Building Machine Learning Challenges for Anomaly Detection in Science

no code implementations • 3 Mar 2025 • Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova, Wahid Bhimji, Wei-Lun Chao, Chris Harris, Shih-Chieh Hsu, Hilmar Lapp, Mark S. Neubauer, Josephine Namayanja, Aneesh Subramanian, Philip Harris, Advaith Anand, David E. Carlyn, Subhankar Ghosh, Christopher Lawrence, Eric Moreno, Ryan Raikman, Jiaman Wu, Ziheng Zhang, Bayu Adhi, Mohammad Ahmadi Gharehtoragh, Saúl Alonso Monsalve, Marta Babicz, Furqan Baig, Namrata Banerji, William Bardon, Tyler Barna, Tanya Berger-Wolf, Adji Bousso Dieng, Micah Brachman, Quentin Buat, David C. Y. Hui, Phuong Cao, Franco Cerino, Yi-Chun Chang, Shivaji Chaulagain, An-Kai Chen, Deming Chen, Eric Chen, Chia-Jui Chou, Zih-Chen Ciou, Miles Cochran-Branson, Artur Cordeiro Oudot Choi, Michael Coughlin, Matteo Cremonesi, Maria Dadarlat, Peter Darch, Malina Desai, Daniel Diaz, Steven Dillmann, Javier Duarte, Isla Duporge, Urbas Ekka, Saba Entezari Heravi, Hao Fang, Rian Flynn, Geoffrey Fox, Emily Freed, Hang Gao, Jing Gao, Julia Gonski, Matthew Graham, Abolfazl Hashemi, Scott Hauck, James Hazelden, Joshua Henry Peterson, Duc Hoang, Wei Hu, Mirco Huennefeld, David Hyde, Vandana Janeja, Nattapon Jaroenchai, Haoyi Jia, Yunfan Kang, Maksim Kholiavchenko, Elham E. Khoda, Sangin Kim, Aditya Kumar, Bo-Cheng Lai, Trung Le, Chi-Wei Lee, Janghyeon Lee, Shaocheng Lee, Suzan van der Lee, Charles Lewis, Haitong Li, Haoyang Li, Henry Liao, Mia Liu, Xiaolin Liu, Xiulong Liu, Vladimir Loncar, Fangzheng Lyu, Ilya Makarov, Abhishikth Mallampalli Chen-Yu Mao, Alexander Michels, Alexander Migala, Farouk Mokhtar, Mathieu Morlighem, Min Namgung, Andrzej Novak, Andrew Novick, Amy Orsborn, Anand Padmanabhan, Jia-Cheng Pan, Sneh Pandya, Zhiyuan Pei, Ana Peixoto, George Percivall, Alex Po Leung, Sanjay Purushotham, Zhiqiang Que, Melissa Quinnan, Arghya Ranjan, Dylan Rankin, Christina Reissel, Benedikt Riedel, Dan Rubenstein, Argyro Sasli, Eli Shlizerman, Arushi Singh, Kim Singh, Eric R. Sokol, Arturo Sorensen, Yu Su, Mitra Taheri, Vaibhav Thakkar, Ann Mariam Thomas, Eric Toberer, Chenghan Tsai, Rebecca Vandewalle, Arjun Verma, Ricco C. Venterea, He Wang, Jianwu Wang, Sam Wang, Shaowen Wang, Gordon Watts, Jason Weitz, Andrew Wildridge, Rebecca Williams, Scott Wolf, Yue Xu, Jianqi Yan, Jai Yu, Yulei Zhang, Haoran Zhao, Ying Zhao, Yibo Zhong

We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR).

Anomaly Detection scientific discovery

BioCLIP: A Vision Foundation Model for the Tree of Life

3 code implementations • CVPR 2024 • Samuel Stevens, Jiaman Wu, Matthew J Thompson, Elizabeth G Campolongo, Chan Hee Song, David Edward Carlyn, Li Dong, Wasila M Dahdul, Charles Stewart, Tanya Berger-Wolf, Wei-Lun Chao, Yu Su

We then develop BioCLIP, a foundation model for the tree of life, leveraging the unique properties of biology captured by TreeOfLife-10M, namely the abundance and variety of images of plants, animals, and fungi, together with the availability of rich structured biological knowledge.

LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

1 code implementation • ICCV 2023 • Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M. Sadler, Wei-Lun Chao, Yu Su

In this work, we propose a novel method, LLM-Planner, that harnesses the power of large language models to do few-shot planning for embodied agents.

A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots

1 code implementation • Findings (ACL) 2022 • Sai Zhang, Yuwei Hu, Yuchuan Wu, Jiaman Wu, Yongbin Li, Jian Sun, Caixia Yuan, Xiaojie Wang

We find some new linguistic phenomena and interactive manners in SSTOD which raise critical challenges of building dialog agents for the task.

SSTOD

Sensei: Self-Supervised Sensor Name Segmentation

1 code implementation • Findings (ACL) 2021 • Jiaman Wu, Dezhi Hong, Rajesh Gupta, Jingbo Shang

A sensor name, typically an alphanumeric string, encodes the key context (e. g., function and location) of a sensor needed for deploying smart building applications.

Language Modeling Language Modelling +1

SeNsER: Learning Cross-Building Sensor Metadata Tagger

1 code implementation • Findings of the Association for Computational Linguistics 2020 • Yang Jiao, Jiacheng Li, Jiaman Wu, Dezhi Hong, Rajesh Gupta, Jingbo Shang

Sensor metadata tagging, akin to the named entity recognition task, provides key contextual information (e. g., measurement type and location) about sensors for running smart building applications.

named-entity-recognition Named Entity Recognition +1

RoNGBa: A Robustly Optimized Natural Gradient Boosting Training Approach with Leaf Number Clipping

no code implementations • 5 Dec 2019 • Liliang Ren, Gen Sun, Jiaman Wu

Natural gradient has been recently introduced to the field of boosting to enable the generic probabilistic predication capability.

A Data-driven Storage Control Framework for Dynamic Pricing

no code implementations • 1 Dec 2019 • Jiaman Wu, Zhiqi Wang, Chenye Wu, Kui Wang, Yang Yu

Dynamic pricing is both an opportunity and a challenge to the demand side.

Optimal Storage Control for Dynamic Pricing

no code implementations • 16 Nov 2019 • Jiaman Wu, Zhiqi Wang, Yang Yu, Chenye Wu

Renewable energy brings huge uncertainties to the power system, which challenges the traditional power system operation with limited flexible resources.

Systems and Control Systems and Control Optimization and Control