Search Results for author: Arun Ramamurthy

Found 8 papers, 1 papers with code

Path Auxiliary Proposal for MCMC in Discrete Space

no code implementations ICLR 2022 Haoran Sun, Hanjun Dai, Wei Xia, Arun Ramamurthy

Energy-based Model (EBM) offers a powerful approach for modeling discrete structure, but both inference and learning of EBM are hard as it involves sampling from discrete distributions.

Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation

no code implementations7 Jan 2021 Wei Chen, Arun Ramamurthy

We demonstrate FFD-GAN's performance using a wing shape design example.

Answering Any-hop Open-domain Questions with Iterative Document Reranking

no code implementations16 Sep 2020 Ping Nie, Yuyu Zhang, Arun Ramamurthy, Le Song

Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered.

Multi-hop Question Answering Natural Questions +2

Conditional Neural Architecture Search

no code implementations6 Jun 2020 Sheng-Chun Kao, Arun Ramamurthy, Reed Williams, Tushar Krishna

Designing resource-efficient Deep Neural Networks (DNNs) is critical to deploy deep learning solutions over edge platforms due to diverse performance, power, and memory budgets.

Neural Architecture Search

Generative Design of Hardware-aware DNNs

no code implementations6 Jun 2020 Sheng-Chun Kao, Arun Ramamurthy, Tushar Krishna

We propose a new way for autonomous quantization and HW-aware tuning.


DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding

no code implementations28 Feb 2020 Yuyu Zhang, Ping Nie, Xiubo Geng, Arun Ramamurthy, Le Song, Daxin Jiang

Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.

Natural Questions Open-Domain Question Answering +1

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

1 code implementation ICLR 2020 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.

Variational Inference

Can Graph Neural Networks Help Logic Reasoning?

no code implementations5 Jun 2019 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.

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