Copertina

Meta-Learning Neural Procedural Biases

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Raymond, Christian, Chen, Qi, Xue, Bing, Zhang, Mengjie
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Meta-Learning Adaptive Loss Functions
di: Raymond, Christian, et al.
Pubblicazione: (2023)

Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
di: Raymond, Christian, et al.
Pubblicazione: (2022)

Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning
di: Raymond, Christian, et al.
Pubblicazione: (2024)

LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression
di: Zhang, Hengzhe, et al.
Pubblicazione: (2025)

Meta-Learning Loss Functions for Deep Neural Networks
di: Raymond, Christian
Pubblicazione: (2024)

A Consistent Lebesgue Measure for Multi-label Learning
di: Demir, Kaan, et al.
Pubblicazione: (2024)

EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming
di: Yang, Xuanhao, et al.
Pubblicazione: (2026)

Sharpness-Aware Minimization for Evolutionary Feature Construction in Regression
di: Zhang, Hengzhe, et al.
Pubblicazione: (2024)

Enhancing Generalization in Evolutionary Feature Construction for Symbolic Regression through Vicinal Jensen Gap Minimization
di: Zhang, Hengzhe, et al.
Pubblicazione: (2026)

Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations
di: Wang, Bin, et al.
Pubblicazione: (2022)

Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning
di: Hu, Yifan, et al.
Pubblicazione: (2020)

A Genetic Algorithm-Based Approach for Automated Optimization of Kolmogorov-Arnold Networks in Classification Tasks
di: Long, Quan, et al.
Pubblicazione: (2025)

Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification
di: Pei, Wenbin, et al.
Pubblicazione: (2026)

EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning
di: Pei, Wenbin, et al.
Pubblicazione: (2024)

Scalable Spatiotemporal Inference with Biased Scan Attention Transformer Neural Processes
di: Jenson, Daniel, et al.
Pubblicazione: (2025)

Characterising the Inductive Biases of Neural Networks on Boolean Data
di: Mingard, Chris, et al.
Pubblicazione: (2025)

Machine Learning for Two-Stage Graph Sparsification for the Travelling Salesman Problem
di: Lin, Bo-Cheng, et al.
Pubblicazione: (2026)

Meta Clustering of Neural Bandits
di: Ban, Yikun, et al.
Pubblicazione: (2024)

A Copula Graphical Model for Multi-Attribute Data using Optimal Transport
di: Zhang, Qi, et al.
Pubblicazione: (2024)

Expressivity of Neural Networks with Random Weights and Learned Biases
di: Williams, Ezekiel, et al.
Pubblicazione: (2024)

Procedural Fairness in Machine Learning
di: Wang, Ziming, et al.
Pubblicazione: (2024)

Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation
di: Kou, Zhiqiang, et al.
Pubblicazione: (2025)

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases
di: Romero, David W.
Pubblicazione: (2024)

Amortising Inference and Meta-Learning Priors in Neural Networks
di: Rochussen, Tommy, et al.
Pubblicazione: (2026)

From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions
di: Chang, Trenton, et al.
Pubblicazione: (2024)

Deconstructing Positional Information: From Attention Logits to Training Biases
di: Gu, Zihan, et al.
Pubblicazione: (2025)

GRADIEND: Feature Learning within Neural Networks Exemplified through Biases
di: Drechsel, Jonathan, et al.
Pubblicazione: (2025)

Learning from a Biased Sample
di: Sahoo, Roshni, et al.
Pubblicazione: (2022)

MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs
di: Chen, Jiawen, et al.
Pubblicazione: (2025)

Meta-Learning for Neural Network-based Temporal Point Processes
di: Takimoto, Yoshiaki, et al.
Pubblicazione: (2024)

When Diffusion Models Memorize: Inductive Biases in Probability Flow of Minimum-Norm Shallow Neural Nets
di: Zeno, Chen, et al.
Pubblicazione: (2025)

Machine Learning for Raman Spectroscopy-based Cyber-Marine Fish Biochemical Composition Analysis
di: Zhou, Yun, et al.
Pubblicazione: (2024)

MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
di: Zhang, Baoquan, et al.
Pubblicazione: (2023)

Lyapunov Stability Learning with Nonlinear Control via Inductive Biases
di: Lu, Yupu, et al.
Pubblicazione: (2025)

Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning
di: Li, Zijian, et al.
Pubblicazione: (2025)

GPLight+: A Genetic Programming Method for Learning Symmetric Traffic Signal Control Policy
di: Liao, Xiao-Cheng, et al.
Pubblicazione: (2025)

Curriculum Learning in Genetic Programming Guided Local Search for Large-scale Vehicle Routing Problems
di: Liu, Saining, et al.
Pubblicazione: (2025)

Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control
di: Liao, Xiao-Cheng, et al.
Pubblicazione: (2025)

Beyond Specialization: Robust Reinforcement Learning Navigation via Procedural Map Generators
di: Jestel, Christian, et al.
Pubblicazione: (2026)

From Perception to Punchline: Empowering VLM with the Art of In-the-wild Meme
di: Li, Xueyan, et al.
Pubblicazione: (2025)