Fetching the paper…
Reading the bibliography…
There is a growing interest in the area of machine learning and creativity.
An Interaction Framework for Studying Co-Creative AI
Matthew Guzdial and Mark O. Riedl. 2019 · 1903
Earlier work this paper cites.
Generating Long Sequences with Sparse Transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019 · 1904
Earlier work this paper cites.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. 2019 · 1909
Earlier work this paper cites.
Computing Machinery and Intelligence
Alan M. Turing. 1950 · 1950
Earlier work this paper cites.
An Analysis of Creativity
Mel Rhodes. 1961 · 1961
Earlier work this paper cites.
The Processes of Creative Thinking
Allen Newell, J. C. Shaw, and Herbert A. Simon. 1962 · 1962
Earlier work this paper cites.
Aesthetics and Psychobiology
Daniel E. Berlyne. 1971 · 1971
Earlier work this paper cites.
TALE-SPIN, an Interactive Program That Writes Stories. In Proc. of the 5th International Joint Conference on Artificial Intelligence - Volume 1 (IJCAI’77) (Cambridge, MA)
James R. Meehan. 1977 · 1977
Earlier work this paper cites.
The Social Psychology of Creativity: A Componential Conceptualization
Teresa M. Amabile. 1983 · 1983
Earlier work this paper cites.
The Policeman’s Beard Is Half Constructed
Racter. 1984 · 1984
Earlier work this paper cites.
Allocating ownership rights in computer-generated works
Pamela Samuelson. 1985 · 1985
Earlier work this paper cites.
Scientific Discovery: Computational Explorations of the Creative Process
Pat Langley, Herbert A. Simon, Gary L. Bradshaw, and Jan M. Zytkow. 1987 · 1987
Earlier work this paper cites.
How to Draw Three People in a Botanical Garden. In Proc. of the 7th AAAI National Conference on Artificial Intelligence (AAAI’88) (Saint Paul, MN)
Harold Cohen. 1988 · 1988
Earlier work this paper cites.
Experiments in Musical Intelligence (EMI): Non-Linear Linguistic-Based Composition
David Cope. 1989 · 1989
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams. 1992 · 1992
Earlier work this paper cites.
Creativity: A Survey of AI Approaches
Jon Rowe and Derek Partridge. 1993 · 1993
Earlier work this paper cites.
Learning Long-Term Dependencies with Gradient Descent is Difficult
Yoshua Bengio, Patrice Simard, and Paolo Frasconi. 1994 · 1994
Earlier work this paper cites.
The Copycat Project: A Model of Mental Fluidity and Analogy-Making
Douglas R. Hofstadter and Melanie Mitchell. 1994 · 1994
Earlier work this paper cites.
Creativity, Creative Thinking, and Critical Thinking: In Search of Definitions
Donald J. Treffinger. 1996 · 1996
Earlier work this paper cites.
Long Short-Term Memory
Sepp Hochreiter and Jurgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
An Introduction to Variational Methods for Graphical Models
Michael I. Jordan, Zoubin Ghahrmamani, Tommi S. Jaakkola, and Lawrence K. Saul. 1999 · 1999
Earlier work this paper cites.
Creating Creativity: 101 Definitions (what Webster Never Told You)
Andrei G. Aleinikov, Sharon Kackmeister, and Ron Koenig. 2000 · 2000
Earlier work this paper cites.
Computational Models of Innovative and Creative Design Processes
John Gero. 2000 · 2000
Earlier work this paper cites.
Creativity, the Turing Test, and the (Better) Lovelace Test
Selmer Bringsjord, Paul Bello, and David Ferrucci. 2001 · 2001
Earlier work this paper cites.
Finding temporal structure in music: blues improvisation with LSTM recurrent networks. In Proc. of the 12th IEEE Workshop on Neural Networks for Signal Processing (New York, NY)
Douglas Eck and Jurgen Schmidhuber. 2002 · 2002
Earlier work this paper cites.
The Creative Mind: Myths and Mechanisms
Margaret A. Boden. 2003 · 2003
Earlier work this paper cites.
Creativity and Imagination
Berys Gaut. 2003 · 2003
Earlier work this paper cites.
The Challenges of Clustering High Dimensional Data
Michael Steinbach, Levent Ertöz, and Vipin Kumar. 2004 · 2004
Earlier work this paper cites.
Jukebox: A Generative Model for Music
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever. 2020 · 2005
Earlier work this paper cites.
Experiments With Assessment of Creative Systems: An Application of Ritchie’s Criteria. In Proc. of the IJCAI’15 Second Computational Creativity Workshop (Edinburgh, Scotland)
Francisco C. Pereira, Mateus Mendes, Pablo Gervas, and Amilcar Cardoso. 2005 · 2005
Earlier work this paper cites.
Atoms of EVE’: A Bayesian Basis for Esthetic Analysis of Style in Sketching
Kevin Burns. 2006 · 2006
Earlier work this paper cites.
The Emotion Machine
Marvin Minsky. 2006 · 2006
Earlier work this paper cites.
