crucial
发表于 2025-3-30 08:33:57
Anika Fiebich,Nhung Nguyen,Sarah Schwarzkopfrences and temporal differences with optimal scale convolution, which solves restrictions of the results when classifying. The experiments on public DEAP dataset show that the 1D multi-scale CNN proposed outperforms other existing models.
FAST
发表于 2025-3-30 13:16:42
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euphoria
发表于 2025-3-30 17:51:53
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gorgeous
发表于 2025-3-30 21:38:06
Particle Swarm Based Reinforcement Learning particle swarm based reinforcement learning framework (PRL). Compared with the standard reinforcement learning algorithms, this framework greatly improves the exploration ability and obtains better scores in a series of gym experimental tests.
纺织品
发表于 2025-3-31 02:32:23
Emotion Recognition Based on Multi-scale Convolutional Neural Networkrences and temporal differences with optimal scale convolution, which solves restrictions of the results when classifying. The experiments on public DEAP dataset show that the 1D multi-scale CNN proposed outperforms other existing models.
exigent
发表于 2025-3-31 06:33:34
Multiple Residual Quantization of Pruninghts by combining the low-bit weights stem and residual parts many times, to minimize the error between the quantized weights and the full-precision weights, and to ensure higher precision quantization. At the same time, MRQP prunes some weights that have less impact on loss function to further reduce model size.
演讲
发表于 2025-3-31 11:11:33
Heterogeneous Multi-unit Control with Curriculum Learning for Multi-agent Reinforcement Learningnctions. Methods that utilize parameter or replay-buffer sharing are able to address the problem of combinatorial explosion under isomorphism assumption, but may lead to divergence under heterogeneous setting. This work use curriculum learning to bypass the barrier of a needle in a haystack that is
角斗士
发表于 2025-3-31 15:17:51
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addition
发表于 2025-3-31 18:23:16
Particle Swarm Based Reinforcement Learningnt learning has become a research hotspot. Nowadays, deep reinforcement learning algorithms have been successfully applied to the fields of games, industry and commerce. However, deep reinforcement learning algorithms often fall into the dilemma of “exploration” and “exploitation”, and the effect of