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of 38
pro vyhledávání: '"Zou, Xinyun"'
Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency maps are frequently used to provide interpretability for deep neural ne
Externí odkaz:
http://arxiv.org/abs/2205.08685
Publikováno v:
In International Biodeterioration & Biodegradation May 2024 190
Autor:
Zou, Xinyun, Scott, Eric O., Johnson, Alexander B., Chen, Kexin, Nitz, Douglas A., De Jong, Kenneth A., Krichmar, Jeffrey L.
Animals ranging from rats to humans can demonstrate cognitive map capabilities. We evolved weights in a biologically plausible recurrent neural network (RNN) using an evolutionary algorithm to replicate the behavior and neural activity observed in ra
Externí odkaz:
http://arxiv.org/abs/2102.12638
Despite the recent success of deep reinforcement learning (RL), domain adaptation remains an open problem. Although the generalization ability of RL agents is critical for the real-world applicability of Deep RL, zero-shot policy transfer is still a
Externí odkaz:
http://arxiv.org/abs/2102.05714
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Robots and self-driving vehicles face a number of challenges when navigating through real environments. Successful navigation in dynamic environments requires prioritizing subtasks and monitoring resources. Animals are under similar constraints. It h
Externí odkaz:
http://arxiv.org/abs/1909.06533
Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learning new tasks. Neural networks, in particular, suffer plenty from the c
Externí odkaz:
http://arxiv.org/abs/1903.06070
In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive excitation backprop (c-EB) was used in a goal-driven perception task with pairs of noisy MNIST digits, where the system h
Externí odkaz:
http://arxiv.org/abs/1903.00068
Publikováno v:
In Neural Networks April 2023 161:228-241
Akademický článek
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