Tootfinder

Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation
Donghwa Kang, Doohyun Kim, Sang-Ki Ko, Jinkyu Lee, Hyeongboo Baek, Brent ByungHoon Kang
https://arxiv.org/abs/2508.13812

Timestep-Compressed Attack on Spiking Neural Networks through Timestep-Level Backpropagation
State-of-the-art (SOTA) gradient-based adversarial attacks on spiking neural networks (SNNs), which largely rely on extending FGSM and PGD frameworks, face a critical limitation: substantial attack latency from multi-timestep processing, rendering them infeasible for practical real-time applications. This inefficiency stems from their design as direct extensions of ANN paradigms, which fail to exploit key SNN properties. In this paper, we propose the timestep-compressed attack (TCA), a novel fr…

Training nonlinear optical neural networks with Scattering Backpropagation
Nicola Dal Cin, Florian Marquardt, Clara C. Wanjura
https://arxiv.org/abs/2508.11750 https://

Training nonlinear optical neural networks with Scattering Backpropagation
As deep learning applications continue to deploy increasingly large artificial neural networks, the associated high energy demands are creating a need for alternative neuromorphic approaches. Optics and photonics are particularly compelling platforms as they offer high speeds and energy efficiency. Neuromorphic systems based on nonlinear optics promise high expressivity with a minimal number of parameters. However, so far, there is no efficient and generic physics-based training method allowing…

A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Zhengyu Ma, Huihui Zhou, Yonghong Tian, Zhouchen Lin
https://arxiv.org/abs/2508.12609

A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
Spiking Neural Networks (SNNs) are a promising approach to low-power applications on neuromorphic hardware due to their energy efficiency. However, training SNNs is challenging because of the non-differentiable spike generation function. To address this issue, the commonly used approach is to adopt the backpropagation through time framework, while assigning the gradient of the non-differentiable function with some surrogates. Similarly, Binary Neural Networks (BNNs) also face the non-differenti…

Crosslisted article(s) found for cond-mat.mes-hall. https://arxiv.org/list/cond-mat.mes-hall/new
[1/1]:
- Training nonlinear optical neural networks with Scattering Backpropagation
Nicola Dal Cin, Florian Marquardt, Clara C. Wanjura

StableTracker: Learning to Stably Track Target via Differentiable Simulation
Fanxing Li, Shengyang Wang, Fangyu Sun, Shuyu Wu, Dexin Zuo, Wenxian Yu, Danping Zou
https://arxiv.org/abs/2509.14147

StableTracker: Learning to Stably Track Target via Differentiable Simulation
FPV object tracking methods heavily rely on handcraft modular designs, resulting in hardware overload and cumulative error, which seriously degrades the tracking performance, especially for rapidly accelerating or decelerating targets. To address these challenges, we present \textbf{StableTracker}, a learning-based control policy that enables quadrotors to robustly follow the moving target from arbitrary perspectives. The policy is trained using backpropagation-through-time via differentiable s…

Crosslisted article(s) found for cond-mat.dis-nn. https://arxiv.org/list/cond-mat.dis-nn/new
[1/1]:
- Training nonlinear optical neural networks with Scattering Backpropagation
Nicola Dal Cin, Florian Marquardt, Clara C. Wanjura

Biologically Plausible Online Hebbian Meta-Learning: Two-Timescale Local Rules for Spiking Neural Brain Interfaces
Sriram V. C. Nallani, Gautham Ramachandran, Sahil S. Shah
https://arxiv.org/abs/2509.14447

Biologically Plausible Online Hebbian Meta-Learning: Two-Timescale Local Rules for Spiking Neural Brain Interfaces
Brain-Computer Interfaces face challenges from neural signal instability and memory constraints for real-time implantable applications. We introduce an online SNN decoder using local three-factor learning rules with dual-timescale eligibility traces that avoid backpropagation through time while maintaining competitive performance. Our approach combines error-modulated Hebbian updates, fast/slow trace consolidation, and adaptive learning rate control, requiring only O(1) memory versus O(T) for B…

Noise-Level Diffusion Guidance: Well Begun is Half Done
Harvey Mannering, Zhiwu Huang, Adam Prugel-Bennett
https://arxiv.org/abs/2509.13936 https://arxiv.o…

