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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1109/tbdata.2015.2472014openalex-snapshot

Petuum: A New Platform for Distributed Machine Learning on Big Data

inferredcites →https://doi.org/10.1145/2505515.2505665openalex-snapshot

Learning deep structured semantic models for web search using clickthrough data

inferredcites →https://doi.org/10.1145/2647868.2654889openalex-snapshot

Caffe

inferredcites →https://doi.org/10.1145/2959100.2959190openalex-snapshot

Deep Neural Networks for YouTube Recommendations

inferredcites →https://doi.org/10.1145/2988450.2988454openalex-snapshot

Wide & Deep Learning for Recommender Systems

inferredcites →https://doi.org/10.1145/3097983.3098029openalex-snapshot

KunPeng

inferredcites →https://doi.org/10.1145/3219819.3219823openalex-snapshot

Deep Interest Network for Click-Through Rate Prediction

inferredcites →https://doi.org/10.1145/3219819.3219826openalex-snapshot

Learning Tree-based Deep Model for Recommender Systems

inferredcites →https://doi.org/10.1609/aaai.v33i01.33015941openalex-snapshot

Deep Interest Evolution Network for Click-Through Rate Prediction

inferredcites →https://doi.org/10.24963/ijcai.2017/239openalex-snapshot

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

inferredcites →https://doi.org/10.48550/arxiv.1408.5093openalex-snapshot

Caffe: Convolutional Architecture for Fast Feature Embedding

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1512.01274openalex-snapshot

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

inferredcites →https://doi.org/10.48550/arxiv.1512.02595openalex-snapshot

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

inferredcites →https://doi.org/10.48550/arxiv.1609.04836openalex-snapshot

On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

inferredcites →https://doi.org/10.48550/arxiv.1609.08144openalex-snapshot

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

inferredcites →https://doi.org/10.48550/arxiv.1705.08741openalex-snapshot

Train longer, generalize better: closing the generalization gap in large batch training of neural networks

inferredcites →https://doi.org/10.48550/arxiv.1706.02677openalex-snapshot

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

inferredcites →https://doi.org/10.48550/arxiv.1706.04972openalex-snapshot

Device Placement Optimization with Reinforcement Learning

inferredcites →https://doi.org/10.48550/arxiv.1707.07435openalex-snapshot

Deep Learning based Recommender System: A Survey and New Perspectives

inferredcites →https://doi.org/10.48550/arxiv.1712.01887openalex-snapshot

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

inferredcites →https://doi.org/10.48550/arxiv.1804.07931openalex-snapshot

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

inferredcites →https://doi.org/10.48550/arxiv.1807.05358openalex-snapshot

Beyond Data and Model Parallelism for Deep Neural Networks

inferredcites →https://doi.org/10.5555/2388996.2389086openalex-snapshot

Legion: expressing locality and independence with logical regions

inferredcites →https://doi.org/10.5555/3026877.3026899openalex-snapshot

TensorFlow: A system for large-scale machine learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4673688openalex-extracted

A Novel Semi-Supervised Restricted Boltzmann Machine from the Perspective of Low-Dimensional Representation Distribution for Microstructure Image Clustering

inferred← citeshttps://doi.org/10.1016/j.compind.2022.103668openalex-snapshot

Combining deep learning with knowledge graph for macro process planning

inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121484openalex-snapshot

Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods

inferred← citeshttps://doi.org/10.1016/j.jmsy.2024.11.006openalex-snapshot

Ontology and production rules-based dynamic knowledge base construction methodology for machining process

inferred← citeshttps://doi.org/10.1021/acsomega.4c02393openalex-snapshot

Advancing Ionic Liquid Research with pSCNN: A Novel Approach for Accurate Normal Melting Temperature Predictions

inferred← citeshttps://doi.org/10.1109/hpca68181.2026.11408549openalex-snapshot

Near-Zero-Overhead Freshness for Recommendation Systems via Inference-Side Model Updates

inferred← citeshttps://doi.org/10.1109/icde53745.2022.00324openalex-snapshot

PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems

inferred← citeshttps://doi.org/10.1109/icde65448.2025.00308openalex-snapshot

MLKV: Efficiently Scaling up Large Embedding Model Training with Disk-based Key-Value Storage

inferred← citeshttps://doi.org/10.1109/icnp52444.2021.9651946openalex-snapshot

NetFC: Enabling Accurate Floating-point Arithmetic on Programmable Switches

inferred← citeshttps://doi.org/10.1109/icnp61940.2024.10858501openalex-snapshot

Zebra: Accelerating Distributed Sparse Deep Training With in-Network Gradient Aggregation for Hot Parameters

inferred← citeshttps://doi.org/10.1109/ipdps49936.2021.00069openalex-snapshot

BiPS: Hotness-aware Bi-tier Parameter Synchronization for Recommendation Models

inferred← citeshttps://doi.org/10.1109/isca59077.2024.00033openalex-snapshot

PreSto: An In-Storage Data Preprocessing System for Training Recommendation Models

inferred← citeshttps://doi.org/10.1109/isca59077.2024.00081openalex-snapshot

Heterogeneous Acceleration Pipeline for Recommendation System Training

inferred← citeshttps://doi.org/10.1109/lca.2023.3336841openalex-snapshot

FPGA-Accelerated Data Preprocessing for Personalized Recommendation Systems

inferred← citeshttps://doi.org/10.1109/rmkmate59243.2023.10369815openalex-snapshot

