Learning the greatest common divisor: explaining transformer predictions
Fuente:
arXiv
Saved in:
| Main Author: | Charton, François |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Int2Int: a framework for mathematics with transformers
by: Charton, François
Published: (2025)
by: Charton, François
Published: (2025)
Emergent properties with repeated examples
by: Charton, François, et al.
Published: (2024)
by: Charton, François, et al.
Published: (2024)
Instruction Diversity Drives Generalization To Unseen Tasks
by: Zhang, Dylan, et al.
Published: (2024)
by: Zhang, Dylan, et al.
Published: (2024)
Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
by: Feng, Yunzhen, et al.
Published: (2024)
by: Feng, Yunzhen, et al.
Published: (2024)
$\textbf{Only-IF}$:Revealing the Decisive Effect of Instruction Diversity on Generalization
by: Zhang, Dylan, et al.
Published: (2024)
by: Zhang, Dylan, et al.
Published: (2024)
An explainable transformer circuit for compositional generalization
by: Tang, Cheng, et al.
Published: (2025)
by: Tang, Cheng, et al.
Published: (2025)
A Tale of Tails: Model Collapse as a Change of Scaling Laws
by: Dohmatob, Elvis, et al.
Published: (2024)
by: Dohmatob, Elvis, et al.
Published: (2024)
From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers
by: Zhang, Dylan, et al.
Published: (2024)
by: Zhang, Dylan, et al.
Published: (2024)
Iteration Head: A Mechanistic Study of Chain-of-Thought
by: Cabannes, Vivien, et al.
Published: (2024)
by: Cabannes, Vivien, et al.
Published: (2024)
Static and multivariate-temporal attentive fusion transformer for readmission risk prediction
by: Sun, Zhe, et al.
Published: (2024)
by: Sun, Zhe, et al.
Published: (2024)
Counterfactual explainability and analysis of variance
by: Gao, Zijun, et al.
Published: (2024)
by: Gao, Zijun, et al.
Published: (2024)
Collapsing ROC approach for risk prediction research on both common and rare variants
by: Wei, Changshuai, et al.
Published: (2025)
by: Wei, Changshuai, et al.
Published: (2025)
Pretrained battery transformer (PBT): A foundation model for universal battery life prediction
by: Tan, Ruifeng, et al.
Published: (2025)
by: Tan, Ruifeng, et al.
Published: (2025)
Automatic generation of insights from workers' actions in industrial workflows with explainable Machine Learning
by: de Arriba-Pérez, Francisco, et al.
Published: (2024)
by: de Arriba-Pérez, Francisco, et al.
Published: (2024)
Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability
by: Aslami, Pooja, et al.
Published: (2024)
by: Aslami, Pooja, et al.
Published: (2024)
Class-specific feature selection for classification explainability
by: Aguilar-Ruiz, Jesus S.
Published: (2024)
by: Aguilar-Ruiz, Jesus S.
Published: (2024)
Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data
by: García-Méndez, Silvia, et al.
Published: (2024)
by: García-Méndez, Silvia, et al.
Published: (2024)
An explainable machine learning approach for energy forecasting at the household level
by: Béraud, Pauline, et al.
Published: (2024)
by: Béraud, Pauline, et al.
Published: (2024)
Holistic Artificial Intelligence in Medicine; improved performance and explainability
by: Petridis, Periklis, et al.
Published: (2025)
by: Petridis, Periklis, et al.
Published: (2025)
DTOR: Decision Tree Outlier Regressor to explain anomalies
by: Crupi, Riccardo, et al.
Published: (2024)
by: Crupi, Riccardo, et al.
Published: (2024)
A federated learning framework with knowledge graph and temporal transformer for early sepsis prediction in multi-center ICUs
by: Chang, Yue, et al.
Published: (2026)
by: Chang, Yue, et al.
Published: (2026)
Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformers
by: Alfarano, Alberto, et al.
Published: (2024)
by: Alfarano, Alberto, et al.
