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20212026
most citedUnderstanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training

77 citations · 77 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching

Onkar Susladkar, Tushar Prakash, Gayatri Deshmukh +8

We propose UniDFlow, a unified discrete flow-matching framework for multimodal understanding, generation, and editing. It decouples understanding and generation via task-specific l…

cs.AI2025

Fluidity Index: Next-Generation Super-intelligence Benchmarks

Eric Ngoiya, Tianshu Bao

This paper introduces the Fluidity Index (FI) to quantify model adaptability in dynamic, scaling environments. The benchmark evaluates response accuracy based on deviations in init…

eess.SY2024

A New Hybrid Automaton Framework with Partial Differential Equation Dynamics

Tianshu Bao, Hengrong Du, Weiming Xiang +1

This paper presents the syntax and semantics of a novel type of hybrid automaton (HA) with partial differential equation (PDE) dynamic, partial differential hybrid automata (PDHA).…

physics.flu-dyn2023

Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement

Shengyu Chen, Tianshu Bao, Peyman Givi +2

Simulating turbulence is critical for many societally important applications in aerospace engineering, environmental science, the energy industry, and biomedicine. Large eddy simul…

cs.DC2021★ 77 cited

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

Mark Zhao, Niket Agarwal, Aarti Basant +14

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators (DSA) are used to train increasingly-complex deep learning models. These clusters rely…