Fetching the paper…
Reading the bibliography…
The vast applications of deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations -- across various data types, such as discrete text/protein sequences and continuous images.
Animating rotation with quaternion curves
Shoemake, K · 1985
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. C., and Bengio, Y · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
LSUN: construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
Earlier work this paper cites.
Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., and Fidler, S · 2015
Earlier work this paper cites.
Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Józefowicz, R., and Bengio, S · 2016
Earlier work this paper cites.
Deep unsupervised clustering with gaussian mixture variational autoencoders
Dilokthanakul, N., Mediano, P. A. M., Garnelo, M., Lee, M. C. H., Salimbeni, H., Arulkumaran, K., and Shanahan, M · 2016
Earlier work this paper cites.
Donahue, J., Krähenbühl, P., and Darrell, T · 2016
Earlier work this paper cites.
Deep Generative Models , chapter 20
Goodfellow, I., Bengio, Y., and Courville, A · 2016
Earlier work this paper cites.
Gans for sequences of discrete elements with the gumbel-softmax distribution, 2016
Kusner, M. J. and Hernández-Lobato, J. M · 2016
Earlier work this paper cites.
Autoencoding beyond pixels using a learned similarity metric
Larsen, A. B. L., Sønderby, S. K., Larochelle, H., and Winther, O · 2016
Earlier work this paper cites.
Local fitness landscape of the green fluorescent protein
Sarkisyan, K. S., Bolotin, D. A., Meer, M. V., Usmanova, D. R., Mishin, A. S., Sharonov, G. V., Ivankov, D. N., Bozhanova, N. G., Baranov, M. S., Soylemez, O., Bogatyreva, N. S., Vlasov, P. K., Egorov, E. S., Logacheva, M. D., Kondrashov, A. S., Chudakov, D. M., Putintseva, E. V., Mamedov, I. Z., Tawfik, D. S., Lukyanov, K. A., and Kondrashov, F. A · 2016
Earlier work this paper cites.
Variational lossy autoencoder
Chen, X., Kingma, D. P., Salimans, T., Duan, Y., Dhariwal, P., Schulman, J., Sutskever, I., and Abbeel, P · 2017
Earlier work this paper cites.
Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., and Courville, A. C · 2017
Earlier work this paper cites.
Nonparametric variational auto-encoders for hierarchical representation learning
Goyal, P., Hu, Z., Liang, X., Wang, C., and Xing, E. P · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C. P., Glorot, X., Botvinick, M. M., Mohamed, S., and Lerchner, A · 2017
Earlier work this paper cites.
Toward controlled generation of text
Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., and Xing, E. P · 2017
Earlier work this paper cites.
Improving variational inference with inverse autoregressive flow, 2017
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2017
Earlier work this paper cites.
ALICE: towards understanding adversarial learning for joint distribution matching
Li, C., Liu, H., Chen, C., Pu, Y., Chen, L., Henao, R., and Carin, L · 2017
Earlier work this paper cites.
Style transfer from non-parallel text by cross-alignment
Shen, T., Lei, T., Barzilay, R., and Jaakkola, T · 2017
Earlier work this paper cites.
Improved variational autoencoders for text modeling using dilated convolutions
Yang, Z., Hu, Z., Salakhutdinov, R., and Berg-Kirkpatrick, T · 2017
Earlier work this paper cites.
Seqgan: Sequence generative adversarial nets with policy gradient, 2017
Yu, L., Zhang, W., Wang, J., and Yu, Y · 2017
Earlier work this paper cites.
Inverting the generator of a generative adversarial network
Creswell, A. and Bharath, A. A · 2018
Earlier work this paper cites.
On unifying deep generative models
Hu, Z., Yang, Z., Salakhutdinov, R., and Xing, E. P · 2018
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
Cited alongside, same era.
Delete, retrieve, generate: A simple approach to sentiment and style transfer
Li, J., Jia, R., He, H., and Liang, P · 2018
Cited alongside, same era.
VAE with a vampprior
Tomczak, J. M. and Welling, M · 2018
Cited alongside, same era.
Diagnosing and enhancing VAE models
Dai, B. and Wipf, D · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
NVAE-GAN based approach for unsupervised time series anomaly detection
Xu, L., Zheng, L., Li, W., Chen, Z., Song, W., Deng, Y., Chang, Y., Xiao, J., and Yuan, B · 2021
Later among the works it cites.
Cold diffusion: Inverting arbitrary image transforms without noise
Bansal, A., Borgnia, E., Chu, H., Li, J. S., Kazemi, H., Huang, F., Goldblum, M., Geiping, J., and Goldstein, T · 2022
Later among the works it cites.
