Posts by Collection
In this talk, I explain several major stochastic optimizers from the perspective of the metric, that is the definition of the parameter space of the model. This talk covers algorithms such as
- Quasi-Newton Method Type
- Finite-Difference Method: SGD-QN, AdaDelta, VSGD
- Extended Gauss-Newton: KSD, SMD, HF
- LBFGS: Stochastic LBFGS, RES
- Natural Gradient Type: Natural Gradient, TONGA
- Root Mean Square Type: AdaGrad, RMSProp, Adam
In this talk, I explain a few recent studies in the area of commonsense & relational knowledge probing of pretrained language models. Following papers are covered in this talk:
- Petroni, et al. “Language models as knowledge bases?” 2019
- Jiang, et al. “How can we know what language models know?” 2019
- Bouraoui, et al. “Inducing relational knowledge from BERT.” 2019
In this talk, I explained our recent papers related to relational knowledge representation. The pitch was in Japanese and recoded video is made available in this link.
In this talk, I explained our recent papers related to relational knowledge representation. See here for more detail.