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22 results for “neural networks”
SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks
SeFoRA is a new federated learning algorithm that enables parameter-efficient fine-tuning of large language models across heterogeneous clients using sketch-based aggregation to resolve rank incompatibility and bilinear mismatch in LoRA updates.
Aug 12, 2026
AI Is Dead. Organoids Are Alive
The article declares 'AI Is Dead' and positions lab-grown brain organoids as an emerging alternative to artificial neural networks, suggesting they may soon surpass AI in cognitive capability.
Aug 11, 2026
NeuMoSync: End-to-End Neuromodulatory Control for Plasticity and Adaptability in Continual Learning
A new neural network architecture called NeuMoSync introduces neuron-specific neuromodulatory signals inspired by brain biology to improve plasticity and adaptability in continual learning tasks across multiple benchmark types.
Aug 6, 2026
Learning to Resolve Neutron Resonances with Fully Convolutional Neural Networks
A preliminary study tests a fully convolutional neural network to detect neutron resonances in transmission spectra, finding high point-wise classification accuracy (93%) but poor generalization to unseen isotopes — highlighting feasibility but not readiness for deployment.
Aug 6, 2026
Neural Networks with Local Converging Inputs for Efficient Options Pricing Models
Researchers introduced Neural Networks with Local Converging Inputs (NNLCI), a method that improves numerical option pricing accuracy by locally correcting coarse-and-refined mesh solutions using minimal high-fidelity training data, showing 4–12× RMSE reduction across benchmark PDEs.
Aug 5, 2026
Designing a Good Virtual Node: Addressable and Cardinality-Preserving Global Memory for Message Passing Architectures
A new research paper proposes an 'addressable and cardinality-preserving' virtual node design for graph neural networks that improves global memory representation without self-attention, enabling injective multiset encoding for tasks like motif counting and link prediction.
Aug 5, 2026
GLOBE: Trajectory-Aligned Gradient Matching with Structured SparseOptimization for Coreset Selection
GLOBE is a new coreset selection method that uses gradient trajectories across training checkpoints and multi-order matching to improve on-device training efficiency by selecting compact, representative subsets of training data.
Aug 5, 2026
Sphere Retraction Normalizations
A new family of spherical normalization methods for residual connections in deep neural networks is introduced, unifying existing approaches under a single angular retraction framework and showing improved validation loss on nanoGPT.
Aug 5, 2026
Rethinking Pretraining for Specialized Design Data: Evidence from the JONES-19 Cultural Design Dataset
A new arXiv preprint challenges the necessity of large-scale general pretraining (e.g., ImageNet) for specialized design tasks, showing that learning from scratch on a small, curated dataset—JONES-19—can match performance when augmented with multi-crop sampling.
Aug 4, 2026
Feature Interaction Modeling for Physics-Informed Neural Networks and Neural Operators
Researchers introduced feature interaction modules from factorization machines into physics-informed neural networks and neural operators to improve accuracy on parameterized PDEs with strong cross-variable dependencies, especially shock-dominated or discontinuous systems.
Aug 3, 2026
Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits
A new research paper introduces a feature-map pruning method for CNNs using multi-armed bandit algorithms to selectively remove redundant convolutional channels while preserving model accuracy and reducing compute.
Jul 28, 2026
Hierarchical Grading in Large Language Models
Researchers propose Graded Large Language Models (GLLMs), a theoretical extension of transformer architecture using algebraic grading to improve statistical efficiency for level-stratified prediction tasks, with claims of provable risk separation and pre-certified optimization.
Jul 28, 2026
Anthropic Hit With Patent Suit Over Claude AI Neural Networks - Bloomberg Law News
Anthropic is facing a patent infringement lawsuit alleging its Claude AI models violate neural network architecture patents held by another party.
Jul 22, 2026
A Hybrid Mamba for Audio-Visual Navigation
A new hybrid Mamba-based architecture called Samba is proposed for audio-visual navigation, claiming improved generalization and navigation success rates over existing models on Matterport3D and Replica datasets.
Jul 16, 2026
Automatic Differentiation from Scratch: How PyTorch Computes Gradients in Physics-Informed Neural Networks
A technical arXiv preprint traces PyTorch’s automatic differentiation mechanics for physics-informed neural networks (PINNs), using explicit numerical walkthroughs and verification against hand derivations to clarify how nested gradients are computed.
Jul 16, 2026
Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
A new arXiv preprint (2607.09666v1) publishes a comprehensive, taxonomy-driven survey of Graph Neural Network (GNN) applications across the full knowledge graph (KG) technology lifecycle — from construction to reasoning to applications — identifying gaps, strengths, limitations, and future research directions.
Jul 14, 2026
PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation
A new AI research paper proposes PRecG, a graph-based method for legal precedent retrieval that segments judgments by rhetorical role and builds knowledge graphs per segment to improve semantic matching.
Jul 13, 2026
Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
A new arXiv preprint critically examines foundational evaluation and design paradigms in deep reinforcement learning, demonstrating through large-scale experiments that widely accepted methodologies have led to incorrect conclusions about algorithm performance.
Jul 10, 2026
Image classification via a quantum-inspired strategy involving a mixture of experts
A new arXiv preprint proposes a hybrid classical-quantum image classification framework using amplitude encoding, local unitary convolutions, and quantum stabilizer codes within a mixture-of-experts architecture, reporting ~2x lower failure rates on MNIST and Fashion-MNIST versus single-expert baselines.
Jul 10, 2026
UASPL: Uncertainty-Aware Self-Paced Learning with Evidential Neural Networks
Researchers introduced UASPL, an uncertainty-aware self-paced learning method using evidential neural networks to improve sample selection reliability and interpretability in machine learning training.
Jul 10, 2026
Domain Knowledge Based Temporal-Spatial Graph Convolution Network for ECG Recognition
A new graph convolutional neural network architecture incorporating domain-specific ECG landmarks and temporal-spatial graph structures achieves 88.1% average F1 score on a nine-class Chinese ECG dataset, improving rare-class detection by embedding clinical knowledge into model design.
Published Jul 3, 2026 · Analyzed Jul 6, 2026
Hamiltonian Neural Networks from a Differential Geometry Perspective [D]
A write-up on Hamiltonian Neural Networks from a differential-geometry perspective.
Published Jul 1, 2026 · Analyzed Jul 6, 2026