The NeurIPS @unlearning_2023 Competition is open for submissions @kaggle
→ https://
goo.gle/45ONCc1! With the goal of developing algorithms that can unlearn a subset of training data, the competition is open to all, with prizes for the top performers. More from our earlier blog ↓
@googleai
-
NeurIPS Unlearning Competition Opens on Kaggle
By
–
-
Topics for Figure Research Event Discussion
By
–
Let us know what topics you'd like to see discussed at a figure Research@ event
-
Research@ Accra Showcases AI Applications Across Multiple Domains
By
–
Last week we hosted Research@ Accra in Ghana—it was an inspiring day of lightning talks & interactive demos where researchers explored important topics like weather impact, building detection, food security, maternal health, & automatic speech recognition.
-
Google Maps Improves Route Suggestions Using Inverse Reinforcement Learning
By
–
Presenting world-scale inverse reinforcement learning (IRL) in Google Maps. Learn how RHIP, a new IRL algorithm, along with advances in graph compression & parallelization led to relative improvement in the quality of suggested routes in Google Maps. → https://
goo.gle/3EBAc75 -
Google Chirp: Next-Gen Speech Recognition for Under-Resourced Languages
By
–
For the next episode of #ResearchBytes, check out Chirp, the next generation of speech-to-text models from Google Research! Chirp can perform automatic speech recognition on under-resourced and widely spoken languages. Watch to learn more ↓
-
Differential Privacy Composition Cost in Multi-Step ML
By
–
When applying #DifferentialPrivacy to multi-step #ML algorithms, there is a privacy loss that scales with each step, known as the cost of composition. Learn about a new paradigm that avoids this composition cost for significant improvements in utility. ↓
https://
goo.gle/44NMl3z -
TensorFlow TPU Framework Enables Large-Scale Turbulent Flow Simulations
By
–
Introducing a new simulation framework that leverages advances on @TensorFlow and TPU architecture to enable efficient large-scale simulations of turbulent flows (e.g., for cloud formation, wildfires, and more) at unprecedented scale. Learn more at https://
goo.gle/45WU93Q -
Google Sponsors Deep Learning Indaba 2023 Conference
By
–
We’re proud to be a Sponsor of the annual @DeepIndaba meeting, held this week at the University of Ghana in Accra, Ghana. Registered for #Indaba2023? We hope you’ll visit the Google booth to learn more about our ML research! Learn more about Indaba → https://
deeplearningindaba.com/2023/ -
Kaggle Competition: Optimizing AI Model Runtime Performance
By
–
Announcing a new @Kaggle competition for optimizing programs for AI model workloads, with winners invited to present at the #NeurIPS2023 ML for Systems workshop! Learn more and enter the competition → https://
kaggle.com/competitions/p
redict-ai-model-runtime
… #Kaggle #AI #ML #efficiency -
WeatherBench 2: Next Generation Data-Driven Weather Forecasting Benchmark
By
–
The weather forecast is improving… literally! Introducing WeatherBench 2, a benchmark for the next generation of data-driven, global weather forecast models, providing data, tools, & an evaluation platform. Learn how to use it and check out the website →https://t.co/xDUZ3pfLvX pic.twitter.com/CGBchDANM0
— Google AI (@GoogleAI) 31 août 2023The weather forecast is improving… literally! Introducing WeatherBench 2, a benchmark for the next generation of data-driven, global weather forecast models, providing data, tools, & an evaluation platform. Learn how to use it and check out the website →
https://
goo.gle/3YVUGAU