Towards a framework on tabular synthetic data generation: a minimalist approach: theory, use cases, and limitations

no code implementations • 17 Nov 2024 • Yueyang Shen, Agus Sudjianto, Arun Prakash R, Anwesha Bhattacharyya, Maorong Rao, Yaqun Wang, Joel Vaughan, Nengfeng Zhou

We propose and study a minimalist approach towards synthetic tabular data generation.

Decoder Synthetic Data Generation +1

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling

no code implementations • 25 Oct 2020 • Akash Srivastava, Yamini Bansal, Yukun Ding, Cole Lincoln Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Joshua B. Tenenbaum, Agus Sudjianto, Phuong Le, Arun Prakash R, Nengfeng Zhou, Joel Vaughan, Yaqun Wang, Anwesha Bhattacharyya, Kristjan Greenewald, David D. Cox, Dan Gutfreund

Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.

Disentanglement