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pro vyhledávání: '"Faust, P."'
Autor:
Furuta, Hiroki, Zen, Heiga, Schuurmans, Dale, Faust, Aleksandra, Matsuo, Yutaka, Liang, Percy, Yang, Sherry
Large text-to-video models hold immense potential for a wide range of downstream applications. However, these models struggle to accurately depict dynamic object interactions, often resulting in unrealistic movements and frequent violations of real-w
Externí odkaz:
http://arxiv.org/abs/2412.02617
This note introduces the $\texttt{LikelihoodGeometry}$ package for the computer algebra system $\textit{Macaulay2}$. This package gives tools to construct the likelihood correspondence of a discrete algebraic statistical model, a variety that that ti
Externí odkaz:
http://arxiv.org/abs/2411.11165
Autor:
Chagahi, Mehdi Hosseini, Dashtaki, Saeed Mohammadi, Delfan, Niloufar, Mohammadi, Nadia, Samari, Alireza, Moshiri, Behzad, Piran, Md. Jalil, Faust, Oliver
Osteoporosis is a common condition that increases fracture risk, especially in older adults. Early diagnosis is vital for preventing fractures, reducing treatment costs, and preserving mobility. However, healthcare providers face challenges like limi
Externí odkaz:
http://arxiv.org/abs/2411.00916
The ``AI Olympics with RealAIGym'' competition challenges participants to stabilize chaotic underactuated dynamical systems with advanced control algorithms. In this paper, we present a novel solution submitted to IROS'24 competition, which builds up
Externí odkaz:
http://arxiv.org/abs/2410.20096
Efficient video tokenization remains a key bottleneck in learning general purpose vision models that are capable of processing long video sequences. Prevailing approaches are restricted to encoding videos to a fixed number of tokens, where too few to
Externí odkaz:
http://arxiv.org/abs/2410.08368
Autor:
Kumar, Aviral, Zhuang, Vincent, Agarwal, Rishabh, Su, Yi, Co-Reyes, John D, Singh, Avi, Baumli, Kate, Iqbal, Shariq, Bishop, Colton, Roelofs, Rebecca, Zhang, Lei M, McKinney, Kay, Shrivastava, Disha, Paduraru, Cosmin, Tucker, George, Precup, Doina, Behbahani, Feryal, Faust, Aleksandra
Self-correction is a highly desirable capability of large language models (LLMs), yet it has consistently been found to be largely ineffective in modern LLMs. Current methods for training self-correction typically depend on either multiple models, a
Externí odkaz:
http://arxiv.org/abs/2409.12917
Autor:
Palamarchuk, Daniel, Williams, Lemara, Mayer, Brian, Danielson, Thomas, Faust, Rebecca, Deschaine, Larry, North, Chris
Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embed
Externí odkaz:
http://arxiv.org/abs/2409.10649
Autor:
Furuta, Hiroki, Lee, Kuang-Huei, Gu, Shixiang Shane, Matsuo, Yutaka, Faust, Aleksandra, Zen, Heiga, Gur, Izzeddin
Many algorithms for aligning LLMs with human preferences assume that human preferences are binary and deterministic. However, human preferences can vary across individuals, and therefore should be represented distributionally. In this work, we introd
Externí odkaz:
http://arxiv.org/abs/2409.06691
Dimension reduction (DR) can transform high-dimensional text embeddings into a 2D visual projection facilitating the exploration of document similarities. However, the projection often lacks connection to the text semantics, due to the opaque nature
Externí odkaz:
http://arxiv.org/abs/2409.03949
Autor:
Scheunemann, Lisa, Faust, Erik
The proper orthogonal decomposition (POD) -- a popular projection-based model order reduction (MOR) method -- may require significant model dimensionalities to successfully capture a nonlinear solution manifold resulting from a parameterised quasi-st
Externí odkaz:
http://arxiv.org/abs/2408.12415