Cover Image

Reflective-Net: Learning from Explanations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Schneider, Johannes, Vlachos, Michalis
Format:	Preprint
Published:	2020
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Survey of Deep Learning: From Activations to Transformers
by: Schneider, Johannes, et al.
Published: (2023)

Mitigating Shortcut Learning with InterpoLated Learning
by: Korakakis, Michalis, et al.
Published: (2025)

ALVIN: Active Learning Via INterpolation
by: Korakakis, Michalis, et al.
Published: (2024)

Obtaining Example-Based Explanations from Deep Neural Networks
by: Dong, Genghua, et al.
Published: (2025)

LLM-Generated Explanations Do Not Suffice for Ultra-Strong Machine Learning
by: Ai, Lun, et al.
Published: (2025)

Focus on Likely Classes for Test-Time Prediction
by: Schneider, Johannes
Published: (2025)

Dropout MPC: An Ensemble Neural MPC Approach for Systems with Learned Dynamics
by: Syntakas, Spyridon, et al.
Published: (2024)

Creativity of Deep Learning: Conceptualization and Assessment
by: Basalla, Marcus, et al.
Published: (2020)

GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
by: Bresson, Roman, et al.
Published: (2026)

Uncovering the Structure of Explanation Quality with Spectral Analysis
by: Maeß, Johannes, et al.
Published: (2025)

Learning Concept Bottleneck Models from Mechanistic Explanations
by: De Santis, Antonio, et al.
Published: (2026)

Explaining Predictions by Characteristic Rules
by: Alkhatib, Amr, et al.
Published: (2024)

Counterfactual Explanations for Continuous Action Reinforcement Learning
by: Dong, Shuyang, et al.
Published: (2025)

Global Concept Explanations for Graphs by Contrastive Learning
by: Teufel, Jonas, et al.
Published: (2024)

Learning-Order Autoregressive Models with Application to Molecular Graph Generation
by: Wang, Zhe, et al.
Published: (2025)

Not All Explanations for Deep Learning Phenomena Are Equally Valuable
by: Jeffares, Alan, et al.
Published: (2025)

TREX: Trajectory Explanations for Multi-Objective Reinforcement Learning
by: Rajapakse, Dilina, et al.
Published: (2026)

Learning Quantifiable Visual Explanations Without Ground-Truth
by: Singh, Amritpal, et al.
Published: (2026)

Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
by: Wang, Rushan, et al.
Published: (2024)

Robust Counterfactual Explanations in Machine Learning: A Survey
by: Jiang, Junqi, et al.
Published: (2024)

Learning Model Agnostic Explanations via Constraint Programming
by: Koriche, Frederic, et al.
Published: (2024)

The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations
by: Christodoulou, Leonidas, et al.
Published: (2026)

An LLM Feature-based Framework for Dialogue Constructiveness Assessment
by: Zhou, Lexin, et al.
Published: (2024)

Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning
by: Zuo, Rui, et al.
Published: (2024)

Explanations are a Means to an End: Decision Theoretic Explanation Evaluation
by: Guo, Ziyang, et al.
Published: (2025)

TimeX++: Learning Time-Series Explanations with Information Bottleneck
by: Liu, Zichuan, et al.
Published: (2024)

Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?
by: Nielsen, Jacob, et al.
Published: (2025)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
by: Zhang, Jiaxing, et al.
Published: (2025)

Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks
by: Abbahaddou, Yassine, et al.
Published: (2024)

Interpretable Graph Neural Networks for Tabular Data
by: Alkhatib, Amr, et al.
Published: (2023)

Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks
by: Rigas, Spyros, et al.
Published: (2024)

The Role of Ambiguity in Error Prediction via Uncertainty Quantification
by: Staliūnaitė, Ieva Raminta, et al.
Published: (2026)

Delivering Inflated Explanations
by: Izza, Yacine, et al.
Published: (2023)

UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
by: Kang, Hyunju, et al.
Published: (2026)

Hyperparameter Optimisation with Practical Interpretability and Explanation Methods in Probabilistic Curriculum Learning
by: Salt, Llewyn, et al.
Published: (2025)

Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection
by: Chakraborty, Tanmay, et al.
Published: (2025)

Variational Learning for Insertion-based Generation
by: Zhang, Yangtian, et al.
Published: (2026)

Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models
by: Löfström, Tuwe, et al.
Published: (2024)

Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
by: Arya, Shivvrat, et al.
Published: (2024)

Sanity Checks for Explanation Uncertainty
by: Valdenegro-Toro, Matias, et al.
Published: (2024)