Search Results for author:
Found 7 papers, 5 papers with code
Expected flow networks in stochastic environments and two-player zero-sum games
Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution.
On the Stability of Iterative Retraining of Generative Models on their own Data
In this paper, we develop a framework to rigorously study the impact of training generative models on mixed datasets -- from classical training on real data to self-consuming generative models trained on purely synthetic data.
AI4GCC -- Track 3: Consumption and the Challenges of Multi-Agent RL
The AI4GCC competition presents a bold step forward in the direction of integrating machine learning with traditional economic policy analysis.
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samples
The past few years have seen impressive progress in the development of deep generative models capable of producing high-dimensional, complex, and photo-realistic data.
Proceedings of the ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts
The first, ExVo-MultiTask, requires participants to train a multi-task model to recognize expressed emotions and demographic traits from vocal bursts.
Generating Diverse Vocal Bursts with StyleGAN2 and MEL-Spectrograms
We describe our approach for the generative emotional vocal burst task (ExVo Generate) of the ICML Expressive Vocalizations Competition.
The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts
ExVo 2022, includes three competition tracks using a large-scale dataset of 59, 201 vocalizations from 1, 702 speakers.
