Zobrazeno 1 - 10
of 187
pro vyhledávání: '"Kong, Deguang"'
This paper presents a novel teachable conversation interaction system that is capable of learning users preferences from cold start by gradually adapting to personal preferences. In particular, the TAI system is able to automatically identify and lab
Externí odkaz:
http://arxiv.org/abs/2309.05127
Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference. To bridge this gap, this paper presents a neural learning framework to per
Externí odkaz:
http://arxiv.org/abs/2309.05113
Autor:
Ribeiro, Danilo, Wang, Shen, Ma, Xiaofei, Zhu, Henry, Dong, Rui, Kong, Deguang, Burger, Juliette, Ramos, Anjelica, Wang, William, Huang, Zhiheng, Karypis, George, Xiang, Bing, Roth, Dan
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect models to not only answer questions, but also produce step-by-step struc
Externí odkaz:
http://arxiv.org/abs/2302.06729
Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions. In practice, there exist noise and outliers in clustering task, which, however, may significantly degrade th
Externí odkaz:
http://arxiv.org/abs/2301.00717
In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint. This paper presents a bid recommendation scenario that discovers the concavity cha
Externí odkaz:
http://arxiv.org/abs/2212.13923
In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions. Moreover, the bidding on advertising inventory has few connections with propensity
Externí odkaz:
http://arxiv.org/abs/2212.13915
Autor:
Hu, Xiyang, Chen, Xinchi, Qi, Peng, Kong, Deguang, Liu, Kunlun, Wang, William Yang, Huang, Zhiheng
Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages. We present an effective method to train multilingual IR systems when only English IR training data and some parallel corpora b
Externí odkaz:
http://arxiv.org/abs/2210.06633
Click-through rate (CTR) prediction is a crucial task in online display advertising. The embedding-based neural networks have been proposed to learn both explicit feature interactions through a shallow component and deep feature interactions using a
Externí odkaz:
http://arxiv.org/abs/2002.06987
Science Driven Innovations Powering Mobile Product: Cloud AI vs. Device AI Solutions on Smart Device
Autor:
Kong, Deguang
Recent years have witnessed the increasing popularity of mobile devices (such as iphone) due to the convenience that it brings to human lives. On one hand, rich user profiling and behavior data (including per-app level, app-interaction level and syst
Externí odkaz:
http://arxiv.org/abs/1711.07580
Deploying deep neural networks on mobile devices is a challenging task. Current model compression methods such as matrix decomposition effectively reduce the deployed model size, but still cannot satisfy real-time processing requirement. This paper f
Externí odkaz:
http://arxiv.org/abs/1708.04728