* Satoshi Hara and Kohei Hayashi. Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. AISTATS'18 (to appear).
arXiv ver.: https://arxiv.org/abs/1606.09066#
* GitHub
https://github.com/sato9hara/defragTrees
【DL輪読会】Incorporating group update for speech enhancement based on convolutio...Deep Learning JP
1. The document discusses a research paper on speech enhancement using a convolutional gated recurrent network (CGRN) and ordered neuron long short-term memory (ON-LSTM).
2. The proposed method aims to improve speech quality by incorporating both time and frequency dependencies using CGRN, and handling noise with varying change rates using ON-LSTM.
3. CGRN replaces fully-connected layers with convolutions, allowing it to capture local spatial structures in the frequency domain. ON-LSTM groups neurons based on the change rate of internal information to model hierarchical representations.
BERT を中心に解説した資料です.BERT に比べると,XLNet と RoBERTa の内容は詳細に追ってないです.
あと,自作の図は上から下ですが,引っ張ってきた図は下から上になっているので注意してください.
もし間違い等あったら修正するので,言ってください.
(特に,RoBERTa の英語を読み間違えがちょっと怖いです.言い訳すいません.)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
XLNet: Generalized Autoregressive Pretraining for Language Understanding
RoBERTa: A Robustly Optimized BERT Pretraining Approach
* Satoshi Hara and Kohei Hayashi. Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. AISTATS'18 (to appear).
arXiv ver.: https://arxiv.org/abs/1606.09066#
* GitHub
https://github.com/sato9hara/defragTrees
【DL輪読会】Incorporating group update for speech enhancement based on convolutio...Deep Learning JP
1. The document discusses a research paper on speech enhancement using a convolutional gated recurrent network (CGRN) and ordered neuron long short-term memory (ON-LSTM).
2. The proposed method aims to improve speech quality by incorporating both time and frequency dependencies using CGRN, and handling noise with varying change rates using ON-LSTM.
3. CGRN replaces fully-connected layers with convolutions, allowing it to capture local spatial structures in the frequency domain. ON-LSTM groups neurons based on the change rate of internal information to model hierarchical representations.
BERT を中心に解説した資料です.BERT に比べると,XLNet と RoBERTa の内容は詳細に追ってないです.
あと,自作の図は上から下ですが,引っ張ってきた図は下から上になっているので注意してください.
もし間違い等あったら修正するので,言ってください.
(特に,RoBERTa の英語を読み間違えがちょっと怖いです.言い訳すいません.)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
XLNet: Generalized Autoregressive Pretraining for Language Understanding
RoBERTa: A Robustly Optimized BERT Pretraining Approach