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20162022
most citedAutomatic feature learning for vulnerability prediction

88 citations · 260 across the 29 of their papers we have counts for

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6 papers · 1 filter

stat.ML2018

Hybrid Generative-Discriminative Models for Inverse Materials Design

Phuoc Nguyen, Truyen Tran, Sunil Gupta +2

Discovering new physical products and processes often demands enormous experimentation and expensive simulation. To design a new product with certain target characteristics, an ext…

stat.ML20173 cited

One Size Fits Many: Column Bundle for Multi-X Learning

Trang Pham, Truyen Tran, Svetha Venkatesh

Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-inst…

stat.ML2016

An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments

Thuong Nguyen, Truyen Tran, Shivapratap Gopakumar +2

Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many fa…

stat.ML2016

Learning deep representation of multityped objects and tasks

Truyen Tran, Dinh Phung, Svetha Venkatesh

We introduce a deep multitask architecture to integrate multityped representations of multimodal objects. This multitype exposition is less abstract than the multimodal characteriz…

stat.ML2016

Choice by Elimination via Deep Neural Networks

Truyen Tran, Dinh Phung, Svetha Venkatesh

We introduce Neural Choice by Elimination, a new framework that integrates deep neural networks into probabilistic sequential choice models for learning to rank. Given a set of ite…

stat.ML2016

Collaborative filtering via sparse Markov random fields

Truyen Tran, Dinh Phung, Svetha Venkatesh

Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the…