Noise-Level Diffusion Guidance: Well Begun is Half Done
Diffusion models have achieved state-of-the-art image generation. However, the random Gaussian noise used to start the diffusion process influences the final output, causing variations in image quality and prompt adherence. Existing noise-level optimization approaches generally rely on extra dataset construction, additional networks, or backpropagation-based optimization, limiting their practicality. In this paper, we propose Noise Level Guidance (NLG), a simple, efficient, and general noise-le…

Reshaping the Forward-Forward Algorithm with a Similarity-Based Objective
James Gong, Raymond Luo, Emma Wang, Leon Ge, Bruce Li, Felix Marattukalam, Waleed Abdulla
https://arxiv.org/abs/2509.08697

Reshaping the Forward-Forward Algorithm with a Similarity-Based Objective
Backpropagation is the pivotal algorithm underpinning the success of artificial neural networks, yet it has critical limitations such as biologically implausible backward locking and global error propagation. To circumvent these constraints, the Forward-Forward algorithm was proposed as a more biologically plausible method that replaces the backward pass with an additional forward pass. Despite this advantage, the Forward-Forward algorithm significantly trails backpropagation in accuracy, and i…

A High-order Backpropagation Algorithm for Neural Stochastic Differential Equation Model
Daili Sheng, Minghui Song, Xiang Peng, Xuanqi Dong
https://arxiv.org/abs/2509.06292 http…

A High-order Backpropagation Algorithm for Neural Stochastic Differential Equation Model
Neural stochastic differential equation model with a Brownian motion term can capture epistemic uncertainty of deep neural network from the perspective of a dynamical system. The goal of this paper is to improve the convergence rate of the sample-wise backpropagation algorithm in neural stochastic differential equation model which has been proposed in [Archibald et al., SIAM Journal on Numerical Analysis, 62 (2024), pp. 593-621]. It is necessary to emphasize that, improving the convergence orde…

Bio-Inspired Artificial Neural Networks based on Predictive Coding
Davide Casnici, Charlotte Frenkel, Justin Dauwels
https://arxiv.org/abs/2508.08762 https://

Bio-Inspired Artificial Neural Networks based on Predictive Coding
Backpropagation (BP) of errors is the backbone training algorithm for artificial neural networks (ANNs). It updates network weights through gradient descent to minimize a loss function representing the mismatch between predictions and desired outputs. BP uses the chain rule to propagate the loss gradient backward through the network hierarchy, allowing efficient weight updates. However, this process requires weight updates at every layer to rely on a global error signal generated at the network…

Forward-Forward Autoencoder Architectures for Energy-Efficient Wireless Communications
Daniel Seifert, Onur G\"unl\"u, Rafael F. Schaefer
https://arxiv.org/abs/2510.11418

Forward-Forward Autoencoder Architectures for Energy-Efficient Wireless Communications
The application of deep learning to the area of communications systems has been a growing field of interest in recent years. Forward-forward (FF) learning is an efficient alternative to the backpropagation (BP) algorithm, which is the typically used training procedure for neural networks. Among its several advantages, FF learning does not require the communication channel to be differentiable and does not rely on the global availability of partial derivatives, allowing for an energy-efficient i…

End-to-end Training of High-Dimensional Optimal Control with Implicit Hamiltonians via Jacobian-Free Backpropagation
Eric Gelphman, Deepanshu Verma, Nicole Tianjiao Yang, Stanley Osher, Samy Wu Fung
https://arxiv.org/abs/2510.00359

End-to-end Training of High-Dimensional Optimal Control with Implicit Hamiltonians via Jacobian-Free Backpropagation
Neural network approaches that parameterize value functions have succeeded in approximating high-dimensional optimal feedback controllers when the Hamiltonian admits explicit formulas. However, many practical problems, such as the space shuttle reentry problem and bicycle dynamics, among others, may involve implicit Hamiltonians that do not admit explicit formulas, limiting the applicability of existing methods. Rather than directly parameterizing controls, which does not leverage the Hamiltoni…

Backpropagation in unstable diffusions
Angxiu Ni
https://arxiv.org/abs/2507.21497 https://arxiv.org/pdf/2507.21497

Backpropagation in unstable diffusions
We derive the adjoint path-kernel method for the parameter-gradient of SDEs, where the observable is averaged at a particular time or over the stationary measure. Its cost is almost independent of the number of parameters; it extends the conventional backpropagation method to cases with gradient explosion. It works for non-hyperbolic systems with multiplicative noise controlled by parameters. We derive a Monte-Carlo-type algorithm and demonstrate it on the 40-dimensional Lorenz 96 system.

