: Policy Representations with Successor Features
par: Scarpellini, Gianluca, et autres
Publié: (2023)

Which Features are Best for Successor Features?
par: Ollivier, Yann
Publié: (2025)

Learning Successor Features the Simple Way
par: Chua, Raymond, et autres
Publié: (2024)

Hierarchical Successor Representation for Robust Transfer
par: Yu, Changmin, et autres
Publié: (2026)

ProtoP-OD: Explainable Object Detection with Prototypical Parts
par: Rath-Manakidis, Pavlos, et autres
Publié: (2024)

Understanding Transformer-based Vision Models through Inversion
par: Rathjens, Jan, et autres
Publié: (2024)

A Distributional Analogue to the Successor Representation
par: Wiltzer, Harley, et autres
Publié: (2024)

Temporal Representation Alignment: Successor Features Enable Emergent Compositionality in Robot Instruction Following
par: Myers, Vivek, et autres
Publié: (2025)

Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison
par: Lange, Moritz, et autres
Publié: (2023)

Object-centric Denoising Diffusion Models for Physical Reasoning
par: Lange, Moritz, et autres
Publié: (2025)

Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning
par: Baucks, Frederik, et autres
Publié: (2024)

Distributional Successor Features Enable Zero-Shot Policy Optimization
par: Zhu, Chuning, et autres
Publié: (2024)

Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task
par: Engelhardt, Raphael C., et autres
Publié: (2024)

Learning Model Successors
par: Chang, Yingshan, et autres
Publié: (2025)

Decoupling Exploration and Exploitation for Unsupervised Pre-training with Successor Features
par: Kim, JaeYoon, et autres
Publié: (2024)

Stacked Universal Successor Feature Approximators for Safety in Reinforcement Learning
par: Cannon, Ian, et autres
Publié: (2024)

Hierarchical Active Inference using Successor Representations
par: Rangarajan, Prashant, et autres
Publié: (2026)

Successor-Predecessor Intrinsic Exploration
par: Yu, Changmin, et autres
Publié: (2023)

Learning Successor Features with Distributed Hebbian Temporal Memory
par: Dzhivelikian, Evgenii, et autres
Publié: (2023)

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
par: Jain, Arnav Kumar, et autres
Publié: (2024)

SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning
par: Zhang, Shuai, et autres
Publié: (2024)

Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning
par: Wang, Changhong, et autres
Publié: (2024)

Slowing Learning by Erasing Simple Features
par: Quirke, Lucia, et autres
Publié: (2025)

CID: Measuring Feature Importance Through Counterfactual Distributions
par: Conti, Eddie, et autres
Publié: (2025)

Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making
par: Myers, Vivek, et autres
Publié: (2024)

Switching Successor Measures for Hierarchical Zero-shot Reinforcement Learning
par: Stojanovic, Stefan, et autres
Publié: (2026)

Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
par: Grillotti, Luca, et autres
Publié: (2024)

Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations
par: Stoewer, Paul, et autres
Publié: (2023)

PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting
par: Jia, Yuxin, et autres
Publié: (2024)