Search Results for author: Jerret Ross

Found 6 papers, 2 papers with code

Do Large Scale Molecular Language Representations Capture Important Structural Information?

no code implementations17 Jun 2021 Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi, Youssef Mroueh, Payel Das

Various representation learning methods in a supervised setting, including the features extracted using graph neural nets, have emerged for such tasks.

Drug Discovery Molecular Property Prediction +1

Tabular Transformers for Modeling Multivariate Time Series

1 code implementation3 Nov 2020 Inkit Padhi, Yair Schiff, Igor Melnyk, Mattia Rigotti, Youssef Mroueh, Pierre Dognin, Jerret Ross, Ravi Nair, Erik Altman

This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.

Fraud Detection Synthetic Data Generation +1

Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets

no code implementations ICLR 2020 Mingrui Liu, Youssef Mroueh, Jerret Ross, Wei zhang, Xiaodong Cui, Payel Das, Tianbao Yang

Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \emph{improved} adaptive complexity $O\left(\epsilon^{-\frac{2}{1-\alpha}}\right)$, where $\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq \alpha\leq 1/2$.

A Decentralized Parallel Algorithm for Training Generative Adversarial Nets

no code implementations NeurIPS 2020 Mingrui Liu, Wei zhang, Youssef Mroueh, Xiaodong Cui, Jerret Ross, Tianbao Yang, Payel Das

Despite recent progress on decentralized algorithms for training deep neural networks, it remains unclear whether it is possible to train GANs in a decentralized manner.

Wasserstein Barycenter Model Ensembling

1 code implementation13 Feb 2019 Pierre Dognin, Igor Melnyk, Youssef Mroueh, Jerret Ross, Cicero dos Santos, Tom Sercu

In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters.

General Classification Image Captioning +1

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