Can Test-time Computation Mitigate Reproduction Bias in Neural Symbolic Regression?
Fuente:
arXiv
Saved in:
| Main Authors: | Sato, Shun, Sato, Issei |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Explaining Grokking and Information Bottleneck through Neural Collapse Emergence
by: Sakamoto, Keitaro, et al.
Published: (2025)
by: Sakamoto, Keitaro, et al.
Published: (2025)
Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment
by: Hasegawa, Naoya, et al.
Published: (2024)
by: Hasegawa, Naoya, et al.
Published: (2024)
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training
by: Sakamoto, Keitaro, et al.
Published: (2024)
by: Sakamoto, Keitaro, et al.
Published: (2024)
Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation
by: Tomihari, Akiyoshi, et al.
Published: (2026)
by: Tomihari, Akiyoshi, et al.
Published: (2026)
Benign Overfitting in Token Selection of Attention Mechanism
by: Sakamoto, Keitaro, et al.
Published: (2024)
by: Sakamoto, Keitaro, et al.
Published: (2024)
Exploring Weight Balancing on Long-Tailed Recognition Problem
by: Hasegawa, Naoya, et al.
Published: (2023)
by: Hasegawa, Naoya, et al.
Published: (2023)
On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding
by: Xu, Kevin, et al.
Published: (2024)
by: Xu, Kevin, et al.
Published: (2024)
Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective
by: Tomihari, Akiyoshi, et al.
Published: (2024)
by: Tomihari, Akiyoshi, et al.
Published: (2024)
Top-Down Bayesian Posterior Sampling for Sum-Product Networks
by: Yokoi, Soma, et al.
Published: (2024)
by: Yokoi, Soma, et al.
Published: (2024)
Understanding Generalization in Physics Informed Models through Affine Variety Dimensions
by: Koshizuka, Takeshi, et al.
Published: (2025)
by: Koshizuka, Takeshi, et al.
Published: (2025)
To CoT or To Loop? A Formal Comparison Between Chain-of-Thought and Looped Transformers
by: Xu, Kevin, et al.
Published: (2025)
by: Xu, Kevin, et al.
Published: (2025)
Max-pooling Network Revisited: Analyzing the Role of Semantic Probability in Multiple Instance Learning for Hallucination Detection
by: Fujikawa, Shota, et al.
Published: (2026)
by: Fujikawa, Shota, et al.
Published: (2026)
Rethinking Associative Memory Mechanism in Induction Head
by: Wang, Shuo, et al.
Published: (2024)
by: Wang, Shuo, et al.
Published: (2024)
On the Optimal Memorization Capacity of Transformers
by: Kajitsuka, Tokio, et al.
Published: (2024)
by: Kajitsuka, Tokio, et al.
Published: (2024)
Fix Initial Codes and Iteratively Refine Textual Directions Toward Safe Multi-Turn Code Correction
by: Tanaka, Yuto, et al.
Published: (2026)
by: Tanaka, Yuto, et al.
Published: (2026)
A Formal Comparison Between Chain of Thought and Latent Thought
by: Xu, Kevin, et al.
Published: (2025)
by: Xu, Kevin, et al.
Published: (2025)
Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective
by: Koshizuka, Takeshi, et al.
Published: (2023)
by: Koshizuka, Takeshi, et al.
Published: (2023)
Understanding Transformer Optimization via Gradient Heterogeneity
by: Tomihari, Akiyoshi, et al.
Published: (2025)
by: Tomihari, Akiyoshi, et al.
Published: (2025)
Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?
by: Kajitsuka, Tokio, et al.
Published: (2023)
by: Kajitsuka, Tokio, et al.
Published: (2023)
On the Overlooked Pitfalls of Weight Decay and How to Mitigate Them: A Gradient-Norm Perspective
by: Xie, Zeke, et al.
Published: (2020)
by: Xie, Zeke, et al.
