Dong Chen
where else to find them
no profile links on file
what they talk about
all 8 in the filters
where they keep going back
- Google I/O2 editions2019–2022
all 4 in the filters
on a stage with
- Ali Cevikonce
- Ao Wangonce
- Changqing Yanonce
all 28 in the filters
who talks about the same things
- Caren Chang14 talks
- Dan Sandler10 talks
- Jingyu Shi8 talks
- Tor Norbye27 talks
- Jamal Eason12 talks
- Thomas Ezan9 talks
| title | conference | date |
|---|---|---|
| CG-Bench: Can Language Models Assist Call Graph Construction in the Real World? | LMPL 2025 | 2025-10-12 |
Benchmark contains 104 call sites drawn from seven large C/C++ projects (GCC, FFMPG, radius, ZFS, cure, open SSH, WRK) and isolates hard cases like dynamic… | ||
| Torpor: GPU-Enabled Serverless Computing for Low-Latency, Resource-Efficient... | USENIX ATC '25 | 2025-07-07 |
Torpor implements late binding with model swapping to keep models in host memory and move them to a shared GPU pool on demand, using asynchronous API… | ||
| AnchorNet: Bridging Live and Collaborative Streaming with a Unified Architecture | USENIX ATC '25 | 2025-07-07 |
AnchorNet implements a unified stream path with the RTC SFU as the single CDN publisher and server-side mixing, solving mode switches between single-host and… | ||
| What's new in Android machine learning | Google I/O 2022 | 2022-05-11 |
Android adds concrete on-device ML advances: TensorFlow Lite in Google Play services (BETA) reduces app binary size by ~2 MB and teams see up to 20% CPU wins… | ||
| Build production-ready apps with Extensions, Functions, and Google Cloud | Google I/O 2022 | 2022-05-11 |
Cloud Functions Gen 2 reframes functions on Cloud Run/Eventarc and claims up to a 50% latency/cost win in a three-request example (600ms → 300ms) by using… | ||
| What’s new in Android machine learning | Google I/O 2019 | 2019-05-07 |
Android announces concrete on-device ML pieces: Android runs on 2.5 billion devices and exposes the Neural Networks API for GPU/DSP/NPU acceleration; measured… | ||
| Getting the best of on-device ML with ML Kit | Android Dev Summit 2018 | 2018-11-07 |
Covers practical ML Kit details developers need to ship: which APIs are on-device vs cloud (on-device image labeling ~400 labels, cloud ~10,000), 18× faster… | ||
| nothing matches | ||