Searching for Computational Creativity
Geraint A. Wiggins. 2006 · 2006
Earlier work this paper cites.
On the Development of Evolutionary Artificial Artists
Penousal Machado, Juan Romero, Antonino Santos, Amílcar Cardoso, and Alejandro Pazos. 2007 · 2007
Earlier work this paper cites.
Some Empirical Criteria for Attributing Creativity to a Computer Program
Graeme Ritchie. 2007 · 2007
Earlier work this paper cites.
The Work of Art in the Age of Mechanical Reproduction
Walter Benjamin. 2008 · 2008
Earlier work this paper cites.
Creativity Versus the Perception of Creativity in Computational Systems. In Proc. of the 2008 AAAI Spring Symposium (Stanford, CA)
Simon Colton. 2008 · 2008
Earlier work this paper cites.
Maximum Entropy Inverse Reinforcement Learning. In Proc. of the 23rd AAAI Conference on Artificial Intelligence (AAAI’08) (Chicago, IL)
Brian D. Ziebart, Andrew Maas, J. Andrew Bagnell, and Anind K. Dey. 2008 · 2008
Earlier work this paper cites.
Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity
Amílcar Cardoso, Tony Veale, and Geraint A. Wiggins. 2009 · 2009
Earlier work this paper cites.
Distilling Free-Form Natural Laws from Experimental Data
Michael D. Schmidt and Hod Lipson. 2009 · 2009
Earlier work this paper cites.
Of Bits and Wows: A Bayesian Theory of Surprise with Applications to Attention
Pierre Baldi and Laurent Itti. 2010 · 2010
Earlier work this paper cites.
Evaluating Creativity in Humans, Computers, and Collectively Intelligent Systems. In Proc. of the 1st DESIRE Network Conference on Creativity and Innovation in Design (Aarhus, Denmark)
Mary Maher. 2010 · 2010
Earlier work this paper cites.
Establishing Appreciation in a Creative System. In Proc. of the 1st International Conference on Computational Creativity (ICCC’15) (Lisbon, Portugal)
David Norton, Derral Heath, and Dan Ventura. 2010 · 2010
Earlier work this paper cites.
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010)
Jürgen Schmidhuber. 2010 · 2010
Earlier work this paper cites.
Computational Creativity Theory: The FACE and IDEA Descriptive Models. In Proc. of the 2nd International Conference on Computational Creativity (ICCC’11) (Mexico City, Mexico)
Simon Colton, John William Charnley, and Alison Pease. 2011 · 2011
Earlier work this paper cites.
Abandoning Objectives: Evolution Through the Search for Novelty Alone
Joel Lehman and Kenneth O. Stanley. 2011 · 2011
Earlier work this paper cites.
Bayesian Learning via Stochastic Gradient Langevin Dynamics. In Proc. of the 28th International Conference on International Conference on Machine Learning (ICML’11) (Bellevue, WA)
Max Welling and Yee Whye Teh. 2011 · 2011
Earlier work this paper cites.
The Painting Fool: Stories from Building an Automated Painter
Simon Colton. 2012 · 2012
Earlier work this paper cites.
Computational Creativity: The Final Frontier?. In Proc. of the 20th European Conference on Artificial Intelligence (ECAI’12) (Montpellier, France)
Simon Colton and Geraint A. Wiggins. 2012 · 2012
Earlier work this paper cites.
A Standardised Procedure for Evaluating Creative Systems: Computational Creativity Evaluation Based on What it is to be Creative
Anna Jordanous. 2012 · 2012
Earlier work this paper cites.
Using AI to Evaluate Creative Designs. In Proc. of the 2nd International Conference on Design Creativity (ICDC’12) (Glasgow, United Kingdom)
Mary Maher and Doug Fisher. 2012 · 2012
Earlier work this paper cites.
Soup Over Bean of Pure Joy: Culinary Ruminations of an Artificial Chef. In Proc. of the 3rd International Conference on Computational Creativity (ICCC’12) (Dublin, Ireland)
Richard G. Morris, Scott H. Burton, Paul Bodily, and Dan Ventura. 2012 · 2012
Earlier work this paper cites.
Generative Adversarial Nets. In Advances in Neural Information Processing Systems (NeurIPS’14) (Montreal, Canada)
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
What to Expect when you’re Expecting: The Role of Unexpectedness in Computationally Evaluating Creativity. In Proc. of the 5th International Conference on Computational Creativity (ICCC’14) (Ljubljana, Slovenia)
Kazjon Grace and Mary Lou Maher. 2014 · 2014
Earlier work this paper cites.
Stepping Back to Progress Forwards: Setting Standards for Meta-Evaluation of Computational Creativity. In Proc. of the 5th International Conference on Computational Creativity (ICCC’14) (Ljubljana, Slovenia)
Anna Jordanous. 2014 · 2014
Earlier work this paper cites.
Towards Machines for Measuring Creativity: The Use of Computational Tools in Storytelling Activities. In Proc. of the 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT’14) (Athens, Greece)
Pythagoras Karampiperis, Antonis Koukourikos, and Evangelia Koliopoulou. 2014 · 2014
Earlier work this paper cites.
Semi-supervised Learning with Deep Generative Models. In Advances in Neural Information Processing Systems (NeurIPS’14) (Montreal, Canada)
Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. 2014 · 2014
Earlier work this paper cites.
Auto-Encoding Variational Bayes. In Proc. of the 2nd International Conference on Learning Representations (ICLR’14) (Banff, Canada)
Diederik P. Kingma and Max Welling. 2014 · 2014
Earlier work this paper cites.
Conditional Generative Adversarial Nets
Mehdi Mirza and Simon Osindero. 2014 · 2014
Earlier work this paper cites.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In Proc. of the 31st International Conference on Machine Learning (ICML’14) (Beijing, China)
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014 · 2014
Earlier work this paper cites.
The Lovelace 2.0 Test of Artificial Creativity and Intelligence
Mark O. Riedl. 2014 · 2014
Earlier work this paper cites.
Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS’14) (Montreal, Canada)
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
Earlier work this paper cites.
Chinese Poetry Generation with Recurrent Neural Networks. In Proc. of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP’14) (Doha, Qatar)
Xingxing Zhang and Mirella Lapata. 2014 · 2014
Earlier work this paper cites.
Neural Machine Translation by Jointly Learning to Align and Translate. In Proc. of the 3rd International Conference on Learning Representations (ICLR’15) (San Diego, CA)
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Computing the Creativeness of Amusing Advertisements: A Bayesian Model of Burma-Shave’s Muse
Kevin Burns. 2015 · 2015
Earlier work this paper cites.
The Painting Fool Sees! New Projects with the Automated Painter. In Proc. of the 6th International Conference on Computational Creativity (ICCC’15) (Park City, UT)
Simon Colton, Jakob Halskov, Dan Ventura, Ian Gouldstone, Michael Cook, and Blanca Pérez-Ferrer. 2015 · 2015
Earlier work this paper cites.
Quantifying Creativity in Art Networks. In Proc. of the 6th International Conference on Computational Creativity (ICCC’15) (Park City, UT)
Ahmed Elgammal and Babak Saleh. 2015 · 2015
Earlier work this paper cites.
DRAW: A Recurrent Neural Network For Image Generation. In Proc. of the 32nd International Conference on Machine Learning (ICML’15) (Lille, France)
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, and Daan Wierstra. 2015 · 2015
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network. In Proc. of the NeurIPS’15 Deep Learning and Representation Learning Workshop (Montreal, Canada)
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
Earlier work this paper cites.
The Unreasonable Effectiveness of Recurrent Neural Networks
Andrej Karpathy. 2015 · 2015
Earlier work this paper cites.
Human-Level Control Through Deep Reinforcement Learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. 2015 · 2015
Earlier work this paper cites.
Inceptionism: Going Deeper into Neural Networks
Alexander Mordvintsev, Christopher Olah, and Mike Tyka. 2015 · 2015
Earlier work this paper cites.
GhostWriter: Using an LSTM for Automatic Rap Lyric Generation. In Proc. of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP’15) (Lisbon, Portugal)
Peter Potash, Alexey Romanov, and Anna Rumshisky. 2015 · 2015
Earlier work this paper cites.
Deep Unsupervised Learning using Nonequilibrium Thermodynamics. In Proc. of the 32nd International Conference on Machine Learning (ICML’15) (Lille, France)
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015 · 2015
Earlier work this paper cites.
Data Mining and Machine Learning in Computational Creativity
Hannu Toivonen and Oskar Gross. 2015 · 2015
Earlier work this paper cites.
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images. In Advances in Neural Information Processing Systems (NeurIPS’15) (Montreal, Canada)
Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, and Martin Riedmiller. 2015 · 2015
Earlier work this paper cites.
Generating Sentences from a Continuous Space. In Proc. of the 20th SIGNLL Conference on Computational Natural Language Learning (CoNNL’16) (Berlin, Germany)
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, and Samy Bengio. 2016 · 2016
Earlier work this paper cites.
Importance Weighted Autoencoders. In Proc. of the 4th International Conference on Learning Representations (ICLR’16) (San Juan, Puerto Rico)
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov. 2016 · 2016
Earlier work this paper cites.
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. In Advances in Neural Information Processing Systems (NeurIPS’16) (Barcelona, Spain)
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016 · 2016
Earlier work this paper cites.
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models. In Advances in Neural Information Processing Systems (NeurIPS’16) (Barcelona, Spain)
S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Koray Kavukcuoglu, and Geoffrey E. Hinton. 2016 · 2016
Earlier work this paper cites.
Regent-Dependent Creativity: A Domain Independent Metric for the Assessment of Creative Artifacts. In Proc. of the 7th International Conference on Computational Creativity (ICCC’16) (Paris, France)
Celso França, Luís Fabrício Wanderley Góes, Alvaro Amorim, Rodrigo C. O. Rocha, and Alysson Ribeiro Da Silva. 2016 · 2016
Earlier work this paper cites.