Published: (2024)
A Machine Learning Pipeline for Multiple Sclerosis Biomarker Discovery: Comparing explainable AI and Traditional Statistical Approaches
by: Punzo, Samuele, et al.
Published: (2025)
by: Punzo, Samuele, et al.
Published: (2025)
An explainable model to support the decision about the therapy protocol for AML
by: Almeida, Jade M., et al.
Published: (2023)
by: Almeida, Jade M., et al.
Published: (2023)
survex: an R package for explaining machine learning survival models
by: Spytek, Mikołaj, et al.
Published: (2023)
by: Spytek, Mikołaj, et al.
Published: (2023)
Predicting life satisfaction using machine learning and explainable AI
by: Khan, Alif Elham, et al.
Published: (2025)
by: Khan, Alif Elham, et al.
Published: (2025)
Towards Few-shot Self-explaining Graph Neural Networks
by: Peng, Jingyu, et al.
Published: (2024)
by: Peng, Jingyu, et al.
Published: (2024)
AI Epidemiology: achieving explainable AI through expert oversight patterns
by: Tempest-Walters, Kit
Published: (2025)
by: Tempest-Walters, Kit
Published: (2025)
Temporal convolutional and fusional transformer model with Bi-LSTM encoder-decoder for multi-time-window remaining useful life prediction
by: Pour, Mohamadreza Akbari, et al.
Published: (2025)
by: Pour, Mohamadreza Akbari, et al.
Published: (2025)
Transformers know more than they can tell -- Learning the Collatz sequence
by: Charton, François, et al.
Published: (2025)
by: Charton, François, et al.
Published: (2025)
Distill n' Explain: explaining graph neural networks using simple surrogates
by: Pereira, Tamara, et al.
Published: (2023)
by: Pereira, Tamara, et al.
Published: (2023)
PruneGCRN: Minimizing and explaining spatio-temporal problems through node pruning
by: García-Sigüenza, Javier, et al.
Published: (2025)
by: García-Sigüenza, Javier, et al.
Published: (2025)
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study
by: Bobek, Szymon, et al.
Published: (2024)
by: Bobek, Szymon, et al.
Published: (2024)
CNN-TFT explained by SHAP with multi-head attention weights for time series forecasting
by: Stefenon, Stefano F., et al.
Published: (2025)
by: Stefenon, Stefano F., et al.
Published: (2025)
An explainable approach to detect case law on housing and eviction issues within the HUDOC database
by: Mohammadi, Mohammad, et al.
Published: (2024)
by: Mohammadi, Mohammad, et al.
Published: (2024)
Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
by: Liu, Fanzhen, et al.
Published: (2025)
by: Liu, Fanzhen, et al.
Published: (2025)
A redescription mining framework for post-hoc explaining and relating deep learning models
by: Mihelčić, Matej, et al.
Published: (2025)
by: Mihelčić, Matej, et al.
Published: (2025)
BACON: A fully explainable AI model with graded logic for decision making problems
by: Bai, Haishi, et al.
Published: (2025)
by: Bai, Haishi, et al.
Published: (2025)
An explainable vision transformer with transfer learning based efficient drought stress identification
by: Patra, Aswini Kumar, et al.
Published: (2024)
by: Patra, Aswini Kumar, et al.
Published: (2024)
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations
by: Bobek, Szymon, et al.
Published: (2023)
by: Bobek, Szymon, et al.
Published: (2023)
Similar Items
-
Int2Int: a framework for mathematics with transformers
by: Charton, François
Published: (2025) -
Emergent properties with repeated examples
by: Charton, François, et al.
Published: (2024) -
Instruction Diversity Drives Generalization To Unseen Tasks
by: Zhang, Dylan, et al.
Published: (2024) -
Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
by: Feng, Yunzhen, et al.
Published: (2024) -
$\textbf{Only-IF}$:Revealing the Decisive Effect of Instruction Diversity on Generalization
by: Zhang, Dylan, et al.
Published: (2024)