Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Nathaniel Blanchard"'
Autor:
William Pickard, Kelsey Sikes, Huma Jamil, Nicholas Chaffee, Nathaniel Blanchard, Michael Kirby, Chris Peterson
Publikováno v:
Frontiers in Computer Science, Vol 5 (2023)
Artificial neural networks (ANNs) are sensitive to perturbations and adversarial attacks. One hypothesized solution to adversarial robustness is to align manifolds in the embedded space of neural networks with biologically grounded manifolds. Recent
Externí odkaz:
https://doaj.org/article/3fad801f4afb40c282d0a0e5ad15cef2
Publikováno v:
Frontiers in Computer Science, Vol 5 (2023)
In the ever-evolving landscape of deep learning, novel designs of neural network architectures have been thought to drive progress by enhancing embedded representations. However, recent findings reveal that the embedded representations of various sta
Externí odkaz:
https://doaj.org/article/0a143553e3414a5ab2ee9808c86b4f6e
Autor:
Ibrahim Khebour, Richard Brutti, Indrani Dey, Rachel Dickler, Kelsey Sikes, Kenneth Lai, Mariah Bradford, Brittany Cates, Paige Hansen, Changsoo Jung, Brett Wisniewski, Corbyn Terpstra, Leanne Hirshfield, Sadhana Puntambekar, Nathaniel Blanchard, James Pustejovsky, Nikhil Krishnaswamy
Publikováno v:
Journal of Open Humanities Data, Vol 10, Pp 7-7 (2024)
To adequately model information exchanged in real human-human interactions, considering speech or text alone leaves out many critical modalities. The channels contributing to the “making of sense” in human-human interactions include but are not l
Externí odkaz:
https://doaj.org/article/9ef46dac551c4ca5b7706fe2295eec64
Publikováno v:
User Modeling and User-Adapted Interaction. 33:617-641
Task-unrelated thought (TUT), commonly referred to as mind wandering, is a mental state where a person's attention moves away from the task-at-hand. This state is extremely common, yet not much is known about how to measure it, especially during dyad
Classroom environments are challenging for artificially intelligent agents primarily because classroom noise dilutes the interpretability and usefulness of gathered data. This problem is exacerbated when groups of students participate in collaborativ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd04b661d1bf6367f94a90d8bafda33b
Autor:
Matt Gorbett, Nathaniel Blanchard
Publikováno v:
2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW).
Autor:
Chaitanya Roygaga, Dhruva Patil, Michael Boyle, William Pickard, Raoul Reisers, Aparna Bharati, Nathaniel Blanchard
Publikováno v:
2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW).
We present evidence that many common convolutional neural networks (CNNs) trained for face verification learn functions that are nearly equivalent under rotation. More specifically, we demonstrate that one face verification model's embeddings (i.e. l
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8febec5396baed37b683433f1b21695a
http://arxiv.org/abs/2106.07822
http://arxiv.org/abs/2106.07822
Publikováno v:
WACV
In this paper, we present a novel, end-to-end 6D object pose estimation method that operates on RGB inputs. Our approach is composed of 2 main components: the first component classifies the objects in the input image and proposes an initial 6D pose e
Publikováno v:
WACV
In this paper, we present an end-to-end future-prediction model that focuses on pedestrian safety. Specifically, our model uses previous video frames, recorded from the perspective of the vehicle, to predict if a pedestrian will cross in front of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e923f4c82e360b8fecebccf9213e2634
http://arxiv.org/abs/1910.09077
http://arxiv.org/abs/1910.09077