Search Results for author: Dhruv Choudhary

Found 13 papers, 2 papers with code

Harmless Transfer Learning for Item Embeddings

no code implementations Findings (NAACL) 2022 Chengyue Gong, Xiaocong Du, Dhruv Choudhary, Bhargav Bhushanam, Qiang Liu, Arun Kejariwal

On the definition side, we reduce the bias in transfer loss by focusing on the items to which information from high-frequency items can be efficiently transferred.

Recommendation Systems Transfer Learning

FlexShard: Flexible Sharding for Industry-Scale Sequence Recommendation Models

no code implementations8 Jan 2023 Geet Sethi, Pallab Bhattacharya, Dhruv Choudhary, Carole-Jean Wu, Christos Kozyrakis

Sequence-based deep learning recommendation models (DLRMs) are an emerging class of DLRMs showing great improvements over their prior sum-pooling based counterparts at capturing users' long term interests.

RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure

no code implementations9 Nov 2022 Mark Zhao, Dhruv Choudhary, Devashish Tyagi, Ajay Somani, Max Kaplan, Sung-Han Lin, Sarunya Pumma, Jongsoo Park, Aarti Basant, Niket Agarwal, Carole-Jean Wu, Christos Kozyrakis

RecD addresses immense storage, preprocessing, and training overheads caused by feature duplication inherent in industry-scale DLRM training datasets.

Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems

no code implementations2 Sep 2022 Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu

One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and hence domain generalization error.

Domain Generalization Recommendation Systems

AutoShard: Automated Embedding Table Sharding for Recommender Systems

1 code implementation12 Aug 2022 Daochen Zha, Louis Feng, Bhargav Bhushanam, Dhruv Choudhary, Jade Nie, Yuandong Tian, Jay Chae, Yinbin Ma, Arun Kejariwal, Xia Hu

This is a significant design challenge of distributed systems named embedding table sharding, i. e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard.

Recommendation Systems

Positive Unlabeled Contrastive Learning

no code implementations1 Jun 2022 Anish Acharya, Sujay Sanghavi, Li Jing, Bhargav Bhushanam, Dhruv Choudhary, Michael Rabbat, Inderjit Dhillon

We extend this paradigm to the classical positive unlabeled (PU) setting, where the task is to learn a binary classifier given only a few labeled positive samples, and (often) a large amount of unlabeled samples (which could be positive or negative).

Contrastive Learning Pseudo Label

Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits

no code implementations ICLR 2022 Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao, Guanghui Lan

We show that incorporating frequency information of tokens in the embedding learning problems leads to provably efficient algorithms, and demonstrate that common adaptive algorithms implicitly exploit the frequency information to a large extent.

Language Modelling Recommendation Systems

Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale

no code implementations26 May 2021 Zhaoxia, Deng, Jongsoo Park, Ping Tak Peter Tang, Haixin Liu, Jie, Yang, Hector Yuen, Jianyu Huang, Daya Khudia, Xiaohan Wei, Ellie Wen, Dhruv Choudhary, Raghuraman Krishnamoorthi, Carole-Jean Wu, Satish Nadathur, Changkyu Kim, Maxim Naumov, Sam Naghshineh, Mikhail Smelyanskiy

We share in this paper our search strategies to adapt reference recommendation models to low-precision hardware, our optimization of low-precision compute kernels, and the design and development of tool chain so as to maintain our models' accuracy throughout their lifespan during which topic trends and users' interests inevitably evolve.

Recommendation Systems

Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems

no code implementations4 May 2021 Xiaocong Du, Bhargav Bhushanam, Jiecao Yu, Dhruv Choudhary, Tianxiang Gao, Sherman Wong, Louis Feng, Jongsoo Park, Yu Cao, Arun Kejariwal

Our method leverages structured sparsification to reduce computational cost without hurting the model capacity at the end of offline training so that a full-size model is available in the recurring training stage to learn new data in real-time.

Recommendation Systems

Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data

no code implementations16 Oct 2020 Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, Zeliang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal

To the best of our knowledge, this is the first work to provide in-depth analysis and discussion of applying pruning to online recommendation systems with non-stationary data distribution.

Recommendation Systems

On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data

no code implementations12 Oct 2017 Dhruv Choudhary, Arun Kejariwal, Francois Orsini

Further, the lack of characterization of performance -- both with respect to real-timeliness and accuracy -- on production data sets makes model selection very challenging.

Anomaly Detection Astronomy +3

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