Mononito Goswami

📣 Website & Cfp: icml-structured-fm-workshop.github.io

Submission deadline: May 19
Acceptance notification: June 9
Workshop date: July 18/19

📄 Page limit: Short papers with up to 4 pages
👀 Misc.: Double blind, Non-archival, Posters & Orals for accepted papers

🚀 Interested in time series 📈📉 and tabular data? Then our @icmlconf.bsky.social’25 Workshop on Foundation Models for Structured Data is exactly what you are looking for 😁!
Send us your best work and join us in Vancouver 🇨🇦🍁

AutonLab (Auton Lab) Informed Machine Learning, Knowledge Discovery, Pragmatic Deep Learning, Making AI Usable & Accessible

Check out all our datasets and models 👉🏻 huggingface.co/AutonLab

And stay tuned for long-context MOMENT! 😁

Investigating Compositional Reasoning in Time Series Foundation Models Large pre-trained time series foundation models (TSFMs) have demonstrated promising zero-shot performance across a wide range of domains. However, a question remains: Do TSFMs succeed solely by memori...

📜 Check out our papers hot off the press!

[O1] Investigating Compositional Reasoning in Time Series Foundation Models (arxiv.org/abs/2502.06037)
[O2] Exploring Representations and Interventions in Time Series Foundation Models (arxiv.org/abs/2409.12915)

Also we're continuing to push the boundaries of science on time series foundation models, and I am thrilled about our recent work on 🔹 compositional reasoning [O1] and 🔹 steering [O2] time series foundation models!

🚀 Excited to share that MOMENT has been downloaded 1.3M+ times on HuggingFace, with over 280K downloads in just the last month!

MOMENT is the BERT for time series. It can solve many tasks across different domains, including healthcare! Best part? It's completely open source!

Also my awesome collaborators and mentees, Arjun Choudhry and @inverse-hessian.bsky.social are looking for PhD positions. If you are looking for students with experience in multimodal foundation models, efficiency, mechanistic interpretability, I don’t know of anyone better than them!

Come chat with me and my wonderful collaborators about Time Series Foundation Models at the NeurIPS Workshops on Time Series Age of Large Models, Systems 2 Reasoning, and Foundation Models Interventions!

Interested in learning about making univariate time series foundation models multivariate with only 24 parameters?

Check out our poster on Long Context TSFMs in the NeurIPS’24 FITML Workshop, East Exhibition Hall A at 11:30 AM

Excited to be at NeurIPS'24, where I'll be presenting at several workshops! Looking forward to chatting about (time series & tabular) foundation models, data science agents, or ML for healthcare!

Also, I'm also on the industry job market, looking forward to connect 😁!

Excited to be at NeurIPS'24, where I'll be presenting at several workshops! Looking forward to chatting about (time series & tabular) foundation models, data science agents, or ML for healthcare!

Also, I'm also on the industry job market, looking forward to connect 😁!

If you’re headed to NeurIPS 2024, and want to learn about IBM Research Human-Centered Trustworthy AI, there are many many opportunities to do so.

1. Start with the official NeurIPS explorer by @henstr.bsky.social and @benhoover.bsky.social. It is infoviz par excellence. neurips2024.vizhub.ai

if you're a PhD student at CMU doing AI/ML, lmk if you want to be added to this starter pack.

(I don't belong in this list, but I don't know how to remove myself from this pack 😂)

go.bsky.app/9APVxQQ

The question that a reviewer should ask themselves is:
Does this paper take a gradient step in a promising direction? Is the community better off with this paper published? If the answer is yes, then the recommendation should be to accept.

Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress? Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs) via continued pret...

Medically adapted foundation models (think Med-*) turn out to be more hot air than hot stuff. Correcting for fatal flaws in evaluation, the current crop are no better on balance than generic foundation models, even on the very tasks for which benefits are claimed.
arxiv.org/abs/2411.04118