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KAN: Kolmogorov–Arnold Networks https://arxiv.org/pdf/2404.19756
Neural Characteristic Function Learning for Conditional Image Generation
A Characteristic Function Approach to Deep Implicit Generative Modeling 2020
Expressive Modeling Is Insufficient for Offline RL: A Tractable Inference Perspe...
The Meltdown Pathway: A Multidisciplinary Account of Autistic Meltdowns 崩溃之路:自闭症...
AI的TCPIP协议I:超维计算(向量符号体系结构)综述,第一部分:模型和数据转换
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6544897/
Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for ...
预测编码网络是受神经科学启发的模型,根源于贝叶斯统计和神经科学。然而,训练这样的模型通常效率低下且不稳定。在这项工作中,我们展示了通过简单地改变突触权重更新规则...
https://www.nature.com/articles/s42003-024-06037-4
Bistable perception, precision and neuromodulation
TABLE 2 | isomorphism between cognition and pattern formation.
A Review of Neuroscience-Inspired Machine Learning https://arxiv.org/abs/2403.18...
Making and breaking symmetries in mind and life
Designing explainable artificial intelligence with active inference: A framework...
https://learnableloop.com/posts/FlyToTarget_PORT.html
Hierarchical hybrid modeling for flexible tool use
本文考虑了热力学、信息和推理之间的关系。特别是,它在自组织的变分(自由能)原理下探索了信念更新的热力学伴随物。简而言之,任何拥有马尔可夫毯的(弱混合)随机动力系...
ngc-learn 是一个 Python 模拟库,旨在通过以灵活重新排列的组件和操作的形式具体实例化神经元动力学和突触可塑性形式来满足上述需求,以构建用于大脑研...
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