Happy to share our new paper, accepted at [ICML] Int'l Conference on Machine Learning, in which we tackle ill-posed learning tasks, uncertainty prediction and conditional density estimation. In this work we address these challenges by exploring the Winner-Takes-All (WTA) training scheme. This approach can be used to train a multi-head network to produce diverse, plausible outputs for each input. One major open problem is evaluating these multiple predictions probabilistically. To address this, we introduce a novel density estimator based on WTA learners. Our estimator shows promising properties, both theoretically in terms of quantization and probabilistic convergence, and experimentally through extensive evaluation on both synthetic and audio data. Check out our paper at https://lnkd.in/e4QesXsq A joint work with David Perera, Cédric Rommel, Mathieu Fontaine, Slim Essid, Gaël Richard and Patrick Pérez Multi-head #neuralnetworks #machinelearning #Uncertainty #DensityEstimation
Hector Ancel
3mo
Congrats Victor !
PhD Student in Deep/Theoretical Reinforcement Learning @Inria 👾
4moVery nice work 👏 ! Do its work well in high dimensional spaces?