95 citations · 430 across the 31 of their papers we have counts for
23 papers · 1 filter
Improving Molecule Properties Through 2-Stage VAE
Chenghui Zhou, Barnabas Poczos
Variational autoencoder (VAE) is a popular method for drug discovery and there had been a great deal of architectures and pipelines proposed to improve its performance. But the VAE…
Covariate Distribution Aware Meta-learning
Amrith Setlur, Saket Dingliwal, Barnabas Poczos
Meta-learning has proven to be successful for few-shot learning across the regression, classification, and reinforcement learning paradigms. Recent approaches have adopted Bayesian…
Minimizing FLOPs to Learn Efficient Sparse Representations
Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen +3
Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification. Retrieval of such representations from a la…
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets
Ilqar Ramazanli, Han Nguyen, Hai Pham +2
We study distributed optimization algorithms for minimizing the average of \emph{heterogeneous} functions distributed across several machines with a focus on communication efficien…
RotationOut as a Regularization Method for Neural Network
Kai Hu, Barnabas Poczos
In this paper, we propose a novel regularization method, RotationOut, for neural networks. Different from Dropout that handles each neuron/channel independently, RotationOut regard…
Better Approximate Inference for Partial Likelihood Models with a Latent Structure
Amrith Setlur, Barnabás Póczós
Temporal Point Processes (TPP) with partial likelihoods involving a latent structure often entail an intractable marginalization, thus making inference hard. We propose a novel app…