Search Results for author: Tanya Berger-Wolf

Found 27 papers, 16 papers with code

Building Machine Learning Challenges for Anomaly Detection in Science

no code implementations3 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

Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation

1 code implementation CVPR 2025 Ziheng Zhang, Jianyang Gu, Arpita Chowdhury, Zheda Mai, David Carlyn, Tanya Berger-Wolf, Yu Su, Wei-Lun Chao

Despite its simplicity and computational efficiency, CAM often struggles to identify discriminative regions that distinguish visually similar fine-grained classes.

Computational Efficiency

Prompt-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis

1 code implementation16 Jan 2025 Arpita Chowdhury, Dipanjyoti Paul, Zheda Mai, Jianyang Gu, Ziheng Zhang, Kazi Sajeed Mehrab, Elizabeth G. Campolongo, Daniel Rubenstein, Charles V. Stewart, Anuj Karpatne, Tanya Berger-Wolf, Yu Su, Wei-Lun Chao

We present a simple usage of pre-trained Vision Transformers (ViTs) for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as different bird species or dog breeds.

Explainable Artificial Intelligence (XAI) Explainable Models +3

Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis

1 code implementation CVPR 2025 Arpita Chowdhury, Dipanjyoti Paul, Zheda Mai, Jianyang Gu, Ziheng Zhang, Kazi Sajeed Mehrab, Elizabeth G. Campolongo, Daniel Rubenstein, Charles V. Stewart, Anuj Karpatne, Tanya Berger-Wolf, Yu Su, Wei-Lun Chao

We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as bird species.

Visual Prompt Tuning

Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge

1 code implementation1 Dec 2024 Jenna Kline, Austin O'Quinn, Tanya Berger-Wolf, Christopher Stewart

With emerging remote sensing hardware, e. g., camera traps and drones, and sophisticated AI models in situ, edge computing will be more significant in future AI-driven animal ecology (ADAE) studies.

Edge-computing

Fine-Tuning is Fine, if Calibrated

1 code implementation24 Sep 2024 Zheda Mai, Arpita Chowdhury, Ping Zhang, Cheng-Hao Tu, Hong-You Chen, Vardaan Pahuja, Tanya Berger-Wolf, Song Gao, Charles Stewart, Yu Su, Wei-Lun Chao

For example, fine-tuning a pre-trained classifier capable of recognizing a large number of classes to master a subset of classes at hand is shown to drastically degrade the model's accuracy in the other classes it had previously learned.

VLM4Bio: A Benchmark Dataset to Evaluate Pretrained Vision-Language Models for Trait Discovery from Biological Images

1 code implementation28 Aug 2024 M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Harish Babu Manogaran, Abhilash Neog, Medha Sawhney, Mridul Khurana, James P. Balhoff, Yasin Bakis, Bahadir Altintas, Matthew J. Thompson, Elizabeth G. Campolongo, Josef C. Uyeda, Hilmar Lapp, Henry L. Bart, Paula M. Mabee, Yu Su, Wei-Lun Chao, Charles Stewart, Tanya Berger-Wolf, Wasila Dahdul, Anuj Karpatne

Images are increasingly becoming the currency for documenting biodiversity on the planet, providing novel opportunities for accelerating scientific discoveries in the field of organismal biology, especially with the advent of large vision-language models (VLMs).

Hallucination

Modeling Access Differences to Reduce Disparity in Resource Allocation

no code implementations31 Jan 2024 Kenya Andrews, Mesrob Ohannessian, Tanya Berger-Wolf

Motivated by COVID-19 vaccine allocation, where vulnerable subpopulations are simultaneously more impacted in terms of health and more disadvantaged in terms of access to the vaccine, we formalize and study the problem of resource allocation when there are inherent access differences that correlate with advantage and disadvantage.

Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs

1 code implementation31 Dec 2023 Vardaan Pahuja, Weidi Luo, Yu Gu, Cheng-Hao Tu, Hong-You Chen, Tanya Berger-Wolf, Charles Stewart, Song Gao, Wei-Lun Chao, Yu Su

In this work, we exploit the structured context linked to camera trap images to boost out-of-distribution generalization for species classification tasks in camera traps.

 Ranked #1 on Image Classification on iWildCam2020-WILDS (using extra training data)

Image Classification Knowledge Graphs +2

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.

Reflections from the Workshop on AI-Assisted Decision Making for Conservation

no code implementations17 Jul 2023 Lily Xu, Esther Rolf, Sara Beery, Joseph R. Bennett, Tanya Berger-Wolf, Tanya Birch, Elizabeth Bondi-Kelly, Justin Brashares, Melissa Chapman, Anthony Corso, Andrew Davies, Nikhil Garg, Angela Gaylard, Robert Heilmayr, Hannah Kerner, Konstantin Klemmer, Vipin Kumar, Lester Mackey, Claire Monteleoni, Paul Moorcroft, Jonathan Palmer, Andrew Perrault, David Thau, Milind Tambe

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022.

Decision Making

Fairness-aware Summarization for Justified Decision-Making

no code implementations13 Jul 2021 Moniba Keymanesh, Tanya Berger-Wolf, Micha Elsner, Srinivasan Parthasarathy

In other words, decision-relevant features should provide sufficient information for the predicted outcome and should be independent of the membership of individuals in protected groups such as race and gender.

Data Poisoning Decision Making +1

Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions

1 code implementation4 Jun 2021 Shishir Adhikari, Akshay Uppal, Robin Mermelstein, Tanya Berger-Wolf, Elena Zheleva

Cannabis legalization has been welcomed by many U. S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear.

Stance Detection Weakly-supervised Learning

Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models

1 code implementation19 Mar 2021 Aynaz Taheri, Kevin Gimpel, Tanya Berger-Wolf

Recently, a few studies have focused on learning temporal information in addition to the topology of a graph.

Decoder Graph Classification +1

Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis

2 code implementations1 Feb 2020 Chainarong Amornbunchornvej, Elena Zheleva, Tanya Berger-Wolf

We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets.

Causal Inference Dynamic Time Warping +3

Framework for Inferring Following Strategies from Time Series of Movement Data

1 code implementation4 Nov 2019 Chainarong Amornbunchornvej, Tanya Berger-Wolf

Given a set of time series that includes coordinated movement and a set of candidate strategies as inputs, we provide the first methodology (to the best of our knowledge) to infer whether each individual uses local-agreement-system or dictatorship-like strategy to achieve movement coordination at the group level.

Leadership Inference Model Selection +2

Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"

1 code implementation ICCV 2019 Silvia Zuffi, Angjoo Kanazawa, Tanya Berger-Wolf, Michael J. Black

In contrast to research on human pose, shape and texture estimation, training data for endangered species is limited, the animals are in complex natural scenes with occlusion, they are naturally camouflaged, travel in herds, and look similar to each other.

Pose Estimation Texture Synthesis

Animal Wildlife Population Estimation Using Social Media Images Collections

no code implementations5 Aug 2019 Matteo Foglio, Lorenzo Semeria, Guido Muscioni, Riccardo Pressiani, Tanya Berger-Wolf

Social media is the rich source of wildlife images, which come with a huge bias, thus thwarting traditional population size estimate approaches.

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