{"total":46,"rows":[{"id":"2507.10524","title":"Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation","created":"2025-07-14","cited_by_count":133,"area":null},{"id":"2407.15017","title":"Knowledge Mechanisms in Large Language Models: A Survey and Perspective","created":"2024-07-22","cited_by_count":51,"area":null},{"id":"2409.11593","title":"Self-Contrastive Forward-Forward Algorithm","created":"2024-09-17","cited_by_count":17,"area":null},{"id":"2406.06482","title":"Quantum Equilibrium Propagation for efficient training of quantum systems based on Onsager reciprocity","created":"2024-06-10","cited_by_count":9,"area":null},{"id":"2510.07739","title":"MeSH: Memory-as-State-Highways for Recursive Transformers","created":"2025-10-09","cited_by_count":8,"area":null},{"id":"2406.12774","title":"Towards Exact Gradient-based Training on Analog In-memory Computing","created":"2024-06-18","cited_by_count":7,"area":null},{"id":"2506.05259","title":"Learning long range dependencies through time reversal symmetry breaking","created":"2025-06-05","cited_by_count":7,"area":null},{"id":"2506.23800","title":"Towards the Training of Deeper Predictive Coding Neural Networks","created":"2025-06-30","cited_by_count":7,"area":null},{"id":"2502.06309","title":"Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions","created":"2025-02-10","cited_by_count":5,"area":null},{"id":"2409.03306","title":"Towards training digitally-tied analog blocks via hybrid gradient computation","created":"2024-09-05","cited_by_count":4,"area":null},{"id":"2409.12965","title":"Streamlined optical training of large-scale modern deep learning architectures with direct feedback alignment","created":"2024-09-01","cited_by_count":3,"area":null},{"id":"2510.16084","title":"Near-Equilibrium Propagation training in nonlinear wave systems","created":"2025-10-17","cited_by_count":3,"area":null},{"id":"2606.03584","title":"Training a Predictive Coding Network on ImageNet using Equilibrium Propagation","created":"2026-06-02","cited_by_count":3,"area":null},{"id":"2502.19053","title":"Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks","created":"2025-02-26","cited_by_count":2,"area":null},{"id":"2509.00747","title":"Self-Organising Memristive Networks as Physical Learning Systems","created":"2025-08-31","cited_by_count":2,"area":null},{"id":"2602.07697","title":"On the Infinite Width and Depth Limits of Predictive Coding Networks","created":"2026-02-07","cited_by_count":2,"area":null},{"id":"2507.21984","title":"Higher-Order Kuramoto Oscillator Network for Dense Associative Memory","created":"2025-07-29","cited_by_count":1,"area":null},{"id":"2509.15113","title":"Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers","created":"2025-09-18","cited_by_count":1,"area":null},{"id":"2602.09569","title":"Training deep physical neural networks with local physical information bottleneck","created":"2026-02-10","cited_by_count":1,"area":null},{"id":"2603.24790","title":"Local learning for stable backpropagation-free neural network training towards physical learning","created":"2026-03-25","cited_by_count":1,"area":null},{"id":"2604.27911","title":"Physical Foundation Models: Fixed hardware implementations of large-scale neural networks","created":"2026-04-30","cited_by_count":1,"area":null},{"id":"2609.36584","title":"Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters","created":"2026-09-29","cited_by_count":1,"area":null},{"id":"2501.15403","title":"Scaling of hardware-compatible perturbative training algorithms","created":"2025-01-26","cited_by_count":0,"area":null},{"id":"2503.09980","title":"Thermodynamic cost of inference and learning in physical neural networks","created":"2025-03-13","cited_by_count":0,"area":null},{"id":"2503.22810","title":"Harnessing uncertainty when learning through Equilibrium Propagation in neural networks","created":"2025-03-28","cited_by_count":0,"area":null},{"id":"2505.11659","title":"Programmable metasurfaces for future photonic artificial intelligence","created":"2025-05-16","cited_by_count":0,"area":null},{"id":"2505.18579","title":"Mechanical in-sensor computing: a programmable meta-sensor for structural damage classification without external electronic power","created":"2025-05-24","cited_by_count":0,"area":null},{"id":"2506.12210","title":"Machine Intelligence on Wireless Edge Networks","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2508.19015","title":"Learning with springs and sticks","created":"2025-08-26","cited_by_count":0,"area":null},{"id":"2510.17849","title":"Neural networks for neurocomputing circuits: a computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2511.17825","title":"Analog Physical Systems Can Exhibit Double Descent","created":"2025-11-21","cited_by_count":0,"area":null},{"id":"2601.18047","title":"Laser interferometry as a robust neuromorphic platform for machine learning","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2602.07518","title":"Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units","created":"2026-02-07","cited_by_count":0,"area":null},{"id":"2604.10272","title":"The Phase Is the Gradient: Equilibrium Propagation for Frequency Learning in Kuramoto Networks","created":"2026-04-11","cited_by_count":0,"area":null},{"id":"2604.10861","title":"Training single-electron and single-photon stochastic physical neural networks","created":"2026-04-12","cited_by_count":0,"area":null},{"id":"2605.07810","title":"Pre-training Enables Extraordinary All-optical Image Denoising","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.10008","title":"Measurement-Adapted Eigentask Representations for Photon-Limited Optical Readout","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2606.09756","title":"Perturbative Contrastive Physical Learning","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2606.13454","title":"Optical Implementation of Equilibrium Propagation Using Spatial Photonic Ising Machines","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2606.18275","title":"A physical adaptive material motor unit neural network: a hygromorph composite material machine","created":"2026-06-05","cited_by_count":0,"area":null},{"id":"2607.02608","title":"Towards transferable lightweight neuromorphic computing through a model-free temporal-switch framework","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2607.18354","title":"Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2609.32350","title":"Analog-Friendly Predictive Coding without Activation Derivatives","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.37525","title":"Physical Muon: Orthogonalization as an Equilibrium Computation","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.02683","title":"RAOA: Alternating-Operator Neural Computation with Programmable Radio Propagation","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.12571","title":"An all-transistor physical-learning transformer can be trained to perform in-context learning","created":"2026-10-08","cited_by_count":0,"area":null}],"next_cursor":null}