all AI news
Challenges in creative generative models for music: a divergence maximization perspective. (arXiv:2211.08856v1 [stat.ML])
Nov. 17, 2022, 2:13 a.m. | Axel Chemla--Romeu-Santos, Philippe Esling
stat.ML updates on arXiv.org arxiv.org
The development of generative Machine Learning (ML) models in creative
practices, enabled by the recent improvements in usability and availability of
pre-trained models, is raising more and more interest among artists,
practitioners and performers. Yet, the introduction of such techniques in
artistic domains also revealed multiple limitations that escape current
evaluation methods used by scientists. Notably, most models are still unable to
generate content that lay outside of the domain defined by the training
dataset. In this paper, we propose …
arxiv challenges creative divergence generative models music perspective
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Data Scientist (m/f/x/d)
@ Symanto Research GmbH & Co. KG | Spain, Germany
Data Science Sustainability Co-Op (Summer & Fall 2024)
@ O-I | Perrysburg, OH, United States
Research Scientist
@ Chevron Phillips Chemical Company | USA: Kingwood, TX, US, 77339
Data Scientist Python (Django) (m/f/d)
@ RoomPriceGenie | Hybrid Mannheim, Remote DACH, Remote Germany
Operational Transformation & Strategy - Data Operations - Associate
@ JPMorgan Chase & Co. | Mumbai, Maharashtra, India
Senior Data Scientist
@ Rocket Travel | Chicago, IL USA