Search Results for author: Manil Maskey

Found 12 papers, 5 papers with code

WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks

1 code implementation3 Dec 2024 Rajat Shinde, Christopher E. Phillips, Kumar Ankur, Aman Gupta, Simon Pfreundschuh, Sujit Roy, Sheyenne Kirkland, Vishal Gaur, Amy Lin, Aditi Sheshadri, Udaysankar Nair, Manil Maskey, Rahul Ramachandran

WxC-Bench is designed as a dataset of datasets for developing ML-models for a complex weather and climate system, addressing selected downstream tasks as machine learning phenomenon.

Challenges in Guardrailing Large Language Models for Science

no code implementations12 Nov 2024 Nishan Pantha, Muthukumaran Ramasubramanian, Iksha Gurung, Manil Maskey, Rahul Ramachandran

Existing general-purpose LLM guardrails are insufficient to address these unique challenges in the scientific domain.

Ethics

AI Foundation Model for Heliophysics: Applications, Design, and Implementation

no code implementations30 Sep 2024 Sujit Roy, Talwinder Singh, Marcus Freitag, Johannes Schmude, Rohit Lal, Dinesha Hegde, Soumya Ranjan, Amy Lin, Vishal Gaur, Etienne Eben Vos, Rinki Ghosal, Badri Narayana Patro, Berkay Aydin, Nikolai Pogorelov, Juan Bernabe Moreno, Manil Maskey, Rahul Ramachandran

Deep learning-based methods have been widely researched in the areas of language and vision, demonstrating their capacity to understand long sequences of data and their usefulness in numerous helio-physics applications.

Prithvi WxC: Foundation Model for Weather and Climate

2 code implementations20 Sep 2024 Johannes Schmude, Sujit Roy, Will Trojak, Johannes Jakubik, Daniel Salles Civitarese, Shraddha Singh, Julian Kuehnert, Kumar Ankur, Aman Gupta, Christopher E Phillips, Romeo Kienzler, Daniela Szwarcman, Vishal Gaur, Rajat Shinde, Rohit Lal, Arlindo Da Silva, Jorge Luis Guevara Diaz, Anne Jones, Simon Pfreundschuh, Amy Lin, Aditi Sheshadri, Udaysankar Nair, Valentine Anantharaj, Hendrik Hamann, Campbell Watson, Manil Maskey, Tsengdar J Lee, Juan Bernabe Moreno, Rahul Ramachandran

Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting.

Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation

no code implementations20 Jun 2024 Aman Gupta, Aditi Sheshadri, Sujit Roy, Vishal Gaur, Manil Maskey, Rahul Ramachandran

These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy.

INDUS: Effective and Efficient Language Models for Scientific Applications

no code implementations17 May 2024 Bishwaranjan Bhattacharjee, Aashka Trivedi, Masayasu Muraoka, Muthukumaran Ramasubramanian, Takuma Udagawa, Iksha Gurung, Nishan Pantha, Rong Zhang, Bharath Dandala, Rahul Ramachandran, Manil Maskey, Kaylin Bugbee, Mike Little, Elizabeth Fancher, Irina Gerasimov, Armin Mehrabian, Lauren Sanders, Sylvain Costes, Sergi Blanco-Cuaresma, Kelly Lockhart, Thomas Allen, Felix Grezes, Megan Ansdell, Alberto Accomazzi, Yousef El-Kurdi, Davis Wertheimer, Birgit Pfitzmann, Cesar Berrospi Ramis, Michele Dolfi, Rafael Teixeira de Lima, Panagiotis Vagenas, S. Karthik Mukkavilli, Peter Staar, Sanaz Vahidinia, Ryan McGranaghan, Tsendgar Lee

The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address NLP tasks, (2) a contrastive-learning based text embedding model trained using a diverse set of datasets to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation for applications which have latency or resource constraints.

Contrastive Learning Information Retrieval +4

Improving Label Error Detection and Elimination with Uncertainty Quantification

no code implementations15 May 2024 Johannes Jakubik, Michael Vössing, Manil Maskey, Christopher Wölfle, Gerhard Satzger

Therefore, we develop a range of novel, model-agnostic algorithms for Uncertainty Quantification-Based Label Error Detection (UQ-LED), which combine the techniques of confident learning (CL), Monte Carlo Dropout (MCD), model uncertainty measures (e. g., entropy), and ensemble learning to enhance label error detection.

Ensemble Learning Image Classification +2

Leveraging Citizen Science for Flood Extent Detection using Machine Learning Benchmark Dataset

no code implementations15 Nov 2023 Muthukumaran Ramasubramanian, Iksha Gurung, Shubhankar Gahlot, Ronny Hänsch, Andrew L. Molthan, Manil Maskey

Specifically, Sentinel-1 C-Band Synthetic Aperture Radar (SAR) imagery has proven to be useful in detecting water bodies due to low backscatter of water features in both co-polarized and cross-polarized SAR imagery.

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