Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Sun, Fan-Yun"'
Synthetic data is widely used in speech recognition due to the availability of text-to-speech models, which facilitate adapting models to previously unseen text domains. However, existing methods suffer in performance when they fine-tune an automatic
Externí odkaz:
http://arxiv.org/abs/2406.02925
Autor:
Yang, Yue, Sun, Fan-Yun, Weihs, Luca, VanderBilt, Eli, Herrasti, Alvaro, Han, Winson, Wu, Jiajun, Haber, Nick, Krishna, Ranjay, Liu, Lingjie, Callison-Burch, Chris, Yatskar, Mark, Kembhavi, Aniruddha, Clark, Christopher
3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope. To mitigate this limitation, we present Holodeck, a system that generates 3D envi
Externí odkaz:
http://arxiv.org/abs/2312.09067
Autor:
Sun, Fan-Yun, Tremblay, Jonathan, Blukis, Valts, Lin, Kevin, Xu, Danfei, Ivanovic, Boris, Karkus, Peter, Birchfield, Stan, Fox, Dieter, Zhang, Ruohan, Li, Yunzhu, Wu, Jiajun, Pavone, Marco, Haber, Nick
We propose Filtering Inversion (FINV), a learning framework and optimization process that predicts a renderable 3D object representation from one or few partial views. FINV addresses the challenge of synthesizing novel views of objects from partial o
Externí odkaz:
http://arxiv.org/abs/2304.00673
Autor:
Sun, Fan-Yun, Kauvar, Isaac, Zhang, Ruohan, Li, Jiachen, Kochenderfer, Mykel, Wu, Jiajun, Haber, Nick
Modeling multi-agent systems requires understanding how agents interact. Such systems are often difficult to model because they can involve a variety of types of interactions that layer together to drive rich social behavioral dynamics. Here we intro
Externí odkaz:
http://arxiv.org/abs/2208.10660
Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution. An algorithm that performs inference on a factor graph should ideally be equivariant or invariant to permutations of globa
Externí odkaz:
http://arxiv.org/abs/2109.14218
Autor:
Bear, Daniel M., Wang, Elias, Mrowca, Damian, Binder, Felix J., Tung, Hsiao-Yu Fish, Pramod, R. T., Holdaway, Cameron, Tao, Sirui, Smith, Kevin, Sun, Fan-Yun, Fei-Fei, Li, Kanwisher, Nancy, Tenenbaum, Joshua B., Yamins, Daniel L. K., Fan, Judith E.
While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion, a dataset and benchmark for rigorously evaluating the ability to pred
Externí odkaz:
http://arxiv.org/abs/2106.08261
Most chatbot literature that focuses on improving the fluency and coherence of a chatbot, is dedicated to making chatbots more human-like. However, very little work delves into what really separates humans from chatbots -- humans intrinsically unders
Externí odkaz:
http://arxiv.org/abs/2103.16429
With the development of image segmentation in computer vision, biomedical image segmentation have achieved remarkable progress on brain tumor segmentation and Organ At Risk (OAR) segmentation. However, most of the research only uses single modality s
Externí odkaz:
http://arxiv.org/abs/1910.07800
This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting the properties of molecules and co
Externí odkaz:
http://arxiv.org/abs/1908.01000
This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respectively. In the current literature, these two tasks are usually indepen
Externí odkaz:
http://arxiv.org/abs/1906.07159