Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Bautista, Miguel Ángel"'
Autor:
Zhai, Shuangfei, Zhang, Ruixiang, Nakkiran, Preetum, Berthelot, David, Gu, Jiatao, Zheng, Huangjie, Chen, Tianrong, Bautista, Miguel Angel, Jaitly, Navdeep, Susskind, Josh
Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relatively little attention in recent years. In this work,
Externí odkaz:
http://arxiv.org/abs/2412.06329
Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on unstructured data like 3D point clouds. These models are commonly trained in two stages: first, a data compressor (i.e., a va
Externí odkaz:
http://arxiv.org/abs/2412.03791
Autor:
Zhang, Qihang, Zhai, Shuangfei, Bautista, Miguel Angel, Miao, Kevin, Toshev, Alexander, Susskind, Joshua, Gu, Jiatao
Recent advancements in diffusion models have set new benchmarks in image and video generation, enabling realistic visual synthesis across single- and multi-frame contexts. However, these models still struggle with efficiently and explicitly generatin
Externí odkaz:
http://arxiv.org/abs/2412.01821
Autor:
Stracke, Nick, Baumann, Stefan Andreas, Susskind, Joshua M., Bautista, Miguel Angel, Ommer, Björn
Text-to-image generative models have become a prominent and powerful tool that excels at generating high-resolution realistic images. However, guiding the generative process of these models to consider detailed forms of conditioning reflecting style
Externí odkaz:
http://arxiv.org/abs/2405.07913
We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the heuristics of prior works and achieves state of the art results by using the advantages of scale. By training a diffusion generative model
Externí odkaz:
http://arxiv.org/abs/2311.17932
Autor:
Abnar, Samira, Saremi, Omid, Dinh, Laurent, Wilson, Shantel, Bautista, Miguel Angel, Huang, Chen, Thilak, Vimal, Littwin, Etai, Gu, Jiatao, Susskind, Josh, Bengio, Samy
Can transformers generalize efficiently on problems that require dealing with examples with different levels of difficulty? We introduce a new task tailored to assess generalization over different complexities and present results that indicate that s
Externí odkaz:
http://arxiv.org/abs/2310.08866
Autor:
Zhao, Xiaoming, Colburn, Alex, Ma, Fangchang, Bautista, