:: Library Catalog

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Furkejuvvon:

Bibliográfalaš dieđut
Váldodahkkit:	Greydanus, Sam, Kobak, Dmitry
Materiálatiipa:	Preprint
Almmustuhtton:	2020
Fáttát:	Machine Learning Neural and Evolutionary Computing
Liŋkkat:	https://arxiv.org/abs/2011.14439
Fáddágilkorat:	Lasit fáddágilkoriid Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!

Geahča maid

1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting
Dahkki: Zhang, Cheng, et al.
Almmustuhtton: (2023)

Modular Duality in Deep Learning
Dahkki: Bernstein, Jeremy, et al.
Almmustuhtton: (2024)

Deep Learning-Based Operators for Evolutionary Algorithms
Dahkki: Shem-Tov, Eliad, et al.
Almmustuhtton: (2024)

Multi-parameter Control for the $(1+(λ,λ))$-GA on OneMax via Deep Reinforcement Learning
Dahkki: Nguyen, Tai, et al.
Almmustuhtton: (2025)

Effects of Introducing Synaptic Scaling on Spiking Neural Network Learning
Dahkki: Touda, Shinnosuke, et al.
Almmustuhtton: (2026)

Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
Dahkki: Yang, Yibo, et al.
Almmustuhtton: (2024)

Deep Intrinsic Surprise-Regularized Control (DISRC): A Biologically Inspired Mechanism for Efficient Deep Q-Learning in Sparse Environments
Dahkki: Kini, Yash, et al.
Almmustuhtton: (2026)

APALU: A Trainable, Adaptive Activation Function for Deep Learning Networks
Dahkki: Subramanian, Barathi, et al.
Almmustuhtton: (2024)

SwarmFusion: Revolutionizing Disaster Response with Swarm Intelligence and Deep Learning
Dahkki: Lankipalle, Vasavi
Almmustuhtton: (2025)

AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
Dahkki: Mohapatra, Rohan, et al.
Almmustuhtton: (2020)

A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
Dahkki: Younesi, Abolfazl, et al.
Almmustuhtton: (2024)

Surrogate Ensemble in Expensive Multi-Objective Optimization via Deep Q-Learning
Dahkki: Wu, Yuxin, et al.
Almmustuhtton: (2026)

Rethinking Deep Learning: Non-backpropagation and Non-optimization Machine Learning Approach Using Hebbian Neural Networks
Dahkki: Itoh, Kei
Almmustuhtton: (2024)

DDCL: Deep Dual Competitive Learning: A Differentiable End-to-End Framework for Unsupervised Prototype-Based Representation Learning
Dahkki: Cirrincione, Giansalvo
Almmustuhtton: (2026)

Stable and Robust Deep Learning By Hyperbolic Tangent Exponential Linear Unit (TeLU)
Dahkki: Fernandez, Alfredo, et al.
Almmustuhtton: (2024)

Predicting the Stay Length of Patients in Hospitals using Convolutional Gated Recurrent Deep Learning Model
Dahkki: Neshat, Mehdi, et al.
Almmustuhtton: (2024)

Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model
Dahkki: Zhou, Yonghang, et al.
Almmustuhtton: (2024)

The Deep Learning model of Higher-Lower-Order Cognition, Memory, and Affection- More General Than KAN
Dahkki: Tao, Jun-Bo, et al.
Almmustuhtton: (2022)

Scale-covariant spiking wavelets
Dahkki: Pedersen, Jens Egholm, et al.
Almmustuhtton: (2026)

Time Series Forecasting of HIV/AIDS in the Philippines Using Deep Learning: Does COVID-19 Epidemic Matter?
Dahkki: Aribe Jr., Sales G., et al.
Almmustuhtton: (2024)

A Novel Denoising Technique and Deep Learning Based Hybrid Wind Speed Forecasting Model for Variable Terrain Conditions
Dahkki: Malakar, Sourav, et al.
Almmustuhtton: (2024)

Scaling can lead to compositional generalization
Dahkki: Redhardt, Florian, et al.
Almmustuhtton: (2025)

Predictive Coding-based Deep Neural Network Fine-tuning for Computationally Efficient Domain Adaptation
Dahkki: Cardoni, Matteo, et al.
Almmustuhtton: (2025)

STEMNIST: Spiking Tactile Extended MNIST Neuromorphic Dataset
Dahkki: Tripathi, Anubhab, et al.
Almmustuhtton: (2026)

Deep Pulse-Coupled Neural Networks
Dahkki: Yi, Zexiang, et al.
Almmustuhtton: (2023)

Generalising E-prop to Deep Networks
Dahkki: Millidge, Beren
Almmustuhtton: (2025)

Scaling Equilibrium Propagation to Deeper Neural Network Architectures
Dahkki: Elayedam, Sankar Vinayak, et al.
Almmustuhtton: (2025)

Exploiting Chaotic Dynamics as Deep Neural Networks
Dahkki: Liu, Shuhong, et al.
Almmustuhtton: (2024)

Hierarchical Kernel Transformer: Multi-Scale Attention with an Information-Theoretic Approximation Analysis
Dahkki: Cirrincione, Giansalvo
Almmustuhtton: (2026)

Auto-Configured Networks for Multi-Scale Multi-Output Time-Series Forecasting
Dahkki: Zha, Yumeng, et al.
Almmustuhtton: (2026)

Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference
Dahkki: Li, Boyan, et al.
Almmustuhtton: (2023)

Self-Supervised Neural Architecture Search for Multimodal Deep Neural Networks
Dahkki: Suzuki, Shota, et al.
Almmustuhtton: (2025)

Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics
Dahkki: Hayes, Conor F., et al.
Almmustuhtton: (2025)

HEATACO: Heatmap-Guided Ant Colony Decoding for Large-Scale Travelling Salesman Problems
Dahkki: Lin, Bo-Cheng, et al.
Almmustuhtton: (2026)

Dynamically Weighted Momentum with Adaptive Step Sizes for Efficient Deep Network Training
Dahkki: Wang, Zhifeng, et al.
Almmustuhtton: (2025)

Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions
Dahkki: Velikanov, Maksim, et al.
Almmustuhtton: (2022)

Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
Dahkki: Yu, Chengting, et al.
Almmustuhtton: (2024)

Reservoir Computing via Multi-Scale Random Fourier Features for Forecasting Fast-Slow Dynamical Systems
Dahkki: Laha, S. K.
Almmustuhtton: (2025)

GT-SNT: A Linear-Time Transformer for Large-Scale Graphs via Spiking Node Tokenization
Dahkki: Zhang, Huizhe, et al.
Almmustuhtton: (2025)

Univariate Radial Basis Function Layers: Brain-inspired Deep Neural Layers for Low-Dimensional Inputs
Dahkki: Jost, Daniel, et al.
Almmustuhtton: (2023)