Tootfinder

This https://arxiv.org/abs/2502.20411 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm
Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm that emulates neuronal activity through discrete spike-based processing. Despite their advantages, training SNNs with traditional backpropagation (BP) remains challenging due to computational inefficiencies and a lack of biological plausibility. This study explores the Forward-Forward (FF) algorithm as an alternative learning framework for SNNs. Unlike backpropagation, which relies on forward and backward passes…

This https://arxiv.org/abs/2502.20411 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Backpropagation-free Spiking Neural Networks with the Forward-Forward Algorithm
Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm that emulates neuronal activity through discrete spike-based processing. Despite their advantages, training SNNs with traditional backpropagation (BP) remains challenging due to computational inefficiencies and a lack of biological plausibility. This study explores the Forward-Forward (FF) algorithm as an alternative learning framework for SNNs. Unlike backpropagation, which relies on forward and backward passes…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Backpropagation Through Time For Networks With Long-Term Dependencies
George Bird, Maxim E. Polivoda

Advanced For-Loop for QML algorithm search
FuTe Wong
https://arxiv.org/abs/2506.18260 https://arxiv.org/pdf/2506.18260

Advanced For-Loop for QML algorithm search
This paper introduces an advanced framework leveraging Large Language Model-based Multi-Agent Systems (LLMMA) for the automated search and optimization of Quantum Machine Learning (QML) algorithms. Inspired by Google DeepMind's FunSearch, the proposed system works on abstract level to iteratively generates and refines quantum transformations of classical machine learning algorithms (concepts), such as the Multi-Layer Perceptron, forward-forward and backpropagation algorithms. As a proof of conc…

Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder
Ioannis Christoforos Koune, Alice Cicirello
https://arxiv.org/abs/2506.13658

Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder
Inference and prediction under partial knowledge of a physical system is challenging, particularly when multiple confounding sources influence the measured response. Explicitly accounting for these influences in physics-based models is often infeasible due to epistemic uncertainty, cost, or time constraints, resulting in models that fail to accurately describe the behavior of the system. On the other hand, data-driven machine learning models such as variational autoencoders are not guaranteed t…

Integrated photonic deep neural network with end-to-end on-chip backpropagation training
Farshid Ashtiani, Mohamad Hossein Idjadi, Kwangwoong Kim
https://arxiv.org/abs/2506.14575 …

Integrated photonic deep neural network with end-to-end on-chip backpropagation training
Integrated photonic neural networks (PNNs) have demonstrated significant potential to complement the digital electronic counterparts [1-3]. Nevertheless, robust and repeatable performance of scalable integrated PNNs is directly tied to the quality of their training. Error backpropagation (BP), which relies on nonlinear activation gradient computation, is the mainstream algorithm to train digital neural networks due to its scalability, versatility, and implementation efficiency [4]. Consequently…

Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
Oscar Boullosa Dapena
https://arxiv.org/abs/2506.12111

Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
Real-time continuous learning over streaming data remains a central challenge in deep learning and AI systems. Traditional gradient-based models such as backpropagation through time (BPTT) face computational and stability limitations when dealing with temporally unbounded data. In this paper, we introduce a novel architecture, Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs), which leverages the Feynman technique of differentiation under the integral sign to formulate neural u…

Gradients of unitary optical neural networks using parameter-shift rule
Jinzhe Jiang, Yaqian Zhao, Xin Zhang, Chen Li, Yunlong Yu, Hailing Liu
https://arxiv.org/abs/2506.11565

Gradients of unitary optical neural networks using parameter-shift rule
This paper explores the application of the parameter-shift rule (PSR) for computing gradients in unitary optical neural networks (UONNs). While backpropagation has been fundamental to training conventional neural networks, its implementation in optical neural networks faces significant challenges due to the physical constraints of optical systems. We demonstrate how PSR, which calculates gradients by evaluating functions at shifted parameter values, can be effectively adapted for training UONNs…

This https://arxiv.org/abs/2505.00533 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Test-time Correlation Alignment
Deep neural networks often degrade under distribution shifts. Although domain adaptation offers a solution, privacy constraints often prevent access to source data, making Test-Time Adaptation (TTA, which adapts using only unlabeled test data) increasingly attractive. However, current TTA methods still face practical challenges: (1) a primary focus on instance-wise alignment, overlooking CORrelation ALignment (CORAL) due to missing source correlations; (2) complex backpropagation operations for…