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/9]:
- Stochastic Layer-wise Learning: Scalable and Efficient Alternative to Backpropagation
Bojian Yin, Federico Corradi

A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
E. A. Dzhivelikian, A. I. Panov
https://arxiv.org/abs/2510.03286

A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
Cognitive maps provide a powerful framework for understanding spatial and abstract reasoning in biological and artificial agents. While recent computational models link cognitive maps to hippocampal-entorhinal mechanisms, they often rely on global optimization rules (e.g., backpropagation) that lack biological plausibility. In this work, we propose a novel cognitive architecture for structuring episodic memories into cognitive maps using local, Hebbian-like learning rules, compatible with neura…

Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment
Youjia Zhang, Youngeun Kim, Young-Geun Choi, Hongyeob Kim, Huiling Liu, Sungeun Hong
https://arxiv.org/abs/2508.15568

Backpropagation-Free Test-Time Adaptation via Probabilistic Gaussian Alignment
Test-time adaptation (TTA) enhances the zero-shot robustness under distribution shifts by leveraging unlabeled test data during inference. Despite notable advances, several challenges still limit its broader applicability. First, most methods rely on backpropagation or iterative optimization, which limits scalability and hinders real-time deployment. Second, they lack explicit modeling of class-conditional feature distributions. This modeling is crucial for producing reliable decision boundarie…

The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
Takehiro Ishikawa
https://arxiv.org/abs/2510.08591

The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
Recent advancements in QML and SNNs have generated considerable excitement, promising exponential speedups and brain-like energy efficiency to revolutionize AI. However, this paper argues that they are unlikely to displace DNNs in the near term. QML struggles with adapting backpropagation due to unitary constraints, measurement-induced state collapse, barren plateaus, and high measurement overheads, exacerbated by the limitations of current noisy intermediate-scale quantum hardware, overfitting…

PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization
Yiming Huang, Yajie Hao, Jing Zhou, Xiao Yuan, Xiaoting Wang, Yuxuan Du
https://arxiv.org/abs/2509.20733

PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization
Variational quantum algorithms (VQAs) are leading strategies to reach practical utilities of near-term quantum devices. However, the no-cloning theorem in quantum mechanics precludes standard backpropagation, leading to prohibitive quantum resource costs when applying VQAs to large-scale tasks. To address this challenge, we reformulate the training dynamics of VQAs as a nonlinear partial differential equation and propose a novel protocol that leverages physics-informed neural networks (PINNs) t…

Over-Barrier Ionization Dynamics Studied by Backpropagation
Yongzhe Ma, Qingcao Liu, Hongcheng Ni, Jian Wu
https://arxiv.org/abs/2509.02026 https://arxiv.o…

Learning Polynomial Activation Functions for Deep Neural Networks
Linghao Zhang, Jiawang Nie, Tingting Tang
https://arxiv.org/abs/2510.03682 https://arxiv.…

Learning Polynomial Activation Functions for Deep Neural Networks
Activation functions are crucial for deep neural networks. This novel work frames the problem of training neural network with learnable polynomial activation functions as a polynomial optimization problem, which is solvable by the Moment-SOS hierarchy. This work represents a fundamental departure from the conventional paradigm of training deep neural networks, which relies on local optimization methods like backpropagation and gradient descent. Numerical experiments are presented to demonstrate…

Partial Parameter Updates for Efficient Distributed Training
Anastasiia Filippova, Angelos Katharopoulos, David Grangier, Ronan Collobert
https://arxiv.org/abs/2509.22418 https:…

