This document discusses generative adversarial networks (GANs) and their relationship to reinforcement learning. It begins with an introduction to GANs, explaining how they can generate images without explicitly defining a probability distribution by using an adversarial training process. The second half discusses how GANs are related to actor-critic models and inverse reinforcement learning in reinforcement learning. It explains how GANs can be viewed as training a generator to fool a discriminator, similar to how policies are trained in reinforcement learning.
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models.
2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representations.
3. Recent models like word2vec use neural networks to learn word embeddings that capture linguistic regularities and can be used for tasks like analogy-making and machine translation.
This document discusses generative adversarial networks (GANs) and their relationship to reinforcement learning. It begins with an introduction to GANs, explaining how they can generate images without explicitly defining a probability distribution by using an adversarial training process. The second half discusses how GANs are related to actor-critic models and inverse reinforcement learning in reinforcement learning. It explains how GANs can be viewed as training a generator to fool a discriminator, similar to how policies are trained in reinforcement learning.
1. The document discusses various statistical and neural network-based models for representing words and modeling semantics, including LSI, PLSI, LDA, word2vec, and neural network language models.
2. These models represent words based on their distributional properties and contexts using techniques like matrix factorization, probabilistic modeling, and neural networks to learn vector representations.
3. Recent models like word2vec use neural networks to learn word embeddings that capture linguistic regularities and can be used for tasks like analogy-making and machine translation.
博士論文の執筆した時に作った,チェックリストをスライドにまとめました.
This slide is only for Japanese speakers
他に参考になるページ
+修士論文の作り方( http://itolab.is.ocha.ac.jp/~itot/lecture/msthesis.html ) by 伊藤先生
+修論(D論)参考( http://d.hatena.ne.jp/rkmt/20101217/1292573279 ) by 暦本純一先生
This document proposes a speaker-dependent WaveNet vocoder to generate high-quality speech from acoustic features. It uses a WaveNet model conditioned on mel-cepstral coefficients and fundamental frequency to directly model the relationship between acoustic features and speech waveforms. Evaluation shows the proposed method improves sound quality over traditional vocoders, as measured by objective metrics and subjective listening tests. Future work will apply this approach to other tasks and make the model independent of individual speakers.