This https://arxiv.org/abs/2412.06481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization
This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparameter tuning of the DeePC algorithm. The necessity for such a method arises from the importance of hyperparameter selection to achieve satisfactory closed-loop DeePC performance. The standard methods for hyperparameter selection are to either optimize the open-loop performance, or use manual guess-and-check. Optimizing th…

Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data
Douwe den Blanken, Charlotte Frenkel
https://arxiv.org/abs/2505.24852

Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data
On-device learning at the edge enables low-latency, private personalization with improved long-term robustness and reduced maintenance costs. Yet, achieving scalable, low-power end-to-end on-chip learning, especially from real-world sequential data with a limited number of examples, is an open challenge. Indeed, accelerators supporting error backpropagation optimize for learning performance at the expense of inference efficiency, while simplified learning algorithms often fail to reach acceptab…

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks
Maximilian Baronig, Yeganeh Bahariasl, Ozan \"Ozdenizci, Robert Legenstein
https://arxiv.org/abs/2506.14464

A Scalable Hybrid Training Approach for Recurrent Spiking Neural Networks
Recurrent spiking neural networks (RSNNs) can be implemented very efficiently in neuromorphic systems. Nevertheless, training of these models with powerful gradient-based learning algorithms is mostly performed on standard digital hardware using Backpropagation through time (BPTT). However, BPTT has substantial limitations. It does not permit online training and its memory consumption scales linearly with the number of computation steps. In contrast, learning methods using forward propagation o…

Neural Jumps for Option Pricing
Duosi Zheng, Hanzhong Guo, Yanchu Liu, Wei Huang
https://arxiv.org/abs/2506.05137 https://arxiv.org/p…

Neural Jumps for Option Pricing
Recognizing the importance of jump risk in option pricing, we propose a neural jump stochastic differential equation model in this paper, which integrates neural networks as parameter estimators in the conventional jump diffusion model. To overcome the problem that the backpropagation algorithm is not compatible with the jump process, we use the Gumbel-Softmax method to make the jump parameter gradient learnable. We examine the proposed model using both simulated data and S&P 500 index options.…

Gradient Similarity Surgery in Multi-Task Deep Learning
Thomas Borsani, Andrea Rosani, Giuseppe Nicosia, Giuseppe Di Fatta
https://arxiv.org/abs/2506.06130

Gradient Similarity Surgery in Multi-Task Deep Learning
The multi-task learning ($MTL$) paradigm aims to simultaneously learn multiple tasks within a single model capturing higher-level, more general hidden patterns that are shared by the tasks. In deep learning, a significant challenge in the backpropagation training process is the design of advanced optimisers to improve the convergence speed and stability of the gradient descent learning rule. In particular, in multi-task deep learning ($MTDL$) the multitude of tasks may generate potentially conf…

This https://arxiv.org/abs/2412.06481 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization
This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparameter tuning of the DeePC algorithm. The necessity for such a method arises from the importance of hyperparameter selection to achieve satisfactory closed-loop DeePC performance. The standard methods for hyperparameter selection are to either optimize the open-loop performance, or use manual guess-and-check. Optimizing th…

This https://arxiv.org/abs/2501.09976 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Dendritic Localized Learning: Toward Biologically Plausible Algorithm
Backpropagation is the foundational algorithm for training neural networks and a key driver of deep learning's success. However, its biological plausibility has been challenged due to three primary limitations: weight symmetry, reliance on global error signals, and the dual-phase nature of training, as highlighted by the existing literature. Although various alternative learning approaches have been proposed to address these issues, most either fail to satisfy all three criteria simultaneously …

Spatio-Temporal Decoupled Learning for Spiking Neural Networks
Chenxiang Ma, Xinyi Chen, Kay Chen Tan, Jibin Wu
https://arxiv.org/abs/2506.01117 https://…

Spatio-Temporal Decoupled Learning for Spiking Neural Networks
Spiking neural networks (SNNs) have gained significant attention for their potential to enable energy-efficient artificial intelligence. However, effective and efficient training of SNNs remains an unresolved challenge. While backpropagation through time (BPTT) achieves high accuracy, it incurs substantial memory overhead. In contrast, biologically plausible local learning methods are more memory-efficient but struggle to match the accuracy of BPTT. To bridge this gap, we propose spatio-tempora…