Search Results for author: Carole-Jean Wu

Found 27 papers, 10 papers with code

Infinite Recommendation Networks: A Data-Centric Approach

2 code implementations3 Jun 2022 Noveen Sachdeva, Mehak Preet Dhaliwal, Carole-Jean Wu, Julian McAuley

We leverage the Neural Tangent Kernel and its equivalence to training infinitely-wide neural networks to devise $\infty$-AE: an autoencoder with infinitely-wide bottleneck layers.

 Ranked #1 on Recommendation Systems on Douban (AUC metric)

Information Retrieval Recommendation Systems

Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity

no code implementations30 May 2022 Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole-Jean Wu

Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device.

Fairness Federated Learning +1

RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendation

no code implementations25 Jan 2022 Geet Sethi, Bilge Acun, Niket Agarwal, Christos Kozyrakis, Caroline Trippel, Carole-Jean Wu

EMBs exhibit distinct memory characteristics, providing performance optimization opportunities for intelligent EMB partitioning and placement across a tiered memory hierarchy.

On Sampling Collaborative Filtering Datasets

1 code implementation13 Jan 2022 Noveen Sachdeva, Carole-Jean Wu, Julian McAuley

We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms.

Collaborative Filtering Recommendation Systems

Papaya: Practical, Private, and Scalable Federated Learning

no code implementations8 Nov 2021 Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, Mani Malek

Our work tackles the aforementioned issues, sketches of some of the system design challenges and their solutions, and touches upon principles that emerged from building a production FL system for millions of clients.

Federated Learning

Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training

no code implementations20 Aug 2021 Mark Zhao, Niket Agarwal, Aarti Basant, Bugra Gedik, Satadru Pan, Mustafa Ozdal, Rakesh Komuravelli, Jerry Pan, Tianshu Bao, Haowei Lu, Sundaram Narayanan, Jack Langman, Kevin Wilfong, Harsha Rastogi, Carole-Jean Wu, Christos Kozyrakis, Parik Pol

Innovations that improve the efficiency and performance of DSI systems and hardware are urgent, demanding a deep understanding of DSI characteristics and infrastructure at scale.

AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning

no code implementations16 Jul 2021 Young Geun Kim, Carole-Jean Wu

Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training samples on device.

Federated Learning

SVP-CF: Selection via Proxy for Collaborative Filtering Data

no code implementations11 Jul 2021 Noveen Sachdeva, Carole-Jean Wu, Julian McAuley

As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance -- masking performance deficiencies in algorithms or altering the relative performance of algorithms, as compared to models trained on the complete dataset.

Collaborative Filtering 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

RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference

no code implementations29 Jan 2021 Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, Gu-Yeon Wei

Neural personalized recommendation models are used across a wide variety of datacenter applications including search, social media, and entertainment.

TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models

1 code implementation25 Jan 2021 Chunxing Yin, Bilge Acun, Xing Liu, Carole-Jean Wu

TT-Rec achieves 117 times and 112 times model size compression, for Kaggle and Terabyte, respectively.

Understanding Training Efficiency of Deep Learning Recommendation Models at Scale

no code implementations11 Nov 2020 Bilge Acun, Matthew Murphy, Xiaodong Wang, Jade Nie, Carole-Jean Wu, Kim Hazelwood

The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models.

CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery

no code implementations5 Nov 2020 Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu

The paper is the first to the extent of our knowledge to perform a data-driven, in-depth analysis of applying partial recovery to recommendation models and identified a trade-off between accuracy and performance.

Understanding Capacity-Driven Scale-Out Neural Recommendation Inference

no code implementations4 Nov 2020 Michael Lui, Yavuz Yetim, Özgür Özkan, Zhuoran Zhao, Shin-Yeh Tsai, Carole-Jean Wu, Mark Hempstead

One approach to support this scale is with distributed serving, or distributed inference, which divides the memory requirements of a single large model across multiple servers.

Recommendation Systems

AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance

no code implementations6 May 2020 Young Geun Kim, Carole-Jean Wu

Such execution scaling decision becomes more complicated with the stochastic nature of mobile-cloud execution, where signal strength variations of the wireless networks and resource interference can significantly affect real-time inference performance and system energy efficiency.

GEVO: GPU Code Optimization using Evolutionary Computation

1 code implementation17 Apr 2020 Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu

If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline version by an average of 51. 08%.

Handwriting Recognition Image Classification

Developing a Recommendation Benchmark for MLPerf Training and Inference

no code implementations16 Mar 2020 Carole-Jean Wu, Robin Burke, Ed H. Chi, Joseph Konstan, Julian McAuley, Yves Raimond, Hao Zhang

Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience.

Image Classification object-detection +3

DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference

no code implementations8 Jan 2020 Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu

Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure.

Distributed, Parallel, and Cluster Computing

Exploiting Parallelism Opportunities with Deep Learning Frameworks

1 code implementation13 Aug 2019 Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim Hazelwood, David Brooks

State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers.

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