A Neural Algorithm of Artistic Style
Leon Gatys, Alexander Ecker, and Matthias Bethge. 2016 · 2016
Earlier work this paper cites.
NIPS 2016 Tutorial: Generative Adversarial Networks
Ian Goodfellow. 2017 · 2016
Earlier work this paper cites.
Surprise Search: Beyond Objectives and Novelty. In Proc. of the Genetic and Evolutionary Computation Conference (GECCO’16) (Denver, CO)
Daniele Gravina, Antonios Liapis, and Georgios Yannakakis. 2016 · 2016
Earlier work this paper cites.
Generating Music by Fine-Tuning Recurrent Neural Networks with Reinforcement Learning. In Proc. of the NeurIPS’16 Deep Reinforcement Learning Workshop (Barcelona, Spain)
Natasha Jaques, Shixiang Gu, Richard E. Turner, and Douglas Eck. 2016 · 2016
Earlier work this paper cites.
Four PPPPerspectives on Computational Creativity in Theory and in Practice
Anna Jordanous. 2016 · 2016
Earlier work this paper cites.
Autoencoding beyond Pixels Using a Learned Similarity Metric. In Proc. of the 33rd International Conference on Machine Learning (ICML’16) (New York, NY)
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2016 · 2016
Earlier work this paper cites.
Quantifying the Vanishing Gradient and Long Distance Dependency Problem in Recursive Neural Networks and Recursive LSTMs. In Proc. of the 1st Workshop on Representation Learning for NLP (Berlin, Germany)
Phong Le and Willem Zuidema. 2016 · 2016
Earlier work this paper cites.
Deep Reinforcement Learning for Dialogue Generation. In Proc. of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP’16) (Austin, TX)
Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. 2016 · 2016
Earlier work this paper cites.
Auxiliary Deep Generative Models. In Proc. of the 33rd International Conference on Machine Learning (ICML’16) (New York, NY)
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther. 2016 · 2016
Earlier work this paper cites.
Adversarial Autoencoders. In Proc. of the 4th International Conference on Learning Representations (ICLR’16) (San Juan, Puerto Rico)
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow. 2016 · 2016
Earlier work this paper cites.
Synthesizing the Preferred Inputs for Neurons in Neural Networks via Deep Generator Networks. In Advances in Neural Information Processing Systems (NeurIPS’16) (Barcelona, Spain)
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Learning with Generative Adversarial Networks
Augustus Odena. 2016 · 2016
Earlier work this paper cites.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In Proc. of the 4th International Conference on Learning Representations (ICLR’16) (San Juan, Puerto Rico)
Alec Radford, Luke Metz, and Soumith Chintala. 2016 · 2016
Earlier work this paper cites.
Sequence Level Training with Recurrent Neural Networks. In Proc. of the 4th International Conference on Learning Representations (ICLR’16) (San Juan, Puerto Rico)
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2016 · 2016
Earlier work this paper cites.
Generative Adversarial Text to Image Synthesis. In Proc. of the 33rd International Conference on Machine Learning (ICML’16) (New York, NY)
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee. 2016 · 2016
Earlier work this paper cites.
Music Transcription Modelling and Composition Using Deep Learning. In Proc. of the 1st Conference on Computer Simulation of Musical Creativity (CSMC’16) (Huddersfield, UK)
Bob L. Sturm, João Felipe Santos, Oded Ben-Tal, and Iryna Korshunova. 2016 · 2016
Earlier work this paper cites.
Mere Generation: Essential Barometer or Dated Concept?. In Proc. of the 7th International Conference on Computational Creativity (ICCC’16) (Paris, France)
Dan Ventura. 2016 · 2016
Earlier work this paper cites.
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. In Advances in Neural Information Processing Systems (NeurIPS’16) (Barcelona, Spain), Vol. 29
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. 2016 · 2016
Cited alongside, same era.
Attribute2Image: Conditional Image Generation from Visual Attributes. In Proc. of the 11th European Conference on Computer Vision (ECCV’16) (Amsterdam, The Netherlands)
Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee. 2016 · 2016
Cited alongside, same era.
Generating Text via Adversarial Training. In Proc. of the NeurIPS’16 Workshop on Adversarial Training (Barcelona, Spain)
Yizhe Zhang, Zhe Gan, and Lawrence Carin. 2016 · 2016
Cited alongside, same era.
An Actor-Critic Algorithm for Sequence Prediction. In Proc. of the 5th International Conference on Learning Representations (ICLR’17) (Toulon, France)
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron Courville, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney Arx, Michael Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Davis, Dora Demszky, and Percy Liang. 2021 · 2021
Closest in time.
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
Sam Bond-Taylor, Adam Leach, Yang Long, and Chris G. Willcocks. 2021 · 2021
Closest in time.
Beyond the Creative Species
Oliver Bown. 2021 · 2021
Closest in time.
