289 citations · 1.3k across the 22 of their papers we have counts for
12 papers · 1 filter
Small-scale proxies for large-scale Transformer training instabilities
Mitchell Wortsman, Peter J. Liu, Lechao Xiao +13
Teams that have trained large Transformer-based models have reported training instabilities at large scale that did not appear when training with the same hyperparameters at smalle…
Regularized Autoencoders via Relaxed Injective Probability Flow
Abhishek Kumar, Ben Poole, Kevin Murphy
Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation and inference. However, the in…
Refined -Divergence Variational Inference via Rejection Sampling
Rahul Sharma, Abhishek Kumar, Piyush Rai
We present an approximate inference method, based on a synergistic combination of Rényi -divergence variational inference (RDVI) and rejection sampling (RS). RDVI is based on mi…
Weakly Supervised Disentanglement with Guarantees
Rui Shu, Yining Chen, Abhishek Kumar +2
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, con…
Co-regularized Alignment for Unsupervised Domain Adaptation
Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan +4
Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a \emph{target domain} whose distribution differs from t…
The Riemannian Geometry of Deep Generative Models
Hang Shao, Abhishek Kumar, P. Thomas Fletcher
Deep generative models learn a mapping from a low dimensional latent space to a high-dimensional data space. Under certain regularity conditions, these models parameterize nonlinea…