Relso: A transformer-based model for latent space optimization and generation of proteins, 2022
Castro, E., Godavarthi, A., Rubinfien, J., Givechian, K. B., Bhaskar, D., and Krishnaswamy, S · 2022
Later among the works it cites.
Diffusionbert: Improving generative masked language models with diffusion models, 2022
He, Z., Sun, T., Wang, K., Huang, X., and Qiu, X · 2022
Later among the works it cites.
Toward a ’Standard Model’ of Machine Learning
Hu, Z. and Xing, E. P · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cyclical annealing schedule: A simple approach to mitigating kl vanishing, 2019
Fu, H., Li, C., Liu, X., Gao, J., Celikyilmaz, A., and Carin, L · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
Cited alongside, same era.
Antibody complementarity determining region design using high-capacity machine learning
Liu, G., Zeng, H., Mueller, J., Carter, B., Wang, Z., Schilz, J., Horny, G., Birnbaum, M. E., Ewert, S., and Gifford, D. K · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Generating diverse high-fidelity images with VQ-VAE-2
Razavi, A., van den Oord, A., and Vinyals, O · 2019
Cited alongside, same era.
Unsupervised representation adversarial learning network: from reconstruction to generation
Zhou, Y., Gu, K., and Huang, T. S · 2019
Cited alongside, same era.
Later among the works it cites.
A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27
LeCun, Y · 2022
Later among the works it cites.
Diffusion-lm improves controllable text generation, 2022
Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., and Hashimoto, T. B · 2022
Later among the works it cites.
Don’t take it literally: An edit-invariant sequence loss for text generation, 2022
Liu, G., Yang, Z., Tao, T., Liang, X., Bao, J., Li, Z., He, X., Cui, S., and Hu, Z · 2022
Later among the works it cites.
Diffusion autoencoders: Toward a meaningful and decodable representation
Preechakul, K., Chatthee, N., Wizadwongsa, S., and Suwajanakorn, S · 2022
Later among the works it cites.
Cold decoding: Energy-based constrained text generation with langevin dynamics
Qin, L., Welleck, S., Khashabi, D., and Choi, Y · 2022
Later among the works it cites.
Stylegan-xl: Scaling stylegan to large diverse datasets
Sauer, A., Schwarz, K., and Geiger, A · 2022
Later among the works it cites.
GAN inversion: A survey
Xia, W., Zhang, Y., Yang, Y., Xue, J., Zhou, B., and Yang, M · 2022
Later among the works it cites.
Gpt-4 technical report, 2023
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Later among the works it cites.
Self-supervised learning from images with a joint-embedding predictive architecture
Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., LeCun, Y., and Ballas, N · 2023
Later among the works it cites.
Hvae: A deep generative model via hierarchical variational auto-encoder for multi-view document modeling
Bai, R., Huang, R., Qin, Y., Chen, Y., and Lin, C · 2023
Later among the works it cites.
Protein design with guided discrete diffusion, 2023
Gruver, N., Stanton, S., Frey, N. C., Rudner, T. G. J., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A. G · 2023
Later among the works it cites.
Reasoning with language model is planning with world model
Hao, S., Gu, Y., Ma, H., Hong, J., Wang, Z., Wang, D., and Hu, Z · 2023
Later among the works it cites.
Language models, agent models, and world models: The law for machine reasoning and planning
Hu, Z. and Shu, T · 2023
Later among the works it cites.
Text generation with diffusion language models: A pre-training approach with continuous paragraph denoise, 2023
Lin, Z., Gong, Y., Shen, Y., Wu, T., Fan, Z., Lin, C., Duan, N., and Chen, W · 2023
Later among the works it cites.
Composable text controls in latent space with ODEs
Liu, G., Feng, Z., Gao, Y., Yang, Z., Liang, X., Bao, J., He, X., Cui, S., Li, Z., and Hu, Z · 2023
Later among the works it cites.
Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
Later among the works it cites.
Wong, L., Grand, G., Lew, A. K., Goodman, N. D., Mansinghka, V. K., Andreas, J., and Tenenbaum, J. B · 2023
Later among the works it cites.
Ar-diffusion: Auto-regressive diffusion model for text generation, 2023
Wu, T., Fan, Z., Liu, X., Gong, Y., Shen, Y., Jiao, J., Zheng, H.-T., Li, J., Wei, Z., Guo, J., Duan, N., and Chen, W · 2023
Later among the works it cites.
Dinoiser: Diffused conditional sequence learning by manipulating noises, 2023
Ye, J., Zheng, Z., Bao, Y., Qian, L., and Wang, M · 2023
Later among the works it cites.
Seqdiffuseq: Text diffusion with encoder-decoder transformers, 2023
Yuan, H., Yuan, Z., Tan, C., Huang, F., and Huang, S · 2023
Later among the works it cites.