Ivan Fioravanti ᯅ (@ivanfioravanti)

코딩(예: WebDev)에 특화된 설정을 공유: 정확한 코딩 작업을 위해 'Thinking mode' 사용을 권장하며 하이퍼파라미터는 temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0으로 제시함. 개발자용 프롬프트/튜닝 팁.

https://x.com/ivanfioravanti/status/2026608739512410311

#prompting #llm #coding #hyperparameters

'Empirical Design in Reinforcement Learning', by Andrew Patterson, Samuel Neumann, Martha White, Adam White.

http://jmlr.org/papers/v25/23-0183.html

#reinforcement #experiments #hyperparameters

#CausalML update - I am now fitting my first #CausalForest on real data!

Does anyone have advice on the most important #hyperparameters (After the # of trees & tree depth.)

I'm working on large imbalanced data sets and a large number of treatment variables, so it's not like anything you see in the economics literature. 🤔 #ML #AI #causal

'On the Hyperparameters in Stochastic Gradient Descent with Momentum', by Bin Shi.

http://jmlr.org/papers/v25/22-1189.html

#sgd #hyperparameters #stochastic

'Pre-trained Gaussian Processes for Bayesian Optimization', by Zi Wang et al.

http://jmlr.org/papers/v25/23-0269.html

#priors #prior #hyperparameters

'An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters', by Julie Keisler, El-Ghazali Talbi, Sandra Claudel, Gilles Cabriel.

http://jmlr.org/papers/v25/23-0166.html

#forecasting #algorithmic #hyperparameters

'Low-rank Variational Bayes correction to the Laplace method', by Janet van Niekerk, Haavard Rue.

http://jmlr.org/papers/v25/21-1405.html

#variational #hyperparameters #approximations