Active Divergence with Generative Deep Learning - A Survey and Taxonomy. In Proc. of the 12th International Conference on Computational Creativity (ICCC’21) (Online)
Terence Broad, Sebastian Berns, Simon Colton, and Mick Grierson. 2021 · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep Reinforcement Learning from Human Preferences. In Advances in Neural Information Processing Systems (NeurIPS’17) (Long Beach, CA)
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
Cited alongside, same era.
Adversarial Feature Learning. In Proc. of the 5th International Conference on Learning Representations (ICLR’17) (Toulon, France)
Jeff Donahue, Philipp Krahenbuhl, and Trevor Darrell. 2017 · 2017
Cited alongside, same era.
Adversarially Learned Inference. In Proc. of the 5th International Conference on Learning Representations (ICLR’17) (Toulon, France)
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville. 2017 · 2017
Cited alongside, same era.
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms. In Proc. of the 8th International Conference on Computational Creativity (ICCC’17) (Atlanta, GA)
Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, and Marian Mazzone. 2017 · 2017
Cited alongside, same era.
Controlling Perceptual Factors in Neural Style Transfer. In Proc. of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17) (Honolulu, HI)
Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, and Eli Shechtman. 2017 · 2017
Cited alongside, same era.
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Gabriel L. Guimaraes, Benjamin Sanchez-Lengeling, Pedro Luis Cunha Farias, and Alan Aspuru-Guzik. 2017 · 2017
Cited alongside, same era.
PixelVAE: A Latent Variable Model for Natural Images. In Proc. of the 5th International Conference on Learning Representations (ICLR’17) (Toulon, France)
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville. 2017 · 2017
Cited alongside, same era.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. In Proc. of the 5th International Conference on Learning Representations (ICLR’17) (Toulon, France)
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017 · 2017
Cited alongside, same era.
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, …, and Wojciech Zaremba. 2021 · 2021
Closest in time.
A Survey on Adversarial Recommender Systems: From Attack/Defense Strategies to Generative Adversarial Networks
Yashar Deldjoo, Tommaso Di Noia, and Felice Antonio Merra. 2021 · 2021
Closest in time.
Diffusion Models Beat GANs on Image Synthesis. In Advances in Neural Information Processing Systems (NeurIPS’21) (Online)
Prafulla Dhariwal and Alexander Quinn Nichol. 2021 · 2021
Closest in time.
CogView: Mastering Text-to-Image Generation via Transformers. In Advances in Neural Information Processing Systems (NeurIPS’21) (Online)
Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, and Jie Tang. 2021 · 2021
Closest in time.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In Proc. of the 9th International Conference on Learning Representations (ICLR’21) (Online)
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021 · 2021
Closest in time.
Taming Transformers for High-Resolution Image Synthesis. In Proc. of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’21) (Nashville, TN)
Patrick Esser, Robin Rombach, and Bjorn Ommer. 2021 · 2021
Closest in time.
Generative Art Using Neural Visual Grammars and Dual Encoders
Chrisantha Fernando, S. M. Ali Eslami, Jean-Baptiste Alayrac, Piotr Mirowski, Dylan Banarse, and Simon Osindero. 2021 · 2021
Closest in time.
An Introduction to Variational Inference
Ankush Ganguly and Samuel W. F. Earp. 2021 · 2021
Closest in time.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics. In Proc. of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM’21) (Online)
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal, Pawan Sasanka Ammanamanchi, Aremu Anuoluwapo, Antoine Bosselut, Khyathi Raghavi Chandu, Miruna Clinciu, Dipanjan Das, Kaustubh D. Dhole, Wanyu Du, Esin Durmus, Ondřej Dušek, Chris Emezue, Varun Gangal, Cristina Garbacea, Tatsunori Hashimoto, Yufang Hou, Yacine Jernite, …, and Jiawei Zhou. 2021 · 2021
Closest in time.
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Jie Gui, Z. Sun, Yonggang Wen, Dacheng Tao, and Ye Jie-ping. 2021 · 2021
Closest in time.
Classifier-Free Diffusion Guidance. In Proc. of the NeurIPS’21 Workshop on Deep Generative Models and Downstream Applications (Online)
Jonathan Ho and Tim Salimans. 2021 · 2021
Closest in time.
Wolfflin’s Affective Generative Analysis for Visual Art. In Proc. of the 20th International Conference on Computational Creativity (ICCC’21) (Online)
Divyansh Jha, Hanna Chang, and Mohamed Elhoseiny. 2021 · 2021
Closest in time.
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. In Proc. of the 38th International Conference on Machine Learning (ICML’21) (Online)
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, and Tom Duerig. 2021 · 2021
Closest in time.
Highly Accurate Protein Structure Prediction with AlphaFold
John M. Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zidek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andy Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas … Adler, and Demis Hassabis. 2021 · 2021
Closest in time.
Alias-Free Generative Adversarial Networks. In Advances in Neural Information Processing Systems (NeurIPS’21) (Online)
Tero Karras, Miika Aittala, Samuli Laine, Erik Harkonen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. 2021 · 2021
Closest in time.
