Cover Image

Annotated History of Modern AI and Deep Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Schmidhuber, Juergen
Format:	Preprint
Published:	2022
Subjects:	Neural and Evolutionary Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Deep Learning: Our Miraculous Year 1990-1991
by: Schmidhuber, Juergen
Published: (2020)

Who invented deep residual learning?
by: Schmidhuber, Juergen
Published: (2025)

Learning to Forget: Continual Learning with Adaptive Weight Decay
by: Ramesh, Aditya A., et al.
Published: (2026)

Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery
by: Gopalakrishnan, Anand, et al.
Published: (2024)

Evolutionary Optimization of Deep Learning Agents for Sparrow Mahjong
by: O'Connor, Jim, et al.
Published: (2025)

Connectome-Guided Automatic Learning Rates for Deep Networks
by: He, Peilin, et al.
Published: (2025)

Deep Reinforcement Learning-Assisted Automated Operator Portfolio for Constrained Multi-objective Optimization
by: Shao, Shuai, et al.
Published: (2026)

Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
by: Yazdani, Danial, et al.
Published: (2021)

SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention
by: Csordás, Róbert, et al.
Published: (2023)

Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
by: Xu, Qi, et al.
Published: (2024)

UAV-assisted Joint Mobile Edge Computing and Data Collection via Matching-enabled Deep Reinforcement Learning
by: Wang, Boxiong, et al.
Published: (2025)

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning
by: Sun, Geng, et al.
Published: (2025)

Deep Neural Crossover
by: Shem-Tov, Eliad, et al.
Published: (2024)

Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model
by: Yang, Xu, et al.
Published: (2025)

Effective Self-Attention-Based Deep Learning Model with Evolutionary Grid Search for Robust Wave Farm Energy Forecasting
by: Dehkordi, Amin Abdollahi, et al.
Published: (2025)

Low Rank Factorizations are Indirect Encodings for Deep Neuroevolution
by: Garbus, Jack, et al.
Published: (2025)

Integrating Pruning with Quantization for Efficient Deep Neural Networks Compression
by: Makenali, Sara, et al.
Published: (2025)

Competition-based Adaptive ReLU for Deep Neural Networks
by: Chen, Junjia, et al.
Published: (2024)

Deep Surrogate Assisted MAP-Elites for Automated Hearthstone Deckbuilding
by: Zhang, Yulun, et al.
Published: (2021)

In-Simulation Testing of Deep Learning Vision Models in Autonomous Robotic Manipulators
by: Humeniuk, Dmytro, et al.
Published: (2024)

Sustainable AI: Mathematical Foundations of Spiking Neural Networks
by: Fono, Adalbert, et al.
Published: (2025)

Stitching for Neuroevolution: Recombining Deep Neural Networks without Breaking Them
by: Guijt, Arthur, et al.
Published: (2024)

Evaluating Deep Neural Networks in Deployment (A Comparative and Replicability Study)
by: Pinconschi, Eduard, et al.
Published: (2024)

Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic Programming
by: Deng, Zihao, et al.
Published: (2023)

Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition
by: Liu, Peiyu, et al.
Published: (2025)

Deep memetic models for combinatorial optimization problems: application to the tool switching problem
by: Amaya, Jhon Edgar, et al.
Published: (2024)

Scalable Dendritic Modeling Advances Expressive and Robust Deep Spiking Neural Networks
by: Huang, Yifan, et al.
Published: (2024)

A Step towards Interpretable Multimodal AI Models with MultiFIX
by: Malafaia, Mafalda, et al.
Published: (2025)

Surrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks
by: Stapleton, Fergal, et al.
Published: (2025)

A Latency Coding Framework for Deep Spiking Neural Networks with Ultra-Low Latency
by: Lu, Yi, et al.
Published: (2026)

Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods
by: Zhou, Chenlin, et al.
Published: (2024)

A Hierarchical Importance-Guided Multi-objective Evolutionary Framework for Deep Neural Network Pruning
by: Khan, Zak, et al.
Published: (2026)

SHA-CNN: Scalable Hierarchical Aware Convolutional Neural Network for Edge AI
by: Dhakad, Narendra Singh, et al.
Published: (2024)

Evolving Neural Controllers for Xpilot-AI Racing Using Neuroevolution of Augmenting Topologies
by: O'Connor, Jim, et al.
Published: (2025)

EventQueues: Autodifferentiable spike event queues for brain simulation on AI accelerators
by: Landsmeer, Lennart P. L., et al.
Published: (2025)

Modular Duality in Deep Learning
by: Bernstein, Jeremy, et al.
Published: (2024)

Deep Neural Network-guided PSO for Tracking a Global Optimal Position in Complex Dynamic Environment
by: Raharja, Stephen, et al.
Published: (2026)

On-Chip Learning via Transformer In-Context Learning
by: Finkbeiner, Jan, et al.
Published: (2024)

Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
by: Nimbekar, Anagha, et al.
Published: (2024)

Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
by: Shen, Guobin, et al.
Published: (2023)