Revenue Forecasting Using Distributed Machine Learning In Online Advertising Systems

inferred← citeshttps://doi.org/10.1109/sc41404.2022.00075openalex-snapshot

EL-Rec: Efficient Large-Scale Recommendation Model Training via Tensor-Train Embedding Table

inferred← citeshttps://doi.org/10.1109/sc41405.2020.00025openalex-snapshot

Kraken: Memory-Efficient Continual Learning for Large-Scale Real-Time Recommendations

inferred← citeshttps://doi.org/10.1109/tpds.2022.3208425openalex-snapshot

Enabling In-Network Floating-Point Arithmetic for Efficient Computation Offloading

inferred← citeshttps://doi.org/10.1145/3404835.3462976openalex-snapshot

ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table

inferred← citeshttps://doi.org/10.1145/3419394.3423637openalex-snapshot

Dissecting the Communication Latency in Distributed Deep Sparse Learning

inferred← citeshttps://doi.org/10.1145/3459637.3481941openalex-snapshot

One Model to Serve All

inferred← citeshttps://doi.org/10.1145/3460231.3474246openalex-snapshot

cDLRM: Look Ahead Caching for Scalable Training of Recommendation Models

inferred← citeshttps://doi.org/10.1145/3470496.3533727openalex-snapshot

Software-hardware co-design for fast and scalable training of deep learning recommendation models

inferred← citeshttps://doi.org/10.1145/3503222.3507777openalex-snapshot

RecShard: statistical feature-based memory optimization for industry-scale neural recommendation

inferred← citeshttps://doi.org/10.1145/3511808.3557479openalex-snapshot

Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models

inferred← citeshttps://doi.org/10.1145/3534678.3539034openalex-snapshot

AutoShard: Automated Embedding Table Sharding for Recommender Systems

inferred← citeshttps://doi.org/10.1145/3534678.3539070openalex-snapshot

Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters

inferred← citeshttps://doi.org/10.1145/3542637.3542645openalex-snapshot

Herald: An Embedding Scheduler for Distributed Embedding Model Training

inferred← citeshttps://doi.org/10.1145/3580305.3599788openalex-snapshot

Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach

inferred← citeshttps://doi.org/10.1145/3589310openalex-snapshot

FEC: Efficient Deep Recommendation Model Training with Flexible Embedding Communication

inferred← citeshttps://doi.org/10.1145/3623278.3624761openalex-snapshot

RECom: A Compiler Approach to Accelerating Recommendation Model Inference with Massive Embedding Columns

inferred← citeshttps://doi.org/10.1145/3627673.3680068openalex-snapshot

Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights

inferred← citeshttps://doi.org/10.1145/3627673.3680091openalex-snapshot

LiNR: Model Based Neural Retrieval on GPUs at LinkedIn

inferred← citeshttps://doi.org/10.1145/3639306openalex-snapshot

CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models

inferred← citeshttps://doi.org/10.1145/3695053.3731105openalex-snapshot

TRACI: Network Acceleration of Input-Dynamic Communication for Large-Scale Deep Learning Recommendation Model

inferred← citeshttps://doi.org/10.1145/3711896.3737241openalex-snapshot

Lightweight Auto-bidding based on Traffic Prediction in Live Advertising

inferred← citeshttps://doi.org/10.1145/3713072openalex-snapshot

CAFE+: Towards Compact, Adaptive, and Fast Embedding for Large-scale Online Recommendation Models

inferred← citeshttps://doi.org/10.1145/3770854.3783925openalex-snapshot

MTGenRec: An Efficient Distributed Training System for Generative Recommendation Models in Meituan

inferred← citeshttps://doi.org/10.1155/int/2389650openalex-snapshot

Dynamic Time‐Varying W‐Shaped and M‐Shaped Transfer Function Based Feature Selection Method for Large‐Scale Sparse Data

inferred← citeshttps://doi.org/10.14288/1.0402605openalex-snapshot

Accelerating input dispatching for deep learning recommendation models training

inferred← citeshttps://doi.org/10.14288/1.0402608openalex-snapshot

Accelerating recommendation system training by leveraging popular choices

inferred← citeshttps://doi.org/10.14778/3485450.3485462openalex-snapshot

Accelerating recommendation system training by leveraging popular choices

inferred← citeshttps://doi.org/10.14778/3611479.3611514openalex-snapshot

Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding

inferred← citeshttps://doi.org/10.14778/3611540.3611546openalex-snapshot

EmbedX: A Versatile, Efficient and Scalable Platform to Embed Both Graphs and High-Dimensional Sparse Data

inferred← citeshttps://doi.org/10.23919/date64628.2025.10992746openalex-snapshot

Slipstream: Semantic-Based Training Acceleration for Recommendation Models

inferred← citeshttps://doi.org/10.48550/arxiv.2003.09518openalex-snapshot

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems

inferred← citeshttps://doi.org/10.48550/arxiv.2011.11983openalex-snapshot

WeiPS: a symmetric fusion model framework for large-scale online learning

inferred← citeshttps://doi.org/10.48550/arxiv.2101.11427openalex-snapshot

One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction

inferred← citeshttps://doi.org/10.48550/arxiv.2104.01981openalex-snapshot

ECRM: Efficient Fault Tolerance for Recommendation Model Training via Erasure Coding

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