{"total":17,"rows":[{"id":"2411.04996","title":"Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models","created":"2024-11-07","cited_by_count":163,"area":null},{"id":"2403.02545","title":"Wukong: Towards a Scaling Law for Large-Scale Recommendation","created":"2024-03-04","cited_by_count":74,"area":null},{"id":"2111.04867","title":"TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches","created":"2021-11-08","cited_by_count":38,"area":null},{"id":"2210.08803","title":"Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference","created":"2022-10-17","cited_by_count":20,"area":null},{"id":"2508.15998","title":"Quantum Federated Learning: A Comprehensive Survey","created":"2025-08-21","cited_by_count":10,"area":null},{"id":"2210.08804","title":"A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models","created":"2022-10-17","cited_by_count":9,"area":null},{"id":"2206.10452","title":"Shifted Compression Framework: Generalizations and Improvements","created":"2022-06-21","cited_by_count":6,"area":null},{"id":"2403.00877","title":"Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation","created":"2024-03-01","cited_by_count":6,"area":null},{"id":"2201.05500","title":"Communication-Efficient TeraByte-Scale Model Training Framework for Online Advertising","created":"2022-01-05","cited_by_count":5,"area":null},{"id":"2402.07529","title":"Accelerating Distributed Deep Learning using Lossless Homomorphic Compression","created":"2024-02-12","cited_by_count":4,"area":null},{"id":"2103.15195","title":"MergeComp: A Compression Scheduler for Scalable Communication-Efficient Distributed Training","created":"2021-03-28","cited_by_count":1,"area":null},{"id":"2205.14465","title":"ByteComp: Revisiting Gradient Compression in Distributed Training","created":"2022-05-28","cited_by_count":1,"area":null},{"id":"2211.09723","title":"Fair and Efficient Distributed Edge Learning with Hybrid Multipath TCP","created":"2022-11-03","cited_by_count":1,"area":null},{"id":"2404.08847","title":"LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models","created":"2024-04-12","cited_by_count":1,"area":null},{"id":"2605.10886","title":"LoKA: Low-precision Kernel Applications for Recommendation Models At Scale","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2105.14088","title":"Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering","created":"2021-05-28","cited_by_count":0,"area":null},{"id":"2604.24073","title":"FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost","created":"2026-04-27","cited_by_count":0,"area":null}],"next_cursor":null}