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of 2 639
pro vyhledávání: '"P Perona"'
Vertical Federated Learning (VFL) enables collaborative model training across different participants with distinct features and common samples, while preserving data privacy. Existing VFL methodologies often struggle with realistic data partitions, t
Externí odkaz:
http://arxiv.org/abs/2410.17648
There is a vast literature on representation learning based on principles such as coding efficiency, statistical independence, causality, controllability, or symmetry. In this paper we propose to learn representations from sequence data by factorizin
Externí odkaz:
http://arxiv.org/abs/2410.05564
The study of social interactions and collective behaviors through multi-agent video analysis is crucial in biology. While self-supervised keypoint discovery has emerged as a promising solution to reduce the need for manual keypoint annotations, exist
Externí odkaz:
http://arxiv.org/abs/2409.09455
We explore social perception of human faces in CLIP, a widely used open-source vision-language model. To this end, we compare the similarity in CLIP embeddings between different textual prompts and a set of face images. Our textual prompts are constr
Externí odkaz:
http://arxiv.org/abs/2408.14435
Autor:
Marks, Markus, Knott, Manuel, Kondapaneni, Neehar, Cole, Elijah, Defraeye, Thijs, Perez-Cruz, Fernando, Perona, Pietro
Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data structure or context by solving a pretext task. With SSL,
Externí odkaz:
http://arxiv.org/abs/2407.12210
Model performance evaluation is a critical and expensive task in machine learning and computer vision. Without clear guidelines, practitioners often estimate model accuracy using a one-time completely random selection of the data. However, by employi
Externí odkaz:
http://arxiv.org/abs/2406.07320
We introduce Discovering Conceptual Network Explanations (DCNE), a new approach for generating human-comprehensible visual explanations to enhance the interpretability of deep neural image classifiers. Our method automatically finds visual explanatio
Externí odkaz:
http://arxiv.org/abs/2405.15243
Autor:
Tian, Yuchen, Moreno, Ari R. Ortiz, Chipaux, Mayeul, Wu, Kaiqi, Martinez, Felipe P. Perona, Shirzad, Hoda, Hamoh, Thamir, Mzyk, Aldona, van Rijn, Patrick, Schirhagl, Romana
Diamond is increasingly popular because of its unique material properties. Diamond defects called nitrogen vacancy (NV) centers allow measurements with unprecedented sensitivity. However, to achieve ideal sensing performance NV centers need to be wit
Externí odkaz:
http://arxiv.org/abs/2404.11961
Autor:
Kay, Justin, Haucke, Timm, Stathatos, Suzanne, Deng, Siqi, Young, Erik, Perona, Pietro, Beery, Sara, Van Horn, Grant
Object detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on addressing this challenge. Unfortunately, we identify systemic benchmarki
Externí odkaz:
http://arxiv.org/abs/2403.12029
We study the topological entropy of the Lagrangian flow restricted to an energy level $E_{L}^{-1}(c) \subset TM$ for $ c >e_0(L)$. We prove that if the flow of the Tonelli Lagrangian $ L: M \to \mathbb{R}$, on a closed manifold of dimension $ n+1$, h
Externí odkaz:
http://arxiv.org/abs/2402.11416