activity
20122021
most citedEfficient Architecture Search by Network Transformation

320 citations · 736 across the 17 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.AI20196 cited

Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning

Hangyu Mao, Wulong Liu, Jianye Hao +5

Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about thei…

cs.IR2019

GREASE: A Generative Model for Relevance Search over Knowledge Graphs

Tianshuo Zhou, Ziyang Li, Gong Cheng +2

Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBped…

stat.ML2019

Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds

Dagui Chen, Junqi Jin, Weinan Zhang +7

Most e-commerce product feeds provide blended results of advertised products and recommended products to consumers. The underlying advertising and recommendation platforms share si…

cs.LG201988 cited

BayesNAS: A Bayesian Approach for Neural Architecture Search

Hongpeng Zhou, Minghao Yang, Jun Wang +1

One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network Compression proble…

cs.LG2019

Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings

Alexander I. Cowen-Rivers, Pasquale Minervini, Tim Rocktaschel +3

Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. While traditional varia…

cs.MA2019

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

Jiarui Jin, Ming Zhou, Weinan Zhang +9

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-h…