no code implementations • 21 Feb 2024 • Jianqiang Shen, Yuchin Juan, Shaobo Zhang, Ping Liu, Wen Pu, Sriram Vasudevan, Qingquan Song, Fedor Borisyuk, Kay Qianqi Shen, Haichao Wei, Yunxiang Ren, Yeou S. Chiou, Sicong Kuang, Yuan Yin, Ben Zheng, Muchen Wu, Shaghayegh Gharghabi, Xiaoqing Wang, Huichao Xue, Qi Guo, Daniel Hewlett, Luke Simon, Liangjie Hong, Wenjing Zhang
Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking.
no code implementations • 10 Feb 2024 • Fedor Borisyuk, Mingzhou Zhou, Qingquan Song, Siyu Zhu, Birjodh Tiwana, Ganesh Parameswaran, Siddharth Dangi, Lars Hertel, Qiang Xiao, Xiaochen Hou, Yunbo Ouyang, Aman Gupta, Sheallika Singh, Dan Liu, Hailing Cheng, Lei Le, Jonathan Hung, Sathiya Keerthi, Ruoyan Wang, Fengyu Zhang, Mohit Kothari, Chen Zhu, Daqi Sun, Yun Dai, Xun Luan, Sirou Zhu, Zhiwei Wang, Neil Daftary, Qianqi Shen, Chengming Jiang, Haichao Wei, Maneesh Varshney, Amol Ghoting, Souvik Ghosh
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods.
no code implementations • 8 Jan 2024 • Zirui Liu, Qingquan Song, Qiang Charles Xiao, Sathiya Keerthi Selvaraj, Rahul Mazumder, Aman Gupta, Xia Hu
This usually results in a trade-off between model accuracy and efficiency.
no code implementations • 5 Sep 2023 • Kayhan Behdin, Ayan Acharya, Aman Gupta, Qingquan Song, Siyu Zhu, Sathiya Keerthi, Rahul Mazumder
Particularly noteworthy is our outlier-aware algorithm's capability to achieve near or sub-3-bit quantization of LLMs with an acceptable drop in accuracy, obviating the need for non-uniform quantization or grouping techniques, improving upon methods such as SpQR by up to two times in terms of perplexity.
no code implementations • 19 Feb 2023 • Kayhan Behdin, Qingquan Song, Aman Gupta, Sathiya Keerthi, Ayan Acharya, Borja Ocejo, Gregory Dexter, Rajiv Khanna, David Durfee, Rahul Mazumder
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance.
no code implementations • 7 Dec 2022 • Kayhan Behdin, Qingquan Song, Aman Gupta, David Durfee, Ayan Acharya, Sathiya Keerthi, Rahul Mazumder
To that end, this paper presents a thorough empirical evaluation of mSAM on various tasks and datasets.
no code implementations • 13 Feb 2022 • Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Qingquan Song, Jundong Li, Xia Hu
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification.
no code implementations • 1 Jan 2021 • Yi-Wei Chen, Qingquan Song, Xia Hu
Differentiable NAS with supernets that encompass all potential architectures in a large graph cuts down search overhead to few GPU days or less.
no code implementations • NeurIPS 2020 • Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu
Motivated by the observation, in this paper, we propose to investigate the interaction detection problem from a novel topological perspective by analyzing the connectivity in neural networks.
no code implementations • 25 Oct 2020 • Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting Hsiang Wang, Ying Shan, Xia Hu
Detecting statistical interactions between input features is a crucial and challenging task.
no code implementations • 29 Jun 2020 • Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang, Yuandong Tian, Xia Hu
Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers.
1 code implementation • 26 Jun 2020 • Ting-Hsiang Wang, Qingquan Song, Xiaotian Han, Zirui Liu, Haifeng Jin, Xia Hu
To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models.
no code implementations • 17 Dec 2019 • Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, Xia Hu
To further improve the graph representation learning ability, hierarchical GNN has been explored.
1 code implementation • 1 Oct 2019 • Yijun Bian, Qingquan Song, Mengnan Du, Jun Yao, Huanhuan Chen, Xia Hu
Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.
no code implementations • 7 Sep 2019 • Kaixiong Zhou, Qingquan Song, Xiao Huang, Xia Hu
First, the search space of GNN is different from the ones in existing NAS work.
Ranked #38 on Node Classification on Cora
no code implementations • 21 Jul 2019 • Yi-Wei Chen, Qingquan Song, Xia Hu
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem.
1 code implementation • 11 Jun 2019 • Qingquan Song, Shiyu Chang, Xia Hu
To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem.
no code implementations • 2 Jan 2019 • Qingquan Song, Haifeng Jin, Xiao Huang, Xia Hu
Experiments on real-world multi-label image classification and ranking problems demonstrate the effectiveness of our proposed frameworks and provide insights of the vulnerability of multi-label deep learning models under diverse targeted attacking strategies.
no code implementations • 2018 IEEE International Conference on Big Knowledge (ICBK) 2018 • Haifeng Jin, Qingquan Song, Xia Hu
Moreover, the learned vector representations are not in a smooth space since the values can only be integers.
Ranked #12 on Graph Classification on PTC
13 code implementations • 27 Jun 2018 • Haifeng Jin, Qingquan Song, Xia Hu
In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.
no code implementations • 19 Mar 2018 • Mengnan Du, Ninghao Liu, Qingquan Song, Xia Hu
While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics.
no code implementations • 28 Nov 2017 • Qingquan Song, Hancheng Ge, James Caverlee, Xia Hu
Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors.