:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Hammad, M. M.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2407.19258
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deep Learning Activation Functions: Fixed-Shape, Parametric, Adaptive, Stochastic, Miscellaneous, Non-Standard, Ensemble
by: Hammad, M. M.
Published: (2024)

Artificial Neural Network and Deep Learning: Fundamentals and Theory
by: Hammad, M. M.
Published: (2024)

Schrodinger Neural Network and Uncertainty Quantification: Quantum Machine
by: Hammad, M. M.
Published: (2025)

Time Series Compression using Quaternion Valued Neural Networks and Quaternion Backpropagation
by: Pöppelbaum, Johannes, et al.
Published: (2024)

Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks
by: Kunc, Vladimír, et al.
Published: (2024)

Backpropagation Neural Tree
by: Ojha, Varun, et al.
Published: (2022)

Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation
by: Lucena, Anaísa, et al.
Published: (2026)

Dynamic Spectral Backpropagation for Efficient Neural Network Training
by: Muthuraman, Mannmohan
Published: (2025)

Steinmetz Neural Networks for Complex-Valued Data
by: Venkatasubramanian, Shyam, et al.
Published: (2024)

Less Memory Means smaller GPUs: Backpropagation with Compressed Activations
by: Barley, Daniel, et al.
Published: (2024)

BASIS: Balanced Activation Sketching with Invariant Scalars for "Ghost Backpropagation"
by: Khasia, Vladimer
Published: (2026)

Backpropagation on Dynamical Networks
by: Tan, Eugene, et al.
Published: (2022)

Rethinking Deep Learning: Propagating Information in Neural Networks without Backpropagation and Statistical Optimization
by: Itoh, Kei
Published: (2024)

Semiring Activation in Neural Networks
by: Smets, Bart M. N., et al.
Published: (2024)

Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm
by: Ghader, Mohammadnavid, et al.
Published: (2025)

Stabilizing Backpropagation Through Time to Learn Complex Physics
by: Schnell, Patrick, et al.
Published: (2024)

A Comprehensive Survey on Graph Summarization with Graph Neural Networks
by: Shabani, Nasrin, et al.
Published: (2023)

DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks
by: Eilers, Florian, et al.
Published: (2024)

Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
by: Hammad, M. M.
Published: (2026)

Grad-Instructor: Universal Backpropagation with Explainable Evaluation Neural Networks for Meta-learning and AutoML
by: Ino, Ryohei
Published: (2024)

A Comprehensive Survey on Self-Interpretable Neural Networks
by: Ji, Yang, et al.
Published: (2025)

Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation
by: Tang, Bo, et al.
Published: (2026)

Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks
by: Brenner, Rorry, et al.
Published: (2025)

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

Newton-Puiseux Analysis for Interpretability and Calibration of Complex-Valued Neural Networks
by: Migus, Piotr
Published: (2025)

Neural Networks with Complex-Valued Weights Have No Spurious Local Minima
by: Liu, Xingtu
Published: (2021)

The Cost of Avoiding Backpropagation
by: Panchal, Kunjal, et al.
Published: (2025)

Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
by: Yu, Chengting, et al.
Published: (2024)

Optimal Control Theoretic Neural Optimizer: From Backpropagation to Dynamic Programming
by: Liu, Guan-Horng, et al.
Published: (2025)

Stabilizing Backpropagation in 16-bit Neural Training with Modified Adam Optimizer
by: Yun, Juyoung
Published: (2023)

Advancing On-Device Neural Network Training with TinyPropv2: Dynamic, Sparse, and Efficient Backpropagation
by: Rüb, Marcus, et al.
Published: (2024)

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

Beyond Backpropagation: Exploring Innovative Algorithms for Energy-Efficient Deep Neural Network Training
by: Spyra, Przemysław
Published: (2025)

Agentic Neural Networks: Self-Evolving Multi-Agent Systems via Textual Backpropagation
by: Ma, Xiaowen, et al.
Published: (2025)

HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagation
by: Dudek, Grzegorz, et al.
Published: (2025)

Timing-Based Backpropagation in Spiking Neural Networks Without Single-Spike Restrictions
by: Yamamoto, Kakei, et al.
Published: (2022)

Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory
by: Mathiasen, Alexander, et al.
Published: (2024)

Perturbing the Phase: Analyzing Adversarial Robustness of Complex-Valued Neural Networks
by: Eilers, Florian, et al.
Published: (2026)

Efficient Training of Neural Fractional-Order Differential Equation via Adjoint Backpropagation
by: Kang, Qiyu, et al.
Published: (2025)

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