Mononito Goswami
@mononitogoswami.bsky.social
Ph.D. Student at Carnegie Mellon,
Student Research at Google
Formerly Applied Science Intern Amazon, Undergrad at Delhi Technological University
📈 Foundation Models for Structured Data (Time Series, Tabular), applications in healthcare.
Student Research at Google
Formerly Applied Science Intern Amazon, Undergrad at Delhi Technological University
📈 Foundation Models for Structured Data (Time Series, Tabular), applications in healthcare.
📣 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
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
March 25, 2025 at 6:51 PM
📣 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
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
Check out all our datasets and models 👉🏻 huggingface.co/AutonLab
And stay tuned for long-context MOMENT! 😁
And stay tuned for long-context MOMENT! 😁
AutonLab (Auton Lab)
Informed Machine Learning, Knowledge Discovery, Pragmatic Deep Learning, Making AI Usable & Accessible
huggingface.co
February 13, 2025 at 12:00 AM
Check out all our datasets and models 👉🏻 huggingface.co/AutonLab
And stay tuned for long-context MOMENT! 😁
And stay tuned for long-context MOMENT! 😁
📜 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)
[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)
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...
arxiv.org
February 13, 2025 at 12:00 AM
📜 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)
[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!
February 13, 2025 at 12:00 AM
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!
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!
December 15, 2024 at 4:20 PM
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!