:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Li, Enji
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2412.14655
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks
by: Subramanian, Barathi, et al.
Published: (2024)

Enhancing Circuit Trainability with Selective Gate Activation Strategy
by: Cho, Jeihee, et al.
Published: (2025)

dynActivation: A Trainable Activation Family for Adaptive Nonlinearity
by: Bachmann, Alois
Published: (2026)

SwishReLU: A Unified Approach to Activation Functions for Enhanced Deep Neural Networks Performance
by: Rahman, Jamshaid Ul, et al.
Published: (2024)

Trainable and Explainable Simplicial Map Neural Networks
by: Paluzo-Hidalgo, Eduardo, et al.
Published: (2023)

Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective
by: Koshizuka, Takeshi, et al.
Published: (2023)

Fractional Concepts in Neural Networks: Enhancing Activation Functions
by: Alijani, Zahra, et al.
Published: (2023)

ENN: A Neural Network with DCT Adaptive Activation Functions
by: Martinez-Gost, Marc, et al.
Published: (2023)

Playing the Lottery With Concave Regularizers for Sparse Trainable Neural Networks
by: Fracastoro, Giulia, et al.
Published: (2025)

Mapping the Edge of Chaos: Fractal-Like Boundaries in The Trainability of Decoder-Only Transformer Models
by: Torkamandi, Bahman
Published: (2025)

Beyond Over-smoothing: Uncovering the Trainability Challenges in Deep Graph Neural Networks
by: Peng, Jie, et al.
Published: (2024)

Neural Network Training via Stochastic Alternating Minimization with Trainable Step Sizes
by: Yan, Chengcheng, et al.
Published: (2025)

NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability
by: Raskovalov, Anton, et al.
Published: (2024)

A Family of Adaptive Activation Functions for Mitigating Failure Modes in Physics-Informed Neural Networks
by: Murari, Krishna
Published: (2026)

Weightless Neural Networks for Continuously Trainable Personalized Recommendation Systems
by: Latif, Rafayel, et al.
Published: (2025)

Provable Benefits of Sinusoidal Activation for Modular Addition
by: Huang, Tianlong, et al.
Published: (2025)

Conjugate Learning Theory: Uncovering the Mechanisms of Trainability and Generalization in Deep Neural Networks
by: Qi, Binchuan
Published: (2026)

Nonlinearity Enhanced Adaptive Activation Functions
by: Yevick, David
Published: (2024)

Rethinking Nonlinearity: Trainable Gaussian Mixture Modules for Modern Neural Architectures
by: Lu, Weiguo, et al.
Published: (2025)

TrAct: Making First-layer Pre-Activations Trainable
by: Petersen, Felix, et al.
Published: (2024)

Topology-Aware Activation Functions in Neural Networks
by: Snopov, Pavel, et al.
Published: (2025)

SmartMixed: A Two-Phase Training Strategy for Adaptive Activation Function Learning in Neural Networks
by: Omidvar, Amin
Published: (2025)

Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
by: Horvath, Samuel, et al.
Published: (2023)

A Trainable Feature Extractor Module for Deep Neural Networks and Scanpath Classification
by: Fuhl, Wolfgang
Published: (2024)

NetworkFF: Unified Layer Optimization in Forward-Only Neural Networks
by: Beigzad, Salar
Published: (2025)

A Trainable Optimizer
by: Wang, Ruiqi, et al.
Published: (2025)

Equivariant Neural Networks for Force-Field Models of Lattice Systems
by: Fan, Yunhao, et al.
Published: (2026)

Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation
by: Xu, Boxun, et al.
Published: (2026)

Thermodynamic Transferability in Coarse-Grained Force Fields using Graph Neural Networks
by: Shinkle, Emily, et al.
Published: (2024)

Enhancing the Trainability of Variational Quantum Circuits with Regularization Strategies
by: Zhuang, Jun, et al.
Published: (2024)

SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming
by: Cai, Yeyue, et al.
Published: (2025)

Extension of Symmetrized Neural Network Operators with Fractional and Mixed Activation Functions
by: Santos, Rômulo Damasclin Chaves dos, et al.
Published: (2025)

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks
by: Zhang, Zhongyue, et al.
Published: (2026)

Optimizing Performance of Feedforward and Convolutional Neural Networks through Dynamic Activation Functions
by: Rane, Chinmay, et al.
Published: (2023)

YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training
by: Li, Yi, et al.
Published: (2024)

Deep Network Trainability via Persistent Subspace Orthogonality
by: Massucco, Alex, et al.
Published: (2025)

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
by: Xie, Qihu, et al.
Published: (2025)

Parameter-Free Structural-Diversity Message Passing for Graph Neural Networks
by: Kong, Mingyue, et al.
Published: (2025)

Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency
by: Poppel, Michael, et al.
Published: (2026)

MetaSymNet: A Tree-like Symbol Network with Adaptive Architecture and Activation Functions
by: Li, Yanjie, et al.
Published: (2023)