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Found 8 papers, 1 papers with code
Path Auxiliary Proposal for MCMC in Discrete Space
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
We demonstrate FFD-GAN's performance using a wing shape design example.
Answering Any-hop Open-domain Questions with Iterative Document Reranking
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.
Ranked #16 on
Question Answering
on HotpotQA
Conditional Neural Architecture Search
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.
Generative Design of Hardware-aware DNNs
We propose a new way for autonomous quantization and HW-aware tuning.
DC-BERT: Decoupling Question and Document for Efficient Contextual Encoding
Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.
Can Graph Neural Networks Help Logic Reasoning?
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.
