[HTML][HTML] Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network
I Jeon, T Kim - Frontiers in Computational Neuroscience, 2023 - ncbi.nlm.nih.gov
Although it may appear infeasible and impractical, building artificial intelligence (AI) using a
bottom-up approach based on the understanding of neuroscience is straightforward. The …
bottom-up approach based on the understanding of neuroscience is straightforward. The …
Cross-Domain Feature learning and data augmentation for few-shot proxy development in oil industry
In reservoir engineering, numerical simulators are crucial for analyzing risks and
uncertainties. The decision-making plan is complex due to numerous uncertain variables …
uncertainties. The decision-making plan is complex due to numerous uncertain variables …
An efficient and lightweight off-policy actor-critic reinforcement learning framework
In the framework of current off-policy actor-critic methods, the state–action pairs in an
experience replay buffer (called historical behaviors) cannot be used to improve the policy …
experience replay buffer (called historical behaviors) cannot be used to improve the policy …
[HTML][HTML] Brain-Inspired Agents for Quantum Reinforcement Learning
In recent years, advancements in brain science and neuroscience have significantly
influenced the field of computer science, particularly in the domain of reinforcement learning …
influenced the field of computer science, particularly in the domain of reinforcement learning …
[HTML][HTML] Exploration–Exploitation Mechanisms in Recurrent Neural Networks and Human Learners in Restless Bandit Problems
A key feature of animal and human decision-making is to balance the exploration of
unknown options for information gain (directed exploration) versus selecting known options …
unknown options for information gain (directed exploration) versus selecting known options …
Human-level reinforcement learning performance of recurrent neural networks is linked to hyperperseveration, not directed exploration
A key feature of animal and human decision-making is to balance exploring unknown
options for information gain (directed exploration) versus exploiting known options for …
options for information gain (directed exploration) versus exploiting known options for …
Neuro-Inspired Plasticity for Biologically Realistic Self-Adaptation of Neural Network Weights
R Kalahasty - … Conference on Development and Learning (ICDL …, 2023 - ieeexplore.ieee.org
The Prefrontal Cortex is the core of higher level learning and memory. It currently operates
much like an AI system, in the sense that its actions are guided via a dopamine based …
much like an AI system, in the sense that its actions are guided via a dopamine based …
Neural network-based methods for finding the shortest path and establishing associative connections between objects
E Fedorov, O Nechyporenko, M Chychuzhko… - … and Computer Systems, 2023 - nti.khai.edu
Nowadays, solving optimizations problems is one of the tasks for intelligent computer
systems. Currently, there is a problem of insufficient efficiency of optimizations tasks solving …
systems. Currently, there is a problem of insufficient efficiency of optimizations tasks solving …
[PDF][PDF] Multi-Agent Reinforcement Learning Methods with Dynamic Parameters for Logistic Tasks
E Fedorov, O Nechyporenko, Y Korpan… - 2024 - ceur-ws.org
Part of Industry 4.0 is building computer systems by combining artificial intelligence with
robotics. Such computer systems play an important role in the planning of cargo …
robotics. Such computer systems play an important role in the planning of cargo …