Partial Parameter Updates for Efficient Distributed Training
We introduce a memory- and compute-efficient method for low-communication distributed training. Existing methods reduce communication by performing multiple local updates between infrequent global synchronizations. We demonstrate that their efficiency can be significantly improved by restricting backpropagation: instead of updating all the parameters, each node updates only a fixed subset while keeping the remainder frozen during local steps. This constraint substantially reduces peak memory us…

When Pipelined In-Memory Accelerators Meet Spiking Direct Feedback Alignment: A Co-Design for Neuromorphic Edge Computing
Haoxiong Ren, Yangu He, Kwunhang Wong, Rui Bao, Ning Lin, Zhongrui Wang, Dashan Shang
https://arxiv.org/abs/2507.15603

When Pipelined In-Memory Accelerators Meet Spiking Direct Feedback Alignment: A Co-Design for Neuromorphic Edge Computing
Spiking Neural Networks (SNNs) are increasingly favored for deployment on resource-constrained edge devices due to their energy-efficient and event-driven processing capabilities. However, training SNNs remains challenging because of the computational intensity of traditional backpropagation algorithms adapted for spike-based systems. In this paper, we propose a novel software-hardware co-design that introduces a hardware-friendly training algorithm, Spiking Direct Feedback Alignment (SDFA) and…

Full Integer Arithmetic Online Training for Spiking Neural Networks
Ismael Gomez, Guangzhi Tang
https://arxiv.org/abs/2509.06636 https://arxiv.org/pdf/2509…

Full Integer Arithmetic Online Training for Spiking Neural Networks
Spiking Neural Networks (SNNs) are promising for neuromorphic computing due to their biological plausibility and energy efficiency. However, training methods like Backpropagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL) remain computationally intensive. This work introduces an integer-only, online training algorithm using a mixed-precision approach to improve efficiency and reduce memory usage by over 60%. The method replaces floating-point operations with integer arithmetic …

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[1/2]:
- Backpropagation scaling in parameterised quantum circuits
Joseph Bowles, David Wierichs, Chae-Yeun Park

Trainable Joint Time-Vertex Fractional Fourier Transform
Ziqi Yan, Zhichao Zhang
https://arxiv.org/abs/2507.21527 https://arxiv.org/pdf/2507.21527

Trainable Joint Time-Vertex Fractional Fourier Transform
To address limitations of the graph fractional Fourier transform (GFRFT) Wiener filtering and the traditional joint time-vertex fractional Fourier transform (JFRFT) Wiener filtering, this study proposes a filtering method based on the hyper-differential form of the JFRFT. The gradient backpropagation mechanism is employed to enable the adaptive selection of transform order pair and filter coefficients. First, leveraging the hyper-differential form of the GFRFT and the fractional Fourier transfo…

All-optical classification of real biomedical cell images using a diffractive neural network: a simulation study
Norihide Sagami, Yueyun Weng, Cheng Lei, Ryosuke Oketani, Kotaro Hiramatsu
https://arxiv.org/abs/2509.00370

All-optical classification of real biomedical cell images using a diffractive neural network: a simulation study
We report an in-silico demonstration of an all-optical cell classification system using a single-layer diffractive neural network (DNN) optimized for real-world biomedical images. Implemented virtually with a spatial light modulator (SLM), the DNN was numerically trained via backpropagation to differentiate breast and lung cancer cells. The training utilized experimentally acquired phase and amplitude images from optofluidic time-stretch quantitative phase imaging. Classification was simulated …

Predictive Coding-based Deep Neural Network Fine-tuning for Computationally Efficient Domain Adaptation
Matteo Cardoni, Sam Leroux
https://arxiv.org/abs/2509.20269 https://

Predictive Coding-based Deep Neural Network Fine-tuning for Computationally Efficient Domain Adaptation
As deep neural networks are increasingly deployed in dynamic, real-world environments, relying on a single static model is often insufficient. Changes in input data distributions caused by sensor drift or lighting variations necessitate continual model adaptation. In this paper, we propose a hybrid training methodology that enables efficient on-device domain adaptation by combining the strengths of Backpropagation and Predictive Coding. The method begins with a deep neural network trained offli…

Game-Theoretic Gradient Control for Robust Neural Network Training
Maria Zaitseva, Ivan Tomilov, Natalia Gusarova
https://arxiv.org/abs/2507.19143 https://…

