Fetching the paper…
Reading the bibliography…
Recent advances in foundation models have led to a promising trend of developing large recommendation models to leverage vast amounts of available data.
On principal angles between subspaces in rn
Miao, J. and Ben-Israel, A · 1992
Earlier work this paper cites.
Maximum likelihood estimation of intrinsic dimension
Levina, E. and Bickel, P · 2004
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Koren, Y., Bell, R., and Volinsky, C · 2009
Earlier work this paper cites.
Factorization machines
Rendle, S · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Nonlinear latent factorization by embedding multiple user interests
Weston, J., Weiss, R. J., and Yee, H · 2013
Earlier work this paper cites.
Display advertising challenge, 2014
Jean-Baptiste Tien, joycenv, O. C · 2014
Earlier work this paper cites.
Click-through rate prediction, 2014
Steve Wang, W. C · 2014
Earlier work this paper cites.
Wide & deep learning for recommender systems
Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., et al · 2016
Earlier work this paper cites.
Field-aware Factorization Machines for CTR Prediction
Juan, Y., Zhuang, Y., Chin, W.-S., and Lin, C.-J · 2016
Earlier work this paper cites.
Product-based neural networks for user response prediction
Qu, Y., Cai, H., Ren, K., Zhang, W., Yu, Y., Wen, Y., and Wang, J · 2016
Earlier work this paper cites.
Deep learning over multi-field categorical data: A case study on user response prediction
Zhang, W., Du, T., and Wang, J · 2016
Earlier work this paper cites.
Compression-aware training of deep networks
Alvarez, J. M. and Salzmann, M · 2017
Earlier work this paper cites.
Deepfm: a factorization-machine based neural network for ctr prediction
Guo, H., Tang, R., Ye, Y., Li, Z., and He, X · 2017
Earlier work this paper cites.
Neural factorization machines for sparse predictive analytics
He, X. and Chua, T.-S · 2017
Earlier work this paper cites.
Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Paul Smolley, S · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Coordinating filters for faster deep neural networks
Wen, W., Xu, C., Wu, C., Wang, Y., Chen, Y., and Li, H · 2017
Cited alongside, same era.
xdeepfm: Combining explicit and implicit feature interactions for recommender systems
Lian, J., Zhou, X., Zhang, F., Chen, Z., Xie, X., and Sun, G · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
Cited alongside, same era.
Field-weighted factorization machines for click-through rate prediction in display advertising
Pan, J., Xu, J., Ruiz, A. L., Zhao, W., Pan, S., Sun, Y., and Lu, Q · 2018
Cited alongside, same era.
Intrinsic dimension of data representations in deep neural networks
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Later among the works it cites.
Fm2: Field-matrixed factorization machines for recommender systems
Sun, Y., Pan, J., Zhang, A., and Flores, A · 2021
Later among the works it cites.
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Wang, R., Shivanna, R., Cheng, D., Jain, S., Lin, D., Hong, L., and Chi, E · 2021
Later among the works it cites.
A geometric analysis of neural collapse with unconstrained features
Zhu, Z., Ding, T., Zhou, J., Li, X., You, C., Sulam, J., and Qu, Q · 2021
Later among the works it cites.
On the representation collapse of sparse mixture of experts
Chi, Z., Dong, L., Huang, S., Dai, D., Ma, S., Patra, B., Singhal, S., Bajaj, P., Song, X., Mao, X.-L., et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ansuini, A., Laio, A., Macke, J. H., and Zoccolan, D · 2019
Cited alongside, same era.
Is a single vector enough? exploring node polysemy for network embedding
Liu, N., Tan, Q., Li, Y., Yang, H., Zhou, J., and Hu, X · 2019
Cited alongside, same era.
Autoint: Automatic feature interaction learning via self-attentive neural networks
Song, W., Shi, C., Xiao, Z., Duan, Z., Xu, Y., Zhang, M., and Tang, J · 2019
Cited alongside, same era.
Better fine-tuning by reducing representational collapse
Aghajanyan, A., Shrivastava, A., Gupta, A., Goyal, N., Zettlemoyer, L., and Gupta, S · 2020
Cited alongside, same era.
Adaptive factorization network: Learning adaptive-order feature interactions
Cheng, W., Shen, Y., and Huang, L · 2020
Cited alongside, same era.
Prevalence of neural collapse during the terminal phase of deep learning training
Papyan, V., Han, X., and Donoho, D. L · 2020
Cited alongside, same era.
The intrinsic dimension of images and its impact on learning
Pope, P., Zhu, C., Abdelkader, A., Goldblum, M., and Goldstein, T · 2020
Cited alongside, same era.
Understanding and improving the role of projection head in self-supervised learning
Gupta, K., Ajanthan, T., Hengel, A. v. d., and Gould, S · 2022
Later among the works it cites.
Fine-tuning can distort pretrained features and underperform out-of-distribution
Kumar, A., Raghunathan, A., Jones, R., Ma, T., and Liang, P · 2022
Later among the works it cites.
Disentangled multimodal representation learning for recommendation
Liu, F., Chen, H., Cheng, Z., Liu, A., Nie, L., and Kankanhalli, M · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Later among the works it cites.
Extended unconstrained features model for exploring deep neural collapse
Tirer, T. and Bruna, J · 2022
Later among the works it cites.
Bars: Towards open benchmarking for recommender systems
Zhu, J., Dai, Q., Su, L., Ma, R., Liu, J., Cai, G., Xiao, X., and Zhang, R · 2022
Later among the works it cites.
Segment anything
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al · 2023
Closest in time.
Finalmlp: An enhanced two-stream mlp model for ctr prediction
Mao, K., Zhu, J., Su, L., Cai, G., Li, Y., and Dong, Z · 2023
Closest in time.
Gpt-4 technical report
OpenAI · 2023
Closest in time.
Eulernet: Adaptive feature interaction learning via euler’s formula for ctr prediction
Tian, Z., Bai, T., Zhao, W. X., Wen, J.-R., and Cao, Z · 2023
Closest in time.
Ad recommendation in a collapsed and entangled world
Pan, J., Xue, W., Wang, X., Yu, H., Liu, X., Quan, S., Qiu, X., Liu, D., Xiao, L., and Jiang, J · 2024
Closest in time.