CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures
Fuente:
arXiv
Saved in:
| Main Authors: | Gherardini, Luca, Varma, Varun Ravi, Capala, Karol, Woods, Roger, Sousa, Jose |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Improving Noise Robustness through Abstractions and its Impact on Machine Learning
by: Ibias, Alfredo, et al.
Published: (2024)
by: Ibias, Alfredo, et al.
Published: (2024)
Preservation of Feature Stability in Machine Learning Under Data Uncertainty for Decision Support in Critical Domains
by: Capała, Karol, et al.
Published: (2024)
by: Capała, Karol, et al.
Published: (2024)
CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration
by: Gherardini, Luca, et al.
Published: (2025)
by: Gherardini, Luca, et al.
Published: (2025)
CACTUS: Chemistry Agent Connecting Tool-Usage to Science
by: McNaughton, Andrew D., et al.
Published: (2024)
by: McNaughton, Andrew D., et al.
Published: (2024)
A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data
by: Andrys-Olek, Justyna, et al.
Published: (2026)
by: Andrys-Olek, Justyna, et al.
Published: (2026)
Realizable Abstractions: Near-Optimal Hierarchical Reinforcement Learning
by: Cipollone, Roberto, et al.
Published: (2025)
by: Cipollone, Roberto, et al.
Published: (2025)
On the Structural Limitations of Weight-Based Neural Adaptation and the Role of Reversible Behavioral Learning
by: Konduru, Pardhu Sri Rushi Varma
Published: (2026)
by: Konduru, Pardhu Sri Rushi Varma
Published: (2026)
Uncovering the Structure of Explanation Quality with Spectral Analysis
by: Maeß, Johannes, et al.
Published: (2025)
by: Maeß, Johannes, et al.
Published: (2025)
Learning Causal Abstractions of Linear Structural Causal Models
by: Massidda, Riccardo, et al.
Published: (2024)
by: Massidda, Riccardo, et al.
Published: (2024)
Neural Causal Abstractions
by: Xia, Kevin, et al.
Published: (2024)
by: Xia, Kevin, et al.
Published: (2024)
Distributionally Robust Causal Abstractions
by: Felekis, Yorgos, et al.
Published: (2025)
by: Felekis, Yorgos, et al.
Published: (2025)
Contrastive Abstraction for Reinforcement Learning
by: Patil, Vihang, et al.
Published: (2024)
by: Patil, Vihang, et al.
Published: (2024)
In-Context Planning with Latent Temporal Abstractions
by: Luo, Baiting, et al.
Published: (2026)
by: Luo, Baiting, et al.
Published: (2026)
Interval Abstractions for Robust Counterfactual Explanations
by: Jiang, Junqi, et al.
Published: (2024)
by: Jiang, Junqi, et al.
Published: (2024)
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
by: Bley, Florian, et al.
Published: (2025)
by: Bley, Florian, et al.
Published: (2025)
Abstraction for Offline Goal-Conditioned Reinforcement Learning
by: Wibault, Clarisse, et al.
Published: (2026)
by: Wibault, Clarisse, et al.
Published: (2026)
Vector Symbolic Algebras for the Abstraction and Reasoning Corpus
by: Joffe, Isaac, et al.
Published: (2025)
by: Joffe, Isaac, et al.
Published: (2025)
seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness
by: Ning, Yilin, et al.
Published: (2025)
by: Ning, Yilin, et al.
Published: (2025)
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
by: Panangaden, Prakash, et al.
Published: (2023)
by: Panangaden, Prakash, et al.
Published: (2023)
Context-Sensitive Abstractions for Reinforcement Learning with Parameterized Actions
by: Nayyar, Rashmeet Kaur, et al.
Published: (2025)
by: Nayyar, Rashmeet Kaur, et al.
Published: (2025)
Causal Abstraction Learning based on the Semantic Embedding Principle
by: D'Acunto, Gabriele, et al.
Published: (2025)
by: D'Acunto, Gabriele, et al.
