Fetching the paper…
Reading the bibliography…
The rapid progress in generative models has resulted in impressive leaps in generation quality, blurring the lines between synthetic and real data.
Rank analysis of incomplete block designs: I. the method of paired comparisons
R. A. Bradley and M. E. Terry · 1952
Earlier work this paper cites.
Stimulus and response generalization: A stochastic model relating generalization to distance in psychological space. psychometrika22: 32545.[dwm](1958) stimulus and response generalization: Deduction of the generalization gradient from a trace model
R. Shepard · 1957
Earlier work this paper cites.
Handbook of mathematical psychology: I
R. Luce, R. R. Bush, and E. E. Galanter · 1963
Earlier work this paper cites.
Individual choice behavior: A theoretical analysis
R. D. Luce · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
Optimal transport: old and new
C. Villani et al · 2009
Earlier work this paper cites.
The weirdest people in the world?
J. Henrich, S. J. Heine, and A. Norenzayan · 2010
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Deep reinforcement learning from human preferences
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei · 2017
Earlier work this paper cites.
Bias correction of learned generative models using likelihood-free importance weighting
A. Grover, J. Song, A. Kapoor, K. Tran, A. Agarwal, E. J. Horvitz, and S. Ermon · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Earlier work this paper cites.
Learning to summarize with human feedback
N. Stiennon, L. Ouyang, J. Wu, D. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P. F. Christiano · 2020
Earlier work this paper cites.
Multimodal datasets: misogyny, pornography, and malignant stereotypes
A. Birhane, V. U. Prabhu, and E. Kahembwe · 2021
Earlier work this paper cites.
K. Lee, L. Smith, and P. Abbeel · 2021
Earlier work this paper cites.
Zero-shot text-to-image generation
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever · 2021
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
Earlier work this paper cites.
Data representativity for machine learning and AI systems
L. H. Clemmensen and R. D. Kjærsgaard · 2022
Earlier work this paper cites.
On reinforcement learning and distribution matching for fine-tuning language models with no catastrophic forgetting
T. Korbak, H. Elsahar, G. Kruszewski, and M. Dymetman · 2022
Earlier work this paper cites.
Flow matching for generative modeling
Y. Lipman, R. T. Chen, H. Ben-Hamu, M. Nickel, and M. Le · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
Cited alongside, same era.
Simulacra aesthetic captions
J. D. Pressman, K. Crowson, and S. C. Contributors · 2022
Cited alongside, same era.
LAION-5B: An open large-scale dataset for training next generation image-text models
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, et al · 2022
Cited alongside, same era.
Defining and characterizing reward gaming
J. Skalse, N. Howe, D. Krasheninnikov, and D. Krueger · 2022
Cited alongside, same era.
Phenaki: Variable length video generation from open domain textual descriptions
R. Villegas, M. Babaeizadeh, P.-J. Kindermans, H. Moraldo, H. Zhang, M. T. Saffar, S. Castro, J. Kunze, and D. Erhan · 2022
Cited alongside, same era.
On kinetic optimal probability paths for generative models
N. Shaul, R. T. Chen, M. Nickel, M. Le, and Y. Lipman · 2023
Later among the works it cites.
Large language model alignment: A survey
T. Shen, R. Jin, Y. Huang, C. Liu, W. Dong, Z. Guo, X. Wu, Y. Liu, and D. Xiong · 2023
Later among the works it cites.
Benchmarks and algorithms for offline preference-based reward learning
D. Shin, A. D. Dragan, and D. S. Brown · 2023
Later among the works it cites.
The curse of recursion: Training on generated data makes models forget
I. Shumailov, Z. Shumaylov, Y. Zhao, Y. Gal, N. Papernot, and R. Anderson · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
Cited alongside, same era.
Synthetic data from diffusion models improves imagenet classification
S. Azizi, S. Kornblith, C. Saharia, M. Norouzi, and D. J. Fleet · 2023
Cited alongside, same era.
Audiolm: a language modeling approach to audio generation
Z. Borsos, R. Marinier, D. Vincent, E. Kharitonov, O. Pietquin, M. Sharifi, D. Roblek, O. Teboul, D. Grangier, M. Tagliasacchi, et al · 2023
Cited alongside, same era.
Large language models suffer from their own output: An analysis of the self-consuming training loop
M. Briesch, D. Sobania, and F. Rothlauf · 2023
Cited alongside, same era.
A density estimation perspective on learning from pairwise human preferences
V. Dumoulin, D. D. Johnson, P. S. Castro, H. Larochelle, and Y. Dauphin · 2023
Cited alongside, same era.
Aligning language models with preferences through f-divergence minimization
D. Go, T. Korbak, G. Kruszewski, J. Rozen, N. Ryu, and M. Dymetman · 2023
Cited alongside, same era.
Reinforced self-training (rest) for language modeling
C. Gulcehre, T. L. Paine, S. Srinivasan, K. Konyushkova, L. Weerts, A. Sharma, A. Siddhant, Ahern, A., Wang, M., C. Gu, and W. Macherey · 2023
Cited alongside, same era.
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al · 2023
Later among the works it cites.
Better diffusion models further improve adversarial training
Z. Wang, T. Pang, C. Du, M. Lin, W. Liu, and S. Yan · 2023
Later among the works it cites.
https://huggingface.co/spaces/stabilityai/stable-diffusion
Hugging face Stable Diffusion 2.1 · 2024
Closest in time.
Self-consuming generative models go MAD
S. Alemohammad, J. Casco-Rodriguez, L. Luzi, A. I. Humayun, H. Babaei, D. LeJeune, A. Siahkoohi, and R. G. Baraniuk · 2024
Closest in time.
A general theoretical paradigm to understand learning from human preferences
M. G. Azar, Z. D. Guo, B. Piot, R. Munos, M. Rowland, M. Valko, and D. Calandriello · 2024
Closest in time.
On the stability of iterative retraining of generative models on their own data
Q. Bertrand, A. J. Bose, A. Duplessis, M. Jiralerspong, and G. Gidel · 2024
Closest in time.
Into the LAION’s Den: Investigating hate in multimodal datasets
A. Birhane, S. Han, V. Boddeti, S. Luccioni, et al · 2024
Closest in time.
Video generation models as world simulators
T. Brooks, B. Peebles, C. Holmes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, and A. Ramesh · 2024
Closest in time.
Kto: Model alignment as prospect theoretic optimization
K. Ethayarajh, W. Xu, N. Muennighoff, D. Jurafsky, and D. Kiela · 2024
Closest in time.
M. Gerstgrasser, R. Schaeffer, A. Dey, R. Rafailov, H. Sleight, J. Hughes, T. Korbak, R. Agrawal, D. Pai, A. Gromov, et al · 2024
Closest in time.
Self-correcting self-consuming loops for generative model training
N. Gillman, M. Freeman, D. Aggarwal, C. H. Hsu, C. Luo, Y. Tian, and C. Sun · 2024
Closest in time.
Direct preference optimization: Your language model is secretly a reward model
R. Rafailov, A. Sharma, E. Mitchell, C. D. Manning, S. Ermon, and C. Finn · 2024
Closest in time.
https://stability.ai/stablediffusion , 2023
Stability AI · 2024
Closest in time.
Improving and generalizing flow-based generative models with minibatch optimal transport
A. Tong, K. Fatras, N. Malkin, G. Huguet, Y. Zhang, J. Rector-Brooks, G. Wolf, and Y. Bengio · 2024
Closest in time.
Fairness feedback loops: Training on synthetic data amplifies bias
S. Wyllie, I. Shumailov, and N. Papernot · 2024
Closest in time.