:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Dalm, Sander, Offergeld, Joshua, Ahmad, Nasir, van Gerven, Marcel
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2405.02385
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning
by: Küçükoğlu, Burcu, et al.
Published: (2025)

Node Perturbation Can Effectively Train Multi-Layer Neural Networks
by: Dalm, Sander, et al.
Published: (2023)

Subspace Node Pruning
by: Offergeld, Joshua, et al.
Published: (2024)

Decorrelation Speeds Up Vision Transformers
by: Carrigg, Kieran, et al.
Published: (2025)

Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines
by: Fernandez, Jesus Garcia, et al.
Published: (2024)

A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptation
by: Fernández, Jesús García, et al.
Published: (2025)

Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
by: Küçükoğlu, Burcu, et al.
Published: (2022)

Noise-based reward-modulated learning
by: Fernández, Jesús García, et al.
Published: (2025)

Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Series
by: Sadough, Amirhossein, et al.
Published: (2025)

Gradient-Free Training of Recurrent Neural Networks using Random Perturbations
by: Fernandez, Jesus Garcia, et al.
Published: (2024)

Discovering Continuous-Time Memory-Based Symbolic Policies using Genetic Programming
by: de Vries, Sigur, et al.
Published: (2024)

Energy-Efficient Spiking Recurrent Neural Network for Gesture Recognition on Embedded GPUs
by: Varposhti, Marzieh Hassanshahi, et al.
Published: (2024)

Neural Co-state Policies: Structuring Hidden States in Recurrent Reinforcement Learning
by: Leeftink, David, et al.
Published: (2026)

Probabilistic Forecasting via Autoregressive Flow Matching
by: ElGazzar, Ahmed, et al.
Published: (2025)

Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations
by: ElGazzar, Ahmed, et al.
Published: (2024)

Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions
by: Koenders, Kees, et al.
Published: (2024)

Fractional Order Distributed Optimization
by: Lixandru, Andrei, et al.
Published: (2024)

Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learning
by: Huebotter, Justus, et al.
Published: (2025)

Generative Modeling of Clinical Time Series via Latent Stochastic Differential Equations
by: Aslanimoghanloo, Muhammad, et al.
Published: (2025)

Optimal Control of Probabilistic Dynamics Models via Mean Hamiltonian Minimization
by: Leeftink, David, et al.
Published: (2025)

Correlations Are Ruining Your Gradient Descent
by: Ahmad, Nasir
Published: (2024)

Factor Decorrelation Enhanced Data Removal from Deep Predictive Models
by: Yang, Wenhao, et al.
Published: (2025)

Efficient Backpropagation with Variance-Controlled Adaptive Sampling
by: Wang, Ziteng, et al.
Published: (2024)

Stochastic Layer-wise Learning: Scalable and Efficient Alternative to Backpropagation
by: Yin, Bojian, et al.
Published: (2025)

Unbiased Approximate Vector-Jacobian Products for Efficient Backpropagation
by: Bakong, Killian, et al.
Published: (2026)

Stability Selection via Variable Decorrelation
by: Nouraie, Mahdi, et al.
Published: (2025)

Rethinking Deep Learning: Propagating Information in Neural Networks without Backpropagation and Statistical Optimization
by: Itoh, Kei
Published: (2024)

Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching
by: Rogalla, Nicole, et al.
Published: (2026)

Spiking neurons as predictive controllers of linear systems
by: Agliati, Paolo, et al.
Published: (2025)

CCFC++: Enhancing Federated Clustering through Feature Decorrelation
by: Yan, Jie, et al.
Published: (2024)

Multimodal Integration of Longitudinal Noninvasive Diagnostics for Survival Prediction in Immunotherapy Using Deep Learning
by: Yeghaian, Melda, et al.
Published: (2024)

Decorrelating the Future: Joint Frequency Domain Learning for Spatio-temporal Forecasting
by: Wang, Zepu, et al.
Published: (2026)

Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism
by: Erdogan, Mete, et al.
Published: (2025)

Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Evaluation of Forward-Only Algorithms
by: Spyra, Przemysław, et al.
Published: (2025)

Backpropagation Neural Tree
by: Ojha, Varun, et al.
Published: (2022)

The Cost of Avoiding Backpropagation
by: Panchal, Kunjal, et al.
Published: (2025)

Decorrelative Network Architecture for Robust Electrocardiogram Classification
by: Wiedeman, Christopher, et al.
Published: (2022)

Beyond Backpropagation: Exploring Innovative Algorithms for Energy-Efficient Deep Neural Network Training
by: Spyra, Przemysław
Published: (2025)

Dynamic Spectral Backpropagation for Efficient Neural Network Training
by: Muthuraman, Mannmohan
Published: (2025)

Decorrelated feature importance from local sample weighting
by: Fröhlich, Benedikt, et al.
Published: (2025)