DiffWave: A Versatile Diffusion Model for Audio Synthesis. In Proc. of the 9th International Conference on Learning Representations (ICLR’21) (Online)
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. 2021 · 2021
Closest in time.
The Power of Scale for Parameter-Efficient Prompt Tuning. In Proc. of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP’21) (Online and Punta Cana, Dominican Republic)
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Closest in time.
Time-series Forecasting with Deep Learning: A Survey
Bryan Lim and Stefan Zohren. 2021 · 2021
Closest in time.
Deep Learning for Procedural Content Generation
Jialin Liu, Sam Snodgrass, Ahmed Khalifa, Sebastian Risi, Georgios N. Yannakakis, and Julian Togelius. 2021 · 2021
Closest in time.
Symbolic Music Generation with Diffusion Models. In Proc. of the 22nd Int. Society for Music Information Retrieval Conf. (ISMIR’21) (Online)
Gautam Mittal, Jesse Engel, Curtis Hawthorne, and Ian Simon. 2021 · 2021
Closest in time.
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-Ray
Saman Motamed, Patrik Rogalla, and Farzad Khalvati. 2021 · 2021
Closest in time.
Improved Denoising Diffusion Probabilistic Models. In Proc. of the 38th International Conference on Machine Learning (ICML’21) (Online)
Alexander Quinn Nichol and Prafulla Dhariwal. 2021 · 2021
Closest in time.
Learning Transferable Visual Models From Natural Language Supervision. In Proc. of the 38th International Conference on Machine Learning (ICML’21) (Online)
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
Closest in time.
Score-Based Generative Modeling through Stochastic Differential Equations. In Proc. of the 9th International Conference on Learning Representations (Online)
Yang Song, Yascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021 · 2021
Closest in time.
Multimodal Few-Shot Learning with Frozen Language Models. In Advances in Neural Information Processing Systems (NeurIPS’21) (Online)
Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, S. M. Ali Eslami, Oriol Vinyals, and Felix Hill. 2021 · 2021
Closest in time.
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Zhengwei Wang, Qi She, and Tomás E. Ward. 2021 · 2021
Closest in time.
VideoGPT: Video Generation using VQ-VAE and Transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. 2021 · 2021
Closest in time.
FUDGE: Controlled Text Generation With Future Discriminators. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL’21) (Online)
Kevin Yang and Dan Klein. 2021 · 2021
Closest in time.
VidTr: Video Transformer Without Convolutions. In Proc. of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV’21) (Montreal, Canada)
Yanyi Zhang, Xinyu Li, Chunhui Liu, Bing Shuai, Yi Zhu, Biagio Brattoli, Hao Chen, Ivan Marsic, and Joseph Tighe. 2021 · 2021
Closest in time.
Constitutional AI: Harmlessness from AI Feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, …, and Jared Kaplan. 2022 · 2022
Closest in time.
BEiT: BERT Pre-Training of Image Transformers. In Proc. of the 10th International Conference on Learning Representations (ICLR’22) (Online)
Hangbo Bao, Li Dong, and Furu Wei. 2022 · 2022
Closest in time.
AudioLM: a Language Modeling Approach to Audio Generation
Zalán Borsos, Raphaël Marinier, Damien Vincent, Eugene Kharitonov, Olivier Pietquin, Matt Sharifi, Olivier Teboul, David Grangier, Marco Tagliasacchi, and Neil Zeghidour. 2022 · 2022
Closest in time.
Generating Long Videos of Dynamic Scenes. In Advances in Neural Information Processing Systems (NeurIPS’22)
Tim Brooks, Janne Hellsten, Miika Aittala, Ting-Chun Wang, Timo Aila, Jaakko Lehtinen, Ming-Yu Liu, Alexei Efros, and Tero Karras. 2022 · 2022
Closest in time.
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance. In Proc. of the 17th European Conference on Computer Vision (ECCV’22) (Tel Aviv, Israel)
Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato, and Edward Raff. 2022 · 2022
Closest in time.
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. In Proc. of the 39th International Conference on Machine Learning (ICML’22) (Baltimore, MD)
Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, …, and Claire Cui. 2022 · 2022
Closest in time.
Towards Artificial General Intelligence via a Multimodal Foundation Model
Nanyi Fei, Zhiwu Lu, Yizhao Gao, Guoxing Yang, Yuqi Huo, Jingyuan Wen, Haoyu Lu, Ruihua Song, Xin Gao, Tao Xiang, Hao Sun, and Ji-Rong Wen. 2022 · 2022
Closest in time.
Copyright in Generative Deep Learning
Giorgio Franceschelli and Mirco Musolesi. 2022 · 2022
Closest in time.
Efficiently Modeling Long Sequences with Structured State Spaces. In Proc. of the 10th International Conference on Learning Representations (ICLR’22) (Online)
Albert Gu, Karan Goel, and Christopher Re. 2022 · 2022
Closest in time.
Masked Autoencoders Are Scalable Vision Learners. In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’22) . 16000–16009
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022 · 2022
Closest in time.
Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans. 2022a · 2022
Closest in time.
MuLan: A Joint Embedding of Music Audio and Natural Language. In Proc. of the 23rd International Society for Music Information Retrieval Conference (ISMIR’22) (Bengaluru, India)
Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, and Daniel P. W. Ellis. 2022 · 2022
Closest in time.
Music2Video: Automatic Generation of Music Video with Fusion of Audio and Text
Joel Jang, Sumin Shin, and Yoonjeon Kim. 2022 · 2022
Closest in time.
Transformers in Vision: A Survey
Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. 2022 · 2022
Closest in time.
Standing on the Shoulders of Giant Frozen Language Models
Yoav Levine, Itay Dalmedigos, Ori Ram, Yoel Zeldes, Daniel Jannai, Dor Muhlgay, Yoni Osin, Opher Lieber, Barak Lenz, Shai Shalev-Shwartz, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2022 · 2022
Closest in time.
Diffusion-LM Improves Controllable Text Generation. In Advances in Neural Information Processing Systems (NeurIPS’22) (New Orleans, LA)
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori Hashimoto. 2022 · 2022
Closest in time.
RePaint: Inpainting Using Denoising Diffusion Probabilistic Models. In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’22) (New Orleans, LA)
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022 · 2022
Closest in time.
Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022 · 2022
Closest in time.
A Generalist Agent
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas. 2022 · 2022
Closest in time.
High-Resolution Image Synthesis With Latent Diffusion Models. In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’22) (New Orleans, LA)
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
Closest in time.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. In Advances in Neural Information Processing Systems (NeurIPS’22) (New Orleans, LA)
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J Fleet, and Mohammad Norouzi. 2022 · 2022
Closest in time.
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods. In Proc. of the 10th International Conference on Learning Representations (ICLR’22) (Online)
Victor Schmidt, Alexandra Sasha Luccioni, Mélisande Teng, Tianyu Zhang, Alexia Reynaud, Sunand Raghupathi, Gautier Cosne, Adrien Juraver, Vahe Vardanyan, Alex Hernandez-Garcia, and Yoshua Bengio. 2022 · 2022
Closest in time.
Intelli-Paint: Towards Developing More Human-Intelligible Painting Agents. In Proc. of the 17th European Conference on Computer Vision (ECCV’22) (Tel Aviv, Israel)
Jaskirat Singh, Cameron Smith, Jose Echevarria, and Liang Zheng. 2022 · 2022
Closest in time.
StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2. In Proc. of the 2022 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’22) (New Orleans, LA)
Ivan Skorokhodov, Sergey Tulyakov, and Mohamed Elhoseiny. 2022 · 2022
Closest in time.
Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, and Bryan Catanzaro. 2022 · 2022
Closest in time.
Putting GPT-3’s Creativity to the (Alternative Uses) Test. In Proc. of the 13th International Conference on Computational Creativity (ICCC’22) (Bozen - Bolzano, Italy)
Claire Stevenson, Iris Smal, Matthijs Baas, Raoul Grasman, and Han van der Maas. 2022 · 2022
Closest in time.
A Survey of Multimodal Deep Generative Models
Masahiro Suzuki and Yutaka Matsuo. 2022 · 2022
Closest in time.
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts. In Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART 2022) (Madrid, Spain)
Yingtao Tian and David Ha. 2022 · 2022
Closest in time.
Natural Language Processing with Transformers
Lewis Tunstall, Leandro von Werra, and Thomas Wolf. 2022 · 2022
Closest in time.
Taxonomy of Risks Posed by Language Models. In Proc. of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT’22) (Seoul, Republic of Korea)
Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, …, and Iason Gabriel. 2022 · 2022
Closest in time.
Wav2CLIP: Learning Robust Audio Representations from CLIP. In Proc. of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’22) (Singapore, Singapore)
Ho-Hsiang Wu, Prem Seetharaman, Kundan Kumar, and Juan Pablo Bello. 2022 · 2022
Closest in time.
OPT: Open Pre-trained Transformer Language Models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
Closest in time.
MusicLM: Generating Music From Text
Andrea Agostinelli, Timo I. Denk, Zalán Borsos, Jesse Engel, Mauro Verzetti, Antoine Caillon, Qingqing Huang, Aren Jansen, Adam Roberts, Marco Tagliasacchi, Matt Sharifi, Neil Zeghidour, and Christian Frank. 2023 · 2023
Closest in time.
Training Diffusion Models with Reinforcement Learning. In ICML’23 Workshop on Efficient Systems for Foundation Models (Honolulu, HI)
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. 2023 · 2023
Closest in time.
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Segerie, Micah Carroll, Andi Peng, Phillip Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, …, and Dylan Hadfield-Menell. 2023 · 2023
Closest in time.
Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writing. In Proc. of the AAAI’23 Workshop on Creative AI Across Modalities (Washington, DC)
Tuhin Chakrabarty, Vishakh Padmakumar, and He He. 2023 · 2023
Closest in time.
