Daochen Zha

MLE @Airbnb | CS Ph.D. @RiceUniversity | Former intern @Meta | AI | ML | Reinforcement learning

[Daochen Zha @zdcfrank]

Curious about what "data-centric AI" is? Our comprehensive survey delves into the increasingly important role of data in building AI systems, including the recent waves of LLMs. Check it out! https://t.co/Dh6WKs0u6B #AI #MachineLearning #LLMs

[Airbnb Engineering @AirbnbEng]

97.8% of guest activity is just listing views. We built a sequence model that learns from that noise plus years of booking history — and made search meaningfully more personalized.
https://t.co/nG3UMViHzd

[Yu-Neng Chuang @YuNengChuang]

Introducing the LTSM-bundle Package!
🌟Thrilled to launch our open-source tool
🔧Assess various crucial designs to train Large Time Series Models (LTSMs), and identity the best training practices

🔗 Paper: https://t.co/cgkb8MvA9d
🔗 GitHub: https://t.co/1RtcmEXr1u

[Daochen Zha @zdcfrank]

Join us for Data-Centric AI (DCAI) at WWW'24 with 19 paper presentations and a series of keynotes. Check out all the details at https://t.co/MD8014khrv. Secure your spot at WWW by signing up forthe DCAI workshop now: https://t.co/WcwtaNyFhQ. See you at WWW'24! 🌟

[Wei Jin @weisshelter]

【Deadline extended to Feb 15th】 Passionate about advancing AI through data excellence? Join the Data-Centric AI (DCAI) Workshop at WWW 2024 to shape the future of AI!

🌐 Submission guidelines: https://t.co/EtkEZL828q Deadline: Feb 15th, 2024

[Jiayi Yuan @jiayiy]

Excited to introduce KIVI🥝, the first 2bit KV cache quantization breakthrough! 🚀 KIVI can be directly integrated into existing LLMs without any tuning.
📄 Paper: https://t.co/UgC9L7KSm1
💻 Code: https://t.co/fQBAGrtaIC
#KIVI #LLM #AI #MachineLearning

[Qiaoyu Tan @qiaoyu_tan]

📢📊 Exciting opportunity alert! The call for papers is now open for the Data-Centric AI Workshop at #WWW2024.

🌐🤖 Join us in shaping the future of data-centric AI at #WWW2024. More info at: https://t.co/gL3rDEiqzD Submission deadline: February 10, 2024

[Daochen Zha @zdcfrank]

Sharing the video recording and slides from our recent tutorial on data-centric AI at KDD 2023. Your feedback is appreciated! #AI #datacentric #KDD2023
YouTube: https://t.co/j6EVusT3eS
Slides: https://t.co/J5pnppVDht

[Daochen Zha @zdcfrank]

🚀Join us for the KDD 2023 Tutorial “Data-centric AI: Techniques and Future Perspectives" tomorrow on Aug 8! Learn cutting-edge methods and discuss the future of data-centric AI. You can also join us virtually in Zoom. See more info at https://t.co/sXjqsavlpz #KDD2023

[Open Finance@Columbia @Ai4Finance]

FinGPT: Democratizing Internet-scale Data for Financial Large Language Models.

Paper: https://t.co/4MvIDWr9Cl
Codes: https://t.co/elrDvSRJGj

[Daochen Zha @zdcfrank]

GSL is a data-centric approach to graph learning, addressing the potential flaws in graph structures to improve performance and unlocks new possibilities. We welcome researchers to explore our code on GitHub and join us in advancing GSL. Your innovation awaits!

[Daochen Zha @zdcfrank]

Excited to announce our latest benchmark, "OpenGSL: A Comprehensive Benchmark for Graph Structure Learning", an evaluation of Graph Structure Learning (GSL) methods that learn graph topologies for GNNs. Paper: https://t.co/fww1k0GvkX Code: https://t.co/FXtemQo1hV

[Towards Data Science @TDataScience]

The Data-centric AI Concepts in Segment Anything by Henry Lai https://t.co/7xLxITwoPa

[Daochen Zha @zdcfrank]

Excited to become a Dr. !

[AK @_akhaliq]

Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models

abs: https://t.co/YloD3x98gv
github: https://t.co/3nuzrmQa1N

[Daochen Zha @zdcfrank]

Our latest work demonstrates the efficacy of pre-training cost models and searching in addressing sharding challenges in ML Systems. Learn how we partitioned a large ML model across multiple devices in our #MLSys2023 paper. Check it out!

[AK @_akhaliq]

Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models

abs: https://t.co/YloD3x98gv
github: https://t.co/3nuzrmQa1N

[Alex Ratner @ajratner]

1/ *Data* is the key differentiator in building LLMs/foundation models.

https://t.co/ubmHbvkWoh - as the world realizes this, things are going to get interesting!

Meanwhile: Enterprises will be avoiding this mess by training on their own data & knowledge for real AI moats.

[Xia “Ben” Hu @huxia]

Should we use LLMs or fine-tuned models for downstream tasks? If you are interested in this question, please take a look: Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond https://t.co/qkz6tnVsQd

[Towards Data Science @TDataScience]

Are Prompts Generated by Large Language Models (LLMs) Reliable? by Henry Lai https://t.co/D7RgLKwjnW

[Zhaozhuo Xu @ZhaozhuoX]

Introducing the first Research On Algorithms & Data Structures (ROADS) to Mega-AI Models Workshop at MLSys 2023! The workshop will focus on algorithmic approaches to address the scalability challenges of AI in the future.

CFP: https://t.co/yZer1CBrPU
DDL: May 05

[MLCommons @MLCommons]

The future of #ML is data-centric! That’s why we built #DataPerf, the leaderboard for data. It is the 1st platform and community for data-centric competitions. Together we will break through data limitations and unlock better ML for the world https://t.co/GAKiFAKS6E