Dynamic Graph | BestHub

DataFunTalk

May 27, 2023 · Artificial Intelligence

Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights

The article presents a comprehensive overview of how graph algorithms are applied in Alibaba's e‑commerce risk control system, detailing six sections that include risk scenario introductions, interaction and product content risk methods, dynamic heterogeneous graph practices, a large‑scale competition, and future research directions.

Dynamic GraphGraph Neural Networkse-commerce risk

0 likes · 18 min read

Graph Algorithms in Alibaba E‑commerce Risk Control: Practices and Insights

DataFunSummit

Apr 11, 2023 · Artificial Intelligence

OneFlow Coop: Joint Optimization of Dynamic‑Graph Recomputation and Memory Allocation

This article introduces OneFlow Coop, a memory‑optimization technique that jointly optimizes dynamic‑graph recomputation strategies and GPU memory allocation by analyzing existing DTR limitations, proposing recomputable in‑place, op‑guided tensor allocation, and layout‑aware eviction modules, and demonstrating superior experimental results.

Deep LearningDynamic GraphGPU memory

0 likes · 18 min read

OneFlow Coop: Joint Optimization of Dynamic‑Graph Recomputation and Memory Allocation

Baidu Geek Talk

Apr 8, 2022 · Artificial Intelligence

Golang Object Pool for Reducing GC Pressure, FFmpeg Concurrency Control, and Paddle Static vs. Dynamic Graphs

The article explains how Go's lock‑free sync.Pool can cut garbage‑collection overhead, shows practical FFmpeg thread‑parameter tuning that balances CPU use and latency for video filtering versus encoding, and compares PaddlePaddle's static and dynamic graph modes, including debugging tips and conversion to static.

ConcurrencyDeep LearningDynamic Graph

0 likes · 13 min read

Golang Object Pool for Reducing GC Pressure, FFmpeg Concurrency Control, and Paddle Static vs. Dynamic Graphs

DataFunSummit

Sep 6, 2021 · Artificial Intelligence

Graph Neural Network‑Based Payment Fraud Detection at eBay

This article explains how eBay uses graph neural networks and a heterogeneous‑graph fraud detection framework (xFraud) to improve payment risk assessment, overcome the limitations of traditional machine‑learning models, and effectively identify both individual and organized fraud in a large‑scale e‑commerce environment.

Dynamic GraphGraph Neural NetworkseBay

0 likes · 15 min read

Graph Neural Network‑Based Payment Fraud Detection at eBay

Python Programming Learning Circle

Jul 22, 2021 · Artificial Intelligence

Understanding PyTorch’s Dynamic Autograd System: Variable, Function, and Engine

This article explains how PyTorch implements its dynamic autograd graph using the core C++ classes Variable, Function, and Engine, detailing their initialization, inheritance hierarchy, code structures, and the execution flow of backward propagation.

CDeep LearningDynamic Graph

0 likes · 13 min read

Understanding PyTorch’s Dynamic Autograd System: Variable, Function, and Engine

DataFunTalk

Mar 15, 2019 · Artificial Intelligence

Designing Personalized, Dynamic, and Multimodal Knowledge Graphs for Chatbots

The article explores how chatbots require personalized dense knowledge graphs, dynamic temporal graphs, subjective emotion modeling, integration with external services, and multimodal media support, while also promoting a new NLP book and a related giveaway for readers.

ChatbotDynamic GraphKnowledge Graph

0 likes · 9 min read

Designing Personalized, Dynamic, and Multimodal Knowledge Graphs for Chatbots