Quality Diversity through Human Feedback. In Proc. of the NeurIPS’23 Workshop ALOE (New Orleans, LA)
Li Ding, Jenny Zhang, Jeff Clune, Lee Spector, and Joel Lehman. 2023 · 2023
Closest in time.
CLAP: Learning Audio Concepts From Natural Language Supervision. In Proc. of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’23) (Rhodes Island, Greece)
Benjamin Elizalde, Soham Deshmukh, Mahmoud Al Ismail, and Huaming Wang. 2023 · 2023
Closest in time.
Gemini: A Family of Highly Capable Multimodal Models
Gemini Team and Google. 2023 · 2023
Closest in time.
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models. In Proc. of the 11th International Conference on Learning Representations (ICLR’23) (Kigali, Rwanda)
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong. 2023 · 2023
Closest in time.
Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, and Yuanzhi Li. 2023 · 2023
Closest in time.
Foundation Models and Fair Use
Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, and Percy Liang. 2023 · 2023
Closest in time.
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu, Thomas Mesnard, Colton Bishop, Victor Carbune, and Abhinav Rastogi. 2023 · 2023
Closest in time.
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models. In Proc. of the 40th International Conference on Machine Learning (ICML’23) (Honolulu, HA). 21450–21474
Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Leveraging Human Preferences to Master Poetry. In Proc. of the AAAI’23 Workshop on Creative AI Across Modalities (Washington, DC)
Rafael Pardinas, Gabriel Huang, David Vazquez, and Alexandre Piché. 2023 · 2023
Closest in time.
RWKV: Reinventing RNNs for the Transformer Era. In Findings of the Association for Computational Linguistics: EMNLP’23 (Singapore)
Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, …, and Rui-Jie Zhu. 2023 · 2023
Closest in time.
Direct Preference Optimization: Your Language Model is Secretly a Reward Model. In Proc. of the 37th Conference on Neural Information Processing Systems (NeurIPS’23) (New Orleans, LA)
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. 2023 · 2023
Closest in time.
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’23) (Vancouver, Canada)
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023 · 2023
Closest in time.
Offline RL for Natural Language Generation with Implicit Language Q Learning. In Proc. of the 11th International Conference on Learning Representations (ICLR’23) (Kigali, Rwanda)
Charlie V. Snell, Ilya Kostrikov, Yi Su, Sherry Yang, and Sergey Levine. 2023 · 2023
Closest in time.
Consistency models. In Proc. of the 40th International Conference on Machine Learning (ICML’23) (Honolulu, HA)
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. 2023 · 2023
Closest in time.
Brainstorm, then Select: a Generative Language Model Improves Its Creativity Score. In Proc. of the AAAI’23 Workshop on Creative AI Across Modalities (Washington, DC)
Douglas Summers-Stay, Clare R. Voss, and Stephanie M. Lukin. 2023 · 2023
Closest in time.
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
Chengyi Wang, Sanyuan Chen, Yu Wu, Ziqiang Zhang, Long Zhou, Shujie Liu, Zhuo Chen, Yanqing Liu, Huaming Wang, Jinyu Li, Lei He, Sheng Zhao, and Furu Wei. 2023 · 2023
Closest in time.
NExT-GPT: Any-to-Any Multimodal LLM
Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. 2023 · 2023
Closest in time.
Diffusion Models: A Comprehensive Survey of Methods and Applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. 2023 · 2023
Closest in time.
Improving Image Generation with Better Captions
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, Wesam Manassra, Prafulla Dhariwal, Casey Chu, Yunxin Jiao, and Aditya Ramesh. 2024 · 2024
Closest in time.
Quality-Diversity through AI Feedback. In Proc. of the 12th International Conference on Learning Representations (ICLR’24) (Wien, Austria)
Herbie Bradley, Andrew Dai, Hannah Teufel, Jenny Zhang, Koen Oostermeijer, Marco Bellagente, Jeff Clune, Kenneth Stanley, Grégory Schott, and Joel Lehman. 2024 · 2024
Closest in time.
Video generation models as world simulators
Tim Brooks, Bill Peebles, Connor Homes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Wing Yin Ng, Ricky Wang, and Aditya Ramesh. 2024 · 2024
Closest in time.
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
Giorgio Franceschelli and Mirco Musolesi. 2024 · 2024
Closest in time.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team and Google. 2024 · 2024
Closest in time.
Introducing ChatGPT
OpenAI. 2022 · 2024
Closest in time.
MuseNet
Christine Payne. 2019 · 2024
Closest in time.
Improving Language Understanding with Unsupervised Learning
Alec Radford. 2018 · 2024
Closest in time.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2024
Closest in time.
Alien Dreams: An Emerging Art Scene
Charlie Snell. 2021 · 2024
Closest in time.
Assessing and Understanding Creativity in Large Language Models
Yunpu Zhao, Rui Zhang, Wenyi Li, Di Huang, Jiaming Guo, Shaohui Peng, Yifan Hao, Yuanbo Wen, Xing Hu, Zidong Du, Qi Guo, Ling Li, and Yunji Chen. 2024 · 2024
Closest in time.