Published: (2025)
A Modular Dataset to Demonstrate LLM Abstraction Capability
by: Atanas, Adam, et al.
Published: (2025)
by: Atanas, Adam, et al.
Published: (2025)
Learning Markov State Abstractions for Deep Reinforcement Learning
by: Allen, Cameron, et al.
Published: (2021)
by: Allen, Cameron, et al.
Published: (2021)
Understanding the Countably Infinite: Neural Network Models of the Successor Function and its Acquisition
by: Gupta, Vima, et al.
Published: (2023)
by: Gupta, Vima, et al.
Published: (2023)
Preference-Conditioned Language-Guided Abstraction
by: Peng, Andi, et al.
Published: (2024)
by: Peng, Andi, et al.
Published: (2024)
Learning with Language-Guided State Abstractions
by: Peng, Andi, et al.
Published: (2024)
by: Peng, Andi, et al.
Published: (2024)
Decoupled Hierarchical Reinforcement Learning with State Abstraction for Discrete Grids
by: Xiao, Qingyu, et al.
Published: (2025)
by: Xiao, Qingyu, et al.
Published: (2025)
Addressing the Abstraction and Reasoning Corpus via Procedural Example Generation
by: Hodel, Michael
Published: (2024)
by: Hodel, Michael
Published: (2024)
Combining Causal Models for More Accurate Abstractions of Neural Networks
by: Pîslar, Theodora-Mara, et al.
Published: (2025)
by: Pîslar, Theodora-Mara, et al.
Published: (2025)
Learning with Expert Abstractions for Efficient Multi-Task Continuous Control
by: Jewett, Jeff, et al.
Published: (2025)
by: Jewett, Jeff, et al.
Published: (2025)
ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learning
by: Lee, Hosung, et al.
Published: (2024)
by: Lee, Hosung, et al.
Published: (2024)
A Relational Inductive Bias for Dimensional Abstraction in Neural Networks
by: Campbell, Declan, et al.
Published: (2024)
by: Campbell, Declan, et al.
Published: (2024)
Reconciling Spatial and Temporal Abstractions for Goal Representation
by: Zadem, Mehdi, et al.
Published: (2024)
by: Zadem, Mehdi, et al.
Published: (2024)
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
by: Zhao, Mingde, et al.
Published: (2023)
by: Zhao, Mingde, et al.
Published: (2023)
Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction
by: GX-Chen, Anthony, et al.
Published: (2024)
by: GX-Chen, Anthony, et al.
Published: (2024)
Precise Verification of Transformers through ReLU-Catalyzed Abstraction Refinement
by: Liu, Hengjie, et al.
Published: (2026)
by: Liu, Hengjie, et al.
Published: (2026)
Self-Abstraction Learning for Effective and Stable Training of Deep Neural Networks
by: Cho, Wonyong, et al.
Published: (2026)
by: Cho, Wonyong, et al.
Published: (2026)
Learning World Models With Hierarchical Temporal Abstractions: A Probabilistic Perspective
by: Shaj, Vaisakh
Published: (2024)
by: Shaj, Vaisakh
Published: (2024)
Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning
by: He, Jinmin, et al.
Published: (2025)
by: He, Jinmin, et al.
Published: (2025)
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning
by: Canonaco, Giuseppe, et al.
Published: (2024)
by: Canonaco, Giuseppe, et al.
Published: (2024)
Similar Items
-
Improving Noise Robustness through Abstractions and its Impact on Machine Learning
by: Ibias, Alfredo, et al.
Published: (2024) -
Preservation of Feature Stability in Machine Learning Under Data Uncertainty for Decision Support in Critical Domains
by: Capała, Karol, et al.
Published: (2024) -
CACTUS as a Reliable Tool for Early Classification of Age-related Macular Degeneration
by: Gherardini, Luca, et al.
Published: (2025) -
CACTUS: Chemistry Agent Connecting Tool-Usage to Science
by: McNaughton, Andrew D., et al.
Published: (2024) -
A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data
by: Andrys-Olek, Justyna, et al.
Published: (2026)