We have met some terrific talents lately and we want to meet more. Shout out to lateral thinkers and bright engineers to join a talented, fearless and relentless team Bell Labs Cambridge to do career-defining work on devices that matter. Researchers & engineers, tell us why you?

Very happy to share that our work on self-supervised federated learning in resource-constrained settings has been accepted at #ICML 2022. A fantastic outcome for the internship work by Ekdeep Singh Lubana in collaboration with Ian Tang Fahim Kawsar. Arxiv link and more details are coming soon.

An excellent summary of our upcoming ICML paper on unsupervised FL. The paper is out on arXiv arxiv.org/abs/2205.11506. Also, a big thank you to the Flower framework flower.dev which helped us scale our FL experiments.

Glad to share that our work FLAME has been accepted to IMWUT '22. We explore algorithmic & system challenges for federated learning in the presence of multiple data-generating devices on a user. Proud of our intern Hyunsung Cho who led this work. arxiv.org/abs/2202.08922 Fahim Kawsar

If you are attending #ICML2022, please join our spotlight talk on self-supervised FL on July 19 and poster presentation on July 21. Please feel free to DM if you are up for a chat on FL, self-supervision, and embedded ML. icml.cc/virtual/2022/s…

Looking forward to presenting our work and learning more about ML efficiency @ Deep Learning Indaba. Kudos to the incredible organizing team for putting this event together.

"At the stroke of the midnight hour, when the world sleeps, India will awake to life and freedom. Happy 75th Independence Day! The journey has just begun #IndiaAt75

Brilliant talk today by Ian Tang on our collaborative self-supervised work aimed at making HAR training data-efficient. #UbiComp2022 UbiComp

Hyunsung Cho (Hyunsung Cho) is presenting her work FLAME 🔥 which shows how to make federated learning work in multi-device environments #UbiComp2022

Shohreh Deldari (@ShohrehDeldari) from RMIT is now presenting her super cool work on cross modal self supervised learning. #UbiComp2022

Charlie Dean was outside the crease for more than 85% of all balls she started at the non-striker's end. "Spirit of cricket!" 🙄

All train services between London and Cambridge are delayed because there is a swan 🦢 sitting on the track 🤷🏻‍♂️🤦‍♂️

🥁🥁 Join us for ACM SIGCHI Symposium for 𝐇𝐂𝐈 𝐚𝐧𝐝 𝐅𝐫𝐢𝐞𝐧𝐝𝐬 at IIT Bombay, India, 9-11 December 2022!🧵 Speakers: namastehci.in/#speakers November 9th: Applications due namastehci.in/#registration 🙏 Anupriya, Akhil Mathur, Anirudha Joshi, @Pushpendra_S__, Neha Kumar

Great progress towards efficient and adaptive FL on ultra-constrained devices. Work led by Fan Vincent Mo during his internship at Bell Labs.

This is huge: Llama-v2 is open source, with a license that authorizes commercial use! This is going to change the landscape of the LLM market. Llama-v2 is available on Microsoft Azure and will be available on AWS, Hugging Face and other providers Pretrained and fine-tuned

So happy to see this important work published. Model fairness should be a key consideration when we optimize machine learning pipelines for edge devices. Our paper offers a framework to approach on-device ML fairness, along with extensive experimental findings for speech models.

🤿 What is latent masking and why is it relevant to multimodal learning? In our paper in ML4MHD at #ICML2023 we presented CroSSL, a new model that masks intermediate embeddings to improve multimodal learning. 📖 Paper: arxiv.org/abs/2307.16847 🎥 Video: drive.google.com/file/d/1lF5GsQ…

1/5 Tired of self-supervised models that can't adapt to new data? In our #WACV2024 paper, we propose Kaizen - an end-to-end approach for continual learning that performs knowledge distillation across both the pre-training and fine-tuning steps. 👉 arxiv.org/abs/2303.17235