:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Jiang, Huixiu, Yang, Ling, Bao, Yu, Si, Rutong, Yang, Sikun
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2407.16944
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs
by: Yang, Sikun, et al.
Published: (2023)

Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
by: Yang, Sikun, et al.
Published: (2024)

Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure
by: Yu, Xincan, et al.
Published: (2024)

Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study
by: Chen, Cheng, et al.
Published: (2020)

On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
by: Liang, Ling, et al.
Published: (2024)

Adaptive Tabu Dropout for Regularization of Deep Neural Network
by: Hasan, Md. Tarek, et al.
Published: (2024)

Stochastic Voronoi Ensembles for Anomaly Detection
by: Cao, Yang, et al.
Published: (2026)

Negative-Binomial Randomized Gamma Markov Processes for Heterogeneous Overdispersed Count Time Series
by: Huang, Rui, et al.
Published: (2024)

Towards Token-Level Text Anomaly Detection
by: Cao, Yang, et al.
Published: (2026)

Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
by: Tucat, Matteo, et al.
Published: (2024)

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
by: Liu, Hao, et al.
Published: (2024)

Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well
by: Du, Xin, et al.
Published: (2025)

MPD-SGR: Robust Spiking Neural Networks with Membrane Potential Distribution-Driven Surrogate Gradient Regularization
by: Jiang, Runhao, et al.
Published: (2025)

Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
by: Rajpal, Rajit, et al.
Published: (2025)

Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks
by: Sato, Naoki, et al.
Published: (2024)

Gradient-based Sample Selection for Faster Bayesian Optimization
by: Wei, Qiyu, et al.
Published: (2025)

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
by: Xie, Xingyu, et al.
Published: (2022)

High-entropy Advantage in Neural Networks' Generalizability
by: Yang, Entao, et al.
Published: (2025)

Generalizability of Memorization Neural Networks
by: Yu, Lijia, et al.
Published: (2024)

Sharpness-Aware Minimization with Adaptive Regularization for Training Deep Neural Networks
by: Zou, Jinping, et al.
Published: (2024)

Adaptive Norm-Based Regularization for Neural Networks
by: Qasim, Muhammad, et al.
Published: (2026)

Provable Generalization Bounds for Deep Neural Networks with Momentum-Adaptive Gradient Dropout
by: Safder, Adeel
Published: (2025)

AdaCubic: An Adaptive Cubic Regularization Optimizer for Deep Learning
by: Tsingalis, Ioannis, et al.
Published: (2026)

Lattice-based Deep Neural Networks: Regularity and Tailored Regularization
by: Keller, Alexander, et al.
Published: (2026)

Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks
by: Yamaguchi, Shin'ya, et al.
Published: (2024)

CauchyNet: Compact and Data-Efficient Learning using Holomorphic Activation Functions
by: Zhang, Hong-Kun, et al.
Published: (2025)

Quantization-Aware Regularizers for Deep Neural Networks Compression
by: Malchiodi, Dario, et al.
Published: (2026)

Improving Infinitely Deep Bayesian Neural Networks with Nesterov's Accelerated Gradient Method
by: Yu, Chenxu, et al.
Published: (2026)

Complementary Fusion of Deep Network and Tree Model for ETA Prediction
by: Huang, YuRui, et al.
Published: (2024)

Decoupled Orthogonal Dynamics: Regularization for Deep Network Optimizers
by: Chen, Hao, et al.
Published: (2026)

Reconstructing Deep Neural Networks: Unleashing the Optimization Potential of Natural Gradient Descent
by: Liu, Weihua, et al.
Published: (2024)

Faster Gradient Methods for Highly-Smooth Stochastic Bilevel Optimization
by: Chen, Lesi, et al.
Published: (2025)

Faster Gradient-Free Algorithms for Nonsmooth Nonconvex Stochastic Optimization
by: Chen, Lesi, et al.
Published: (2023)

Isolation-based Spherical Ensemble Representations for Anomaly Detection
by: Cao, Yang, et al.
Published: (2025)

Kernel Representation and Similarity Measure for Incomplete Data
by: Cao, Yang, et al.
Published: (2025)

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
by: Zhou, Dongruo, et al.
Published: (2018)

Variational Stochastic Gradient Descent for Deep Neural Networks
by: Chen, Haotian, et al.
Published: (2024)

Faster Verified Explanations for Neural Networks
by: De Palma, Alessandro, et al.
Published: (2025)

Signal-Adaptive Trust Regions for Gradient-Free Optimization of Recurrent Spiking Neural Networks
by: Li, Jinhao, et al.
Published: (2026)

Towards Scalable and Deep Graph Neural Networks via Noise Masking
by: Liang, Yuxuan, et al.
Published: (2024)