Zobrazeno 1 - 10
of 2 087
pro vyhledávání: '"Maximus, A."'
Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to ho
Externí odkaz:
http://arxiv.org/abs/2411.18101
Autor:
Wang, Huaxiaoyue, Chin, Nathaniel, Gonzalez-Pumariega, Gonzalo, Sun, Xiangwan, Sunkara, Neha, Pace, Maximus Adrian, Bohg, Jeannette, Choudhury, Sanjiban
Home robots performing personalized tasks must adeptly balance user preferences with environmental affordances. We focus on organization tasks within constrained spaces, such as arranging items into a refrigerator, where preferences for placement col
Externí odkaz:
http://arxiv.org/abs/2410.19656
The detection of bias in natural language processing (NLP) is a critical challenge, particularly with the increasing use of large language models (LLMs) in various domains. This paper introduces GUS-Net, an innovative approach to bias detection that
Externí odkaz:
http://arxiv.org/abs/2410.08388
Human demonstrations as prompts are a powerful way to program robots to do long-horizon manipulation tasks. However, translating these demonstrations into robot-executable actions presents significant challenges due to execution mismatches in movemen
Externí odkaz:
http://arxiv.org/abs/2409.06615
Autor:
Pan, Li, Zhang, Yupei, Yang, Qiushi, Li, Tan, Xing, Xiaohan, Yeung, Maximus C. F., Chen, Zhen
Recently, multimodal deep learning, which integrates histopathology slides and molecular biomarkers, has achieved a promising performance in glioma grading. Despite great progress, due to the intra-modality complexity and inter-modality heterogeneity
Externí odkaz:
http://arxiv.org/abs/2408.08527
Autor:
Gupta, Prannaya, Yau, Le Qi, Low, Hao Han, Lee, I-Shiang, Lim, Hugo Maximus, Teoh, Yu Xin, Koh, Jia Hng, Liew, Dar Win, Bhardwaj, Rishabh, Bhardwaj, Rajat, Poria, Soujanya
WalledEval is a comprehensive AI safety testing toolkit designed to evaluate large language models (LLMs). It accommodates a diverse range of models, including both open-weight and API-based ones, and features over 35 safety benchmarks covering areas
Externí odkaz:
http://arxiv.org/abs/2408.03837
Autor:
Georg, Manfred, Tanzer, Garrett, Hassan, Saad, Shengelia, Maximus, Uboweja, Esha, Sepah, Sam, Forbes, Sean, Starner, Thad
Progress in machine understanding of sign languages has been slow and hampered by limited data. In this paper, we present FSboard, an American Sign Language fingerspelling dataset situated in a mobile text entry use case, collected from 147 paid and
Externí odkaz:
http://arxiv.org/abs/2407.15806
Historically, sign language machine translation has been posed as a sentence-level task: datasets consisting of continuous narratives are chopped up and presented to the model as isolated clips. In this work, we explore the limitations of this task f
Externí odkaz:
http://arxiv.org/abs/2406.11049
Autor:
Wang, Huaxiaoyue, Kedia, Kushal, Ren, Juntao, Abdullah, Rahma, Bhardwaj, Atiksh, Chao, Angela, Chen, Kelly Y, Chin, Nathaniel, Dan, Prithwish, Fan, Xinyi, Gonzalez-Pumariega, Gonzalo, Kompella, Aditya, Pace, Maximus Adrian, Sharma, Yash, Sun, Xiangwan, Sunkara, Neha, Choudhury, Sanjiban
We present MOSAIC, a modular architecture for home robots to perform complex collaborative tasks, such as cooking with everyday users. MOSAIC tightly collaborates with humans, interacts with users using natural language, coordinates multiple robots,
Externí odkaz:
http://arxiv.org/abs/2402.18796
Autor:
Josephine Kawa Maximus
Publikováno v:
HydroResearch, Vol 8, Iss , Pp 178-193 (2025)
This study assesses watershed vulnerability to erosion and sedimentation in Guyana using Digital Elevation Model (DEM) and Land Use/Land Cover (LULC) classification. It aims to assess erosion risk by examining rainfall erosivity, soil types, and spat
Externí odkaz:
https://doaj.org/article/d2b3b45b816f47e2b3c5eea3d6807185
