Machine Learning for Creativity
Machine Learning for Creativity
- Lara Martin, Prithviraj Ammanabrolu, William Hancock, Shruti Singh, Brent Harrison, and Mark Riedl. Event Representations for Automated Story Generation with Deep Neural Nets
- Melissa Roemmele, Andrew Gordon, and Reid Swanson. Evaluating Story Generation Systems Using Automated Linguistic Analyses
- Vitobha Munigala, Srikanth Tamilselvam, and Anush Sankaran. "Let me convince you to buy my product ... ": A Case Study of an Automated Persuasive System in Fashion
- Naveen Panwar, Shreya Khare, Neelamadhav Gantayat, Rahul Aralikatte, Senthil Mani, and Anush Sankaran. mAnI: Movie Amalgamation using Neural Imitation; Visualizing the Movie while Reading a Book
- Omid Alemi, Jules Françoise, and Philippe Pasquier. GrooveNet: Real-Time Music-Driven Dance Movement Generation using Artificial Neural Networks
- Priyanka Agrawal, Parag Jain, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, and Karthik Sankaranarayanan. Story Generation from Sequence of Independent Short Descriptions
- Janani Mukundan and Richard Daskas. Watson Beat: Composing Music Using Foresight And Planning
- Disha Shrivastava, Saneem Ahmed Cg, Anirban Laha, and Karthik Sankaranarayanan. A Machine Learning Approach for Evaluating Creative Artifacts
- Mason Bretan, Sageev Oore, Douglas Eck, and Larry Heck. Learning and Evaluating Musical Features with Deep Autoencoders
|8:00 – 8:05
|8:05 – 8:35
||Invited Talk by Mark Riedl (Georgia Institute of Technology)
|8:35 – 9:05
||Special session on innovative applications of creativity
|9:05 – 10:00
||Oral paper presentations (4 papers)
|10:00 – 11:00
||Coffee Break and Poster session
|11:00 – 11:30
||Invited talk by Flavio du Pin Calmon (Harvard University)
|11:30 – 12:00
||Invited talk by Nick Montfort (MIT)
All of us must have dreamt of having our own JARVIS (refer, Marvel comics) which can help us write poetry, paint a mural, compose a melody, choreograph a dance, or even write a research paper for this workshop! It is true that machine learning has not only solved challenging problems in the areas of speech, vision, natural language etc. but also hit the headlines by winning against humans in grand challenges such as Jeopardy, Go, and more recently Poker. Yet one of the elusive goals of artificial intelligence remains human-level creativity. All attempts to emulate creativity artificially fall under the umbrella of an emerging field called computational creativity.
The goal of this workshop is to generate interest among the machine learning and data science community in this upcoming field by concentrating on applications of machine learning in creative domains. This workshop creates a forum for researchers and practitioners to exchange ideas and decide on the future roadmap of this field.
Topics of Interest
There has already been considerable interest generated among artists and designers in assistive tools and frameworks to create new and original content. We believe that the current limitations of achieving a purely creative machine can be alleviated using the advances in machine learning.
Suggested topics of paper submission for this workshop include, but not restricted to:
- Formulations/perspectives about creativity.
- Evaluation metrics for creativity.
- Learning paradigms for creativity.
- Large scale analytics with creativity understanding.
- Case studies of creative generation process.
- Insights into solutions/models for creativity.
- Identifying and mining creative content.
- Creativity vs Popularity/Likability.
- Surveys or benchmark datasets related to creative technologies.
- Assistive Creative tools for professionals and end-users.
- Frameworks tuned for specific fields like speech, vision and natural language.
- Domain adaptation for creativity.
- Personalized content generation.
- Creative conversational tools.
- Recommendation models for creative applications.
- Reinforcement learning for self-adaption with interactions.
- Multi-modal systems for creativity.
- Applications specific to professions like art, dance, music, literature, gaming, movie, fashion, recipe, education etc.
- Interfaces for creative human-computer interaction.
- Collaborative frameworks for creative domains.
May 26, 2017
June 16, 2017 June 26, 2017
Aug 14, 2017
*All deadlines are at 11:59 PM Pacific Standard Time (PST)
- Mitesh Khapra, Indian Institute of Technology, Madras
- Anirban Laha, IBM Research
- Saneem CG, IBM Research
- Philippe Pasquier, Simon Fraser University
- Haizi Yu, University of Illinois at Urbana-Champaign
- Lingfei Wu, IBM Research
- Flavio du Pin Calmon, Harvard University
- Mark Riedl, Georgia Institute of Technology
- Parag Jain, IBM Research
- Karthikeyan Natesan Ramamurthy, IBM Research
- Prasanna Sattigeri, IBM Research
- Ravi Kothari, IBM Research
- Ashish Verma, IBM Research
- Sameep Mehta, IBM Research
- Vikas Raykar, IBM Research
- Arvind Agarwal, IBM Research
We solicit submission of papers of 4 to 10 pages representing reports of original research, preliminary research results, survey and dataset papers, case studies, proposals for new work, and position papers. We also seek poster submissions based on recently published work (please indicate the conference published).
Following KDD conference tradition, reviews are not double-blind, and author names and affiliations should be listed. If accepted, at least one of the authors must attend the workshop to present the work. The submitted papers must be written in English and formatted in the double column standard according to the ACM Proceedings Template, Tighter Alternate style. The papers should be in PDF format and submitted via the EasyChair submission site. The workshop website will archive the published papers.