Search Results for author:
Found 8 papers, 5 papers with code
DeepTriangle: A Deep Learning Approach to Loss Reserving
We propose a novel approach for loss reserving based on deep neural networks.
Generative Synthesis of Insurance Datasets
One of the impediments in advancing actuarial research and developing open source assets for insurance analytics is the lack of realistic publicly available datasets.
Individual Claims Forecasting with Bayesian Mixture Density Networks
We introduce an individual claims forecasting framework utilizing Bayesian mixture density networks that can be used for claims analytics tasks such as case reserving and triaging.
Towards Explainability of Machine Learning Models in Insurance Pricing
Machine learning methods have garnered increasing interest among actuaries in recent years.
HATSUKI : An anime character like robot figure platform with anime-style expressions and imitation learning based action generation
Although numerous robots have been developed, less have focused on otaku-culture or on embodying the anime character figurine.
Action Generation
Cultural Vocal Bursts Intensity Prediction
+1
Evaluating a Bi-LSTM Model for Metaphor Detection in TOEFL Essays
However, the Bi-LSTM models lag behind the best performing systems in the shared task.
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.
On Noisy Evaluation in Federated Hyperparameter Tuning
In this work, we perform the first systematic study on the effect of noisy evaluation in federated hyperparameter tuning.
