20 citations · 59 across the 7 of their papers we have counts for
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cs.LG2019★ 6 cited
SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning
Keng Wah Loon, Laura Graesser, Milan Cvitkovic
We introduce SLM Lab, a software framework for reproducible reinforcement learning (RL) research. SLM Lab implements a number of popular RL algorithms, provides synchronous and asy…
cs.LG2019★ 3 cited
Sampling-Free Learning of Bayesian Quantized Neural Networks
Jiahao Su, Milan Cvitkovic, Furong Huang
Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important. In this paper, we propose Bayesia…
cs.LG2019★ 7 cited
Minimal Achievable Sufficient Statistic Learning
Milan Cvitkovic, Günther Koliander
We introduce Minimal Achievable Sufficient Statistic (MASS) Learning, a training method for machine learning models that attempts to produce minimal sufficient statistics with resp…