Key papers

• Towards Generalization of Graph Neural Networks for AC Optimal Power Flow (2025)
  Olayiwola Arowolo et al.
  5.21
• Training Hybrid Neural Networks with Multimode Optical Nonlinearities Using Digital Twins (2025)
  Ilker Oguz et al.
  4.71
• Monotonic Kolmogorov-Arnold Networks: A Theoretical and Empirical Study of Monotonicity as an Inductive Bias (2026)
  Mikhail Krasnov et al.
  4.33
• In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models (2026)
  Sam Earle et al.
  4.27
• High-entropy Advantage in Neural Networks' Generalizability (2025)
  Entao Yang et al.
  3.64
• SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning (2026)
  Jinxiu Liu et al.
  3.51
• How the fly holds a single goal: normalization, not selection, in Drosophila FC2 (2026)
  Gioele Nanni et al.
  3.51
• The Whale That Outswam Evolution: Swarm Intelligence Maximises Memory in Connectome Reservoirs (2026)
  Anmol Guragain et al.
  3.45
• LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks (2026)
  Tyler King et al.
  3.45
• Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations (2026)
  Arthur Fyon et al.
  1.89
• Surrogate Neural Architecture Codesign Package (SNAC-Pack) (2026)
  Jason Weitz et al.
  1.89
• Structure as Computation: Developmental Generation of Minimal Neural Circuits (2026)
  Duan Zhou
  1.83
• EvoMU: Evolutionary Machine Unlearning (2026)
  Pawel Batorski et al.
  1.72
• Investigating Quantum Circuit Designs Using Neuro-Evolution (2026)
  Devroop Kar et al.
  1.72
• Clifford Kolmogorov-Arnold Networks (2026)
  Matthias Wolff et al.
  1.72
• Multi-Scale Temporal Homeostasis Enables Efficient and Robust Neural Networks (2026)
  MD Azizul Hakim
  1.72
• Neural Operators Can Discover Functional Clusters (2026)
  Yicen Li et al.
  1.72
• Upper Approximation Bounds for Neural Oscillators (2025)
  Zifeng Huang et al.
  1.61
• ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms (2025)
  Juan Diego Toscano et al.
  1.61
• Conditional Morphogenesis: Emergent Generation of Structural Digits via Neural Cellular Automata (2025)
  Ali Sakour
  1.61
• ZeroSim: Zero-Shot Analog Circuit Evaluation with Unified Transformer Embeddings (2025)
  Xiaomeng Yang et al.
  1.56
• Initialization Schemes for Kolmogorov-Arnold Networks: An Empirical Study (2025)
  Spyros Rigas et al.
  1.44
• Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers (2025)
  Andrei Chertkov et al.
  1.44
• Hybrid unary-binary design for multiplier-less printed Machine Learning classifiers (2025)
  Giorgos Armeniakos et al.
  1.44
• S-Crescendo: A Nested Transformer Weaving Framework for Scalable Nonlinear System in S-Domain Representation (2025)
  Junlang Huang et al.
  1.22
• Kolmogorov-Arnold Network for Transistor Compact Modeling (2025)
  Rodion Novkin and Hussam Amrouch
  1.11
• DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining (2025)
  Sungyoung Lee et al.
  1.06
• Cauchy activation function and XNet (2024)
  Xin Li et al.
  0.83
• Enhancing Computational Efficiency in Multiscale Systems Using Deep Learning of Coordinates and Flow Maps (2024)
  Asif Hamid et al.
  0.72
• Sum-of-Parts: Self-Attributing Neural Networks with End-to-End Learning of Feature Groups (2023)
  Weiqiu You et al.
  0.22
• Moderate Adaptive Linear Units (MoLU) (2023)
  Hankyul Koh et al.
  0.00
• Simulated annealing for optimization of graphs and sequences (2021)
  Xianggen Liu et al.
  —
• Cross-Layer Approximation For Printed Machine Learning Circuits (2022)
  Giorgos Armeniakos et al.
  —
• Generative Pre-Trained Transformers for Biologically Inspired Design (2022)
  Qihao Zhu et al.
  —
• Evolution strategies: Application in hybrid quantum-classical neural networks (2022)
  Lucas Friedrich and Jonas Maziero
  —
• How important are activation functions in regression and classification? A survey, performance comparison, and future directions (2022)
  Ameya D. Jagtap and George Em Karniadakis
  —
• CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning (2023)
  Nihal Sanjay Singh et al.
  —
• A Deep-Genetic Algorithm (Deep-GA) Approach for High-Dimensional Nonlinear Parabolic Partial Differential Equations (2023)
  Endah Rokhmati Merdika Putri et al.
  —
• Stacked tensorial neural networks for reduced-order modeling of a parametric partial differential equation (2023)
  Caleb G. Wagner
  —
• Towards Physical Plausibility in Neuroevolution Systems (2024)
  Gabriel Cort\^es et al.
  —
• Architectural Implications of Neural Network Inference for High Data-Rate, Low-Latency Scientific Applications (2024)
  Olivia Weng et al.
  —
• Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation (2024)
  Sidharth SS et al.
  —
• An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification (2024)
  Ibrahim Al-Hurani et al.
  —
• Circuit design in biology and machine learning. I. Random networks and dimensional reduction (2024)
  Steven A. Frank
  —
• Circuit design in biology and machine learning. II. Anomaly detection (2024)
  Steven A. Frank
  —
• Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacity (2024)
  Samuel Yen-Chi Chen
  —