:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Oh, Jong Kwon, Lyu, Hanbaek, Son, Hwijae
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2509.19773
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
by: Son, Hwijae
Published: (2025)

On the Complexity of First-Order Methods in Stochastic Bilevel Optimization
by: Kwon, Jeongyeol, et al.
Published: (2024)

QFlash: Bridging Quantization and Memory Efficiency in Vision Transformer Attention
by: Oh, Sehyeon, et al.
Published: (2026)

A Predictive Model Based on Transformer with Statistical Feature Embedding in Manufacturing Sensor Dataset
by: Lee, Gyeong Taek, et al.
Published: (2024)

On permutation-invariant neural networks
by: Kimura, Masanari, et al.
Published: (2024)

Attention mechanisms in neural networks
by: Hays, Hasi
Published: (2026)

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
by: Cho, Sung Woong, et al.
Published: (2024)

Linearity-based neural network compression
by: Dobler, Silas, et al.
Published: (2025)

Principles of Lipschitz continuity in neural networks
by: Luo, Róisín
Published: (2026)

Applying graph neural network to SupplyGraph for supply chain network
by: Han, Kihwan
Published: (2024)

Understanding the dynamics of the frequency bias in neural networks
by: Molina, Juan, et al.
Published: (2024)

Graph neural networks informed locally by thermodynamics
by: Tierz, Alicia, et al.
Published: (2024)

Graph neural networks and non-commuting operators
by: Velasco, Mauricio, et al.
Published: (2024)

LayerCollapse: Adaptive compression of neural networks
by: Shabgahi, Soheil Zibakhsh, et al.
Published: (2023)

Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
by: Tucny, Jean-Michel, et al.
Published: (2025)

Concealed Adversarial attacks on neural networks for sequential data
by: Sokerin, Petr, et al.
Published: (2025)

Dopamine-driven synaptic credit assignment in neural networks
by: Nambusubramaniyan, Saranraj, et al.
Published: (2025)

Elimination-compensation pruning for fully-connected neural networks
by: Ballini, Enrico, et al.
Published: (2026)

Planning in a recurrent neural network that plays Sokoban
by: Taufeeque, Mohammad, et al.
Published: (2024)

Understanding polysemanticity in neural networks through coding theory
by: Marshall, Simon C., et al.
Published: (2024)

Variational autoencoder-based neural network model compression
by: Cheng, Liang, et al.
Published: (2024)

Conditional computation in neural networks: principles and research trends
by: Scardapane, Simone, et al.
Published: (2024)

Deep neural networks have an inbuilt Occam's razor
by: Mingard, Chris, et al.
Published: (2023)

Utilizing Lyapunov Exponents in designing deep neural networks
by: Mittra, Tirthankar
Published: (2024)

Grey-informed neural network for time-series forecasting
by: Xie, Wanli, et al.
Published: (2024)

A comparative analysis of a neural network with calculated weights and a neural network with random generation of weights based on the training dataset size
by: Geidarov, Polad
Published: (2025)

Faithful and Fast Influence Function via Advanced Sampling
by: Koh, Jungyeon, et al.
Published: (2025)

Solving Video Inverse Problems Using Image Diffusion Models
by: Kwon, Taesung, et al.
Published: (2024)

VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models
by: Kwon, Taesung, et al.
Published: (2024)

Understanding the learned look-ahead behavior of chess neural networks
by: Cruz, Diogo
Published: (2025)

Confidence-gated training for efficient early-exit neural networks
by: Mokssit, Saad, et al.
Published: (2025)

StaQ it! Growing neural networks for Policy Mirror Descent
by: Shilova, Alena, et al.
Published: (2025)

Topological derivative approach for deep neural network architecture adaptation
by: Krishnanunni, C G, et al.
Published: (2025)

Robustness questions the interpretability of graph neural networks: what to do?
by: Lukyanov, Kirill, et al.
Published: (2025)

sHGCN: Simplified hyperbolic graph convolutional neural networks
by: Arévalo, Pol, et al.
Published: (2025)

Addressing divergent representations from causal interventions on neural networks
by: Grant, Satchel, et al.
Published: (2025)

Adaptive multiple optimal learning factors for neural network training
by: Challagundla, Jeshwanth
Published: (2024)

Stochastic stem bucking using mixture density neural networks
by: Schmiedel, Simon
Published: (2024)

Do graph neural network states contain graph properties?
by: Pelletreau-Duris, Tom, et al.
Published: (2024)

Do deep neural networks utilize the weight space efficiently?
by: Koyun, Onur Can, et al.
Published: (2024)