arXiv AI July 21, 2026 By Yuanzhe Jia

Building a Neural Network from Scratch: Implementation, Evaluation, and Optimization

arXiv:2607. 16682v1 Announce Type: cross Abstract: The widespread adoption of high-level deep learning libraries, while accelerating model development, has increasingly abstracted away the internal mechanics of neural networks, creating a gap between practical usage and fundamental understanding.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

benchmarks

arXiv AI

Aug 5

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

arXiv:2311. 17815v3 Announce Type: replace-cross Abstract: Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performance Computing (HPC) and edge platforms, leading to a wide variety of proposals for specialized deep learning architectures and hardware accelerators.

By Serena Curzel, Fabrizio Ferrandi, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Luca Bompani, Luca Benini, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Stefania Perri

arXiv AI

Jun 16

Multi-Grade Deep Learning for Partial Differential Equations with Applications to the Burgers Equation

arXiv:2309. 07401v2 Announce Type: replace-cross Abstract: Deep neural networks (DNNs) show great promise for solving partial differential equations (PDEs), but their deep architectures introduce complex, large-scale, non-convex optimization challenges.

By Yuesheng Xu, Taishan Zeng

arXiv Machine Learning

Jun 16

Enhancing Physics-Informed Neural Networks Through Feature Engineering

arXiv:2502. 07209v4 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) seek to solve partial differential equations (PDEs) with deep learning.

By Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell

arXiv AI

Jul 29

CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning

arXiv:2509. 11285v2 Announce Type: replace-cross Abstract: Class-Incremental Learning (CIL) in deep neural networks is conventionally framed as an iterative gradient-based optimization problem, incurring high computational cost, hyperparameter sensitivity, and risk of catastrophic forgetting.

By Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdi\~nas, Amparo Alonso-Betanzos

rag diffusion safety

arXiv Machine Learning

Jul 8

Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning

arXiv:2607. 06151v1 Announce Type: new Abstract: Generalization remains a pivotal challenge in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitting and reduced performance on unseen data.

By Yao Fu, Chunxia Zhang, Junmin Liu, Yihang Jin, Haishan Ye, Yuanao Yang

benchmarks

arXiv AI

2d ago

Designing Compact Neural Architectures via Neuron Gating and Mixed Activation

arXiv:2608. 14443v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) is naturally formulated as a bilevel optimization problem, where the upper-level optimizes the architecture using validation performance and the lower-level trains network parameters using training loss.

By Abhishek Shukla, Ankur Sinha, Faiz Hamid

llms

Related stories

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

Multi-Grade Deep Learning for Partial Differential Equations with Applications to the Burgers Equation

Enhancing Physics-Informed Neural Networks Through Feature Engineering

CIFNet: An Analytic Neural Learning Framework for Efficient and Calibrated Class-Incremental Learning

Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning

Designing Compact Neural Architectures via Neuron Gating and Mixed Activation