共同给与
发表于 2025-3-25 03:58:35
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extemporaneous
发表于 2025-3-25 10:42:31
Artificial Neural Networks and Machine Learning – ICANN 2017978-3-319-68600-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
啜泣
发表于 2025-3-25 11:38:20
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拒绝
发表于 2025-3-25 18:45:20
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膝盖
发表于 2025-3-25 23:02:11
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好色
发表于 2025-3-26 03:21:10
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Absenteeism
发表于 2025-3-26 05:37:17
Semi-supervised Phoneme Recognition with Recurrent Ladder Networkse being compatible with many existing neural architectures. We present the recurrent ladder network, a novel modification of the ladder network, for semi-supervised learning of recurrent neural networks which we evaluate with a phoneme recognition task on the TIMIT corpus. Our results show that the
悦耳
发表于 2025-3-26 10:13:00
Mixing Actual and Predicted Sensory States Based on Uncertainty Estimation for Flexible and Robust Rbot behavior. We employ the so-called stochastic continuous-time RNN (S-CTRNN), which can learn to predict the mean and variance (or uncertainty) of subsequent sensorimotor information. Our proposed method uses this estimated uncertainty to determine a mixture ratio for combining actual and predicte
扫兴
发表于 2025-3-26 16:23:47
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左右连贯
发表于 2025-3-26 19:43:53
Neural End-to-End Self-learning of Visuomotor Skills by Environment Interactionex environments, generating suitable training data is time-consuming and depends on the availability of accurate robot models. Deep reinforcement learning alleviates this challenge by letting robots learn in an unsupervised manner through trial and error at the cost of long training times. In contra