Game-Theoretic Gradient Control for Robust Neural Network Training
Feed-forward neural networks (FFNNs) are vulnerable to input noise, reducing prediction performance. Existing regularization methods like dropout often alter network architecture or overlook neuron interactions. This study aims to enhance FFNN noise robustness by modifying backpropagation, interpreted as a multi-agent game, and exploring controlled target variable noising. Our "gradient dropout" selectively nullifies hidden layer neuron gradients with probability 1 - p during backpropagation, w…

Eig-PIELM: A Mesh-Free Approach for Efficient Eigen-Analysis with Physics-Informed Extreme Learning Machines
Rishi Mishra, Smriti, Ganapathy Krishnamurthi, Balaji Srinivasan, Sundararajan Natarajan
https://arxiv.org/abs/2508.15343

Eig-PIELM: A Mesh-Free Approach for Efficient Eigen-Analysis with Physics-Informed Extreme Learning Machines
In this work, a novel Eig-PIELM framework is proposed that extends physics-informed extreme learning machine for an efficient and accurate solution of linear eigenvalue problems. The method reformulates the governing differential equations into a compact algebraic system solvable in a single step. Boundary conditions are enforced exactly via an algebraic projection onto the boundary-admissible subspace, eliminating the computational overhead of penalty parameters, and backpropagation while pres…

Riemannian Optimization on Tree Tensor Networks with Application in Machine Learning
Marius Willner, Marco Trenti, Dirk Lebiedz
https://arxiv.org/abs/2507.21726 https://

Riemannian Optimization on Tree Tensor Networks with Application in Machine Learning
Tree tensor networks (TTNs) are widely used in low-rank approximation and quantum many-body simulation. In this work, we present a formal analysis of the differential geometry underlying TTNs. Building on this foundation, we develop efficient first- and second-order optimization algorithms that exploit the intrinsic quotient structure of TTNs. Additionally, we devise a backpropagation algorithm for training TTNs in a kernel learning setting. We validate our methods through numerical experiments…

Scaling Equilibrium Propagation to Deeper Neural Network Architectures
Sankar Vinayak. E. P, Gopalakrishnan Srinivasan
https://arxiv.org/abs/2509.26003 https://

Scaling Equilibrium Propagation to Deeper Neural Network Architectures
Equilibrium propagation has been proposed as a biologically plausible alternative to the backpropagation algorithm. The local nature of gradient computations, combined with the use of convergent RNNs to reach equilibrium states, make this approach well-suited for implementation on neuromorphic hardware. However, previous studies on equilibrium propagation have been restricted to networks containing only dense layers or relatively small architectures with a few convolutional layers followed by a…

DelRec: learning delays in recurrent spiking neural networks
Alexandre Queant, Ulysse Ran\c{c}on, Benoit R Cottereau, Timoth\'ee Masquelier
https://arxiv.org/abs/2509.24852 …

DelRec: learning delays in recurrent spiking neural networks
Spiking neural networks (SNNs) are a bio-inspired alternative to conventional real-valued deep learning models, with the potential for substantially higher energy efficiency. Interest in SNNs has recently exploded due to a major breakthrough: surrogate gradient learning (SGL), which allows training SNNs with backpropagation, strongly outperforming other approaches. In SNNs, each synapse is characterized not only by a weight but also by a transmission delay. While theoretical works have long sug…

Biologically Plausible Learning via Bidirectional Spike-Based Distillation
Changze Lv, Yifei Wang, Yanxun Zhang, Yiyang Lu, Jingwen Xu, Di Yu, Xin Du, Xuanjing Huang, Xiaoqing Zheng
https://arxiv.org/abs/2509.20284

Biologically Plausible Learning via Bidirectional Spike-Based Distillation
Developing biologically plausible learning algorithms that can achieve performance comparable to error backpropagation remains a longstanding challenge. Existing approaches often compromise biological plausibility by entirely avoiding the use of spikes for error propagation or relying on both positive and negative learning signals, while the question of how spikes can represent negative values remains unresolved. To address these limitations, we introduce Bidirectional Spike-based Distillation …