Published: (2020)
CALT: A Library for Computer Algebra with Transformer
by: Kera, Hiroshi, et al.
Published: (2025)
by: Kera, Hiroshi, et al.
Published: (2025)
The Road to Learning Explainable Inverse Kinematic Models: Graph Neural Networks as Inductive Bias for Symbolic Regression
by: Pandey, Pravin, et al.
Published: (2025)
by: Pandey, Pravin, et al.
Published: (2025)
Training-free Graph Neural Networks and the Power of Labels as Features
by: Sato, Ryoma
Published: (2024)
by: Sato, Ryoma
Published: (2024)
Even GPT-5.2 Can't Count to Five: The Case for Zero-Error Horizons in Trustworthy LLMs
by: Sato, Ryoma
Published: (2026)
by: Sato, Ryoma
Published: (2026)
Scalable Neural Symbolic Regression using Control Variables
by: Chu, Xieting, et al.
Published: (2023)
by: Chu, Xieting, et al.
Published: (2023)
Explicit and Implicit Graduated Optimization in Deep Neural Networks
by: Sato, Naoki, et al.
Published: (2024)
by: Sato, Naoki, et al.
Published: (2024)
Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
by: Sato, Ryoma
Published: (2023)
by: Sato, Ryoma
Published: (2023)
Operator Feature Neural Network for Symbolic Regression
by: Deng, Yusong, et al.
Published: (2024)
by: Deng, Yusong, et al.
Published: (2024)
Neural Symbolic Regression of Complex Network Dynamics
by: Qiu, Haiquan, et al.
Published: (2024)
by: Qiu, Haiquan, et al.
Published: (2024)
Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions
by: Sato, Atsuki, et al.
Published: (2026)
by: Sato, Atsuki, et al.
Published: (2026)
Symbolic Regression via Neural Networks
by: Boddupalli, Nibodh, et al.
Published: (2026)
by: Boddupalli, Nibodh, et al.
Published: (2026)
More Test-Time Compute Can Hurt: Overestimation Bias in LLM Beam Search
by: Dalal, Gal, et al.
Published: (2026)
by: Dalal, Gal, et al.
Published: (2026)
Mitigating Bias in Graph Hyperdimensional Computing
by: Liu, Yezi, et al.
Published: (2025)
by: Liu, Yezi, et al.
Published: (2025)
Comparing Methods for Bias Mitigation in Graph Neural Networks
by: Hoffmann, Barbara, et al.
Published: (2025)
by: Hoffmann, Barbara, et al.
Published: (2025)
Mitigating Degree Bias in Signed Graph Neural Networks
by: He, Fang, et al.
Published: (2024)
by: He, Fang, et al.
Published: (2024)
PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression
by: Wu, Min, et al.
Published: (2024)
by: Wu, Min, et al.
Published: (2024)
Rethinking Inductive Bias in Geographically Neural Network Weighted Regression
by: Chen, Zhenyuan
Published: (2025)
by: Chen, Zhenyuan
Published: (2025)
Interestingness First Classifiers
by: Sato, Ryoma
Published: (2025)
by: Sato, Ryoma
Published: (2025)
SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration
by: Naser, MZ, et al.
Published: (2024)
by: Naser, MZ, et al.
Published: (2024)
Diffusion-Based Symbolic Regression
by: Bastiani, Zachary, et al.
Published: (2025)
by: Bastiani, Zachary, et al.
Published: (2025)
Similar Items
-
Explaining Grokking and Information Bottleneck through Neural Collapse Emergence
by: Sakamoto, Keitaro, et al.
Published: (2025) -
Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment
by: Hasegawa, Naoya, et al.
Published: (2024) -
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training
by: Sakamoto, Keitaro, et al.
Published: (2024) -
Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation
by: Tomihari, Akiyoshi, et al.
Published: (2026) -
Benign Overfitting in Token Selection of Attention Mechanism
by: Sakamoto, Keitaro, et al.
Published: (2024)