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Machine Learning Summit: Multi-Modal Based Frame Rate Prediction

Ruidong Feng  (Senior Data Mining Engineer, Booming Tech & Netease)

Location: Room 2001, West Hall

Date: Tuesday, March 22

Time: 3:00 pm - 3:30 pm

Pass Type: All Access Pass, Summits Pass - Get your pass now!

Topic: Programming

Format: Session

Viewing Experience: In-Person

Vault Recording: Video

Audience Level: No

Frame rate is a key indicator which measure the fluency of a game. Assuming that we can predict frame rate by a model, we can take measures (such as reducing the rendering quality in advance) to ensure the smooth running of a game when the frame rate is predicted to drop. This lecture will take "Conqueror's Blade", a recently released multiplayer action competitive game, as the experimental platform. Here, we will introduce how Booming Tech collects and stores relative data at the scene of game latency and how to implement a frame rate prediction model to mine those key features that affect frame rate. In this experiment, the data we collected are all general game data. In other words, you can also collect similar data in your own game and easily reproduce our model, thereby helping you iterate the strategy of game optimization.

Takeaway

This lecture will take "Conqueror's Blade", a recently released multiplayer action competitive game, as the experimental platform. Here, we will introduce how Booming Tech collects and stores relative data at the scene of game latency and how to implement a frame rate prediction model to mine those key features that affect frame rate. In this experiment, the data we collected are all general game data. In other words, you can also collect similar data in your own game and easily reproduce our model, thereby helping you iterate the strategy of game optimization.

Intended Audience

Data mining engineer, Game developer, Game Programmer, Game data analyst, anyone who is interested in machine learning