🔥The 9th Round of Easy Loan, Earn $40 Reward is in progress❗️ ⏰ Promotion Period: January 15th - Feburary 15th, 2025 👉 Register now and check more details at gate.io/campaigns/358

In partnership with HarvardGlobalHealthInstitute, @GoogleCloud is releasing the COVID-19 Public Forecasts to help first responders and public officials track and predict future cases. Learn more about the #COVID19 Public Forecasts here: goo.gle/3guoYn4

After 5 months of hard work, in partnership with Harvard Global Health Institute we are pleased to release the COVID-19 Public Forecasts to help first responders and public officials track and predict future cases. Huge thank you to everyone who made this possible!

Curious about the impact of individual data samples on your #ML model and want to improve performance focusing on more valuable training data? A new approach uses #ReinforcementLearning to estimate the value of individual data samples. Learn more ↓ goo.gle/34Baz5f

A nice post describing our ICML’20 paper on quantifying the value of training data using a novel approach based on meta-learning, showing which training samples are important and can be used to improve performance by removing less important samples. ai.googleblog.com/2020/10/estima…

Today on the blog we present a 2-stage framework for anomaly detection that combines recent progress on deep representation learning and classic one-class algorithms, is simple to train, and results in state-of-the-art performance. Learn more ↓ goo.gle/3hgBXeT

"Fast Sample Reweighting" is a new paper from our research group Google Cloud that allows you to re-weight training samples effectively without the need for additional unbiased reward data. arxiv.org/abs/2109.03216 PS: We’re hiring! Google AI Google Cloud #ML #research #ICCV2021

Learn more about a new ML-based framework for epidemiology that we applied to COVID-19, including forecasts that are released to the public daily. Read all about how it was developed and has been used by large organizations ↓ goo.gle/3Azo2Hj

Excited to see our latest COVID-19 forecasting paper (AI-augmented forecasting model) from @GoogleCloud appear in Nature Digital Medicine & Google AI Blog! Used in US & Japan for creating COVID-19 testing targets, allocating resources+simulating policies. ai.googleblog.com/2021/10/an-ml-…

This Google AI Blog post summarizes our research from ICLR 2021 & CVPR 2021 on anomaly detection at Google Cloud. ai.googleblog.com/2021/09/discov…

In many AI applications, it is important to learn from “rules” beyond “data”. In our recent NeurIPS paper, we propose DeepCTRL, a novel method to integrate rules into deep learning, in a way that their effect is controllable at inference. Paper link: arxiv.org/pdf/2106.07804…

Announcing the Temporal Fusion Transformer, designed specifically to handle the heterogeneity of data in multi-horizon forecasting, which achieves more accurate forecasts with increased interpretability. Read more, including real-world applications ↓ goo.gle/3oPgE84

Our recent work: Temporal Fusion Transformer (TFT), for interpretable time series forecasting. TFT has been used to help retail and logistics companies for accurate and interpretable demand forecasting, and for applications related to climate change.

Introducing a novel approach for interpretable, robust, and reliable deep neural networks (DNNs) that employs controllable rule representations, which do not require retraining to adjust the rule strength at inference. Learn more below ↓ goo.gle/3IN8TGU

Our recent work: a new design of Vision Transformer (ViT) by simply nesting stacked transformer layers on local regions of images via the proposed aggregation function.

Presenting a novel approach for pre-training video understanding models on untrimmed videos that leverages the teacher-student framework to convert noisy, weak labels to more effective pseudo-labels, resulting in state-of-the-art performance. Learn more ↓ goo.gle/3vJMtDl

Introducing Learning to Prompt (L2P), an #ML model training method that uses learnable task-relevant prompts to guide pre-trained models through training on sequential tasks and results in high performance in the #ContinualLearning setting. Read more → goo.gle/382Vygu

Read about FormNet, a sequence model for form-based document understanding that can process the more complex layouts frequently found in form documents and achieves state-of-the-art performance using less pre-training data than conventional methods. goo.gle/3JYVlbB

Today on the blog, read all about two new frameworks that address challenges with anomaly detection — the task of distinguishing anomalous from normal data — in both unsupervised and semi-supervised settings, with state-of-the-art results in both → goo.gle/3x9Emzg

Great work from the Cloud AI Research team!