6 citations · 10 across the 9 of their papers we have counts for
12 papers
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
Junhan Yang, Zheng Liu, Bowen Jin +7
Transformer encoding networks have been proved to be a powerful tool of understanding natural languages. They are playing a critical role in native ads service, which facilitates t…
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
Jianxun Lian, Iyad Batal, Zheng Liu +4
Precise user modeling is critical for online personalized recommendation services. Generally, users' interests are diverse and are not limited to a single aspect, which is particul…
Multi-Channel Sequential Behavior Networks for User Modeling in Online Advertising
Iyad Batal, Akshay Soni
Multiple content providers rely on native advertisement for revenue by placing ads within the organic content of their pages. We refer to this setting as ``queryless'' to different…
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent
Bowen Weng, Huaqing Xiong, Yingbin Liang +1
Existing convergence analyses of Q-learning mostly focus on the vanilla stochastic gradient descent (SGD) type of updates. Despite the Adaptive Moment Estimation (Adam) has been co…
Towards Automated Single Channel Source Separation using Neural Networks
Arpita Gang, Pravesh Biyani, Akshay Soni
Many applications of single channel source separation (SCSS) including automatic speech recognition (ASR), hearing aids etc. require an estimation of only one source from a mixture…
On Learning Sparsely Used Dictionaries from Incomplete Samples
Thanh V. Nguyen, Akshay Soni, Chinmay Hegde
Most existing algorithms for dictionary learning assume that all entries of the (high-dimensional) input data are fully observed. However, in several practical applications (such a…