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https://doi.org/10.1007/978-3-322-90476-8ng visual information from the surroundings. Currently, many pre-trained models and pre-training tasks have been proposed to assist agents in navigating unfamiliar environments using visual and linguistic information. However, ensuring that the agent stops near the endpoint is a challenging problem.

Permanent

fluffy

Exonerate

Fakultät für Architektur und Raumplanungnature of real-world scenarios where systems encounter unknown objects. Unlike existing OWOD approaches which often rely on manually selected unknown proposals, we introduce an Adaptive Semantic-Degrade Learning framework. This framework, inspired by cognitive development theory, guides the model to

glans-penis

山羊

任意

ARBOR

Toward Efficient Deep Spiking Neuron Networks: A Survey on Compression and asynchronous computation. When deployed on neuromorphic chips, DSNNs offer significant power advantages over Deep Artificial Neural Networks (DANNs) and eliminate time and energy consuming multiplications due to the binary nature of spikes (0 or 1). Additionally, DSNNs excel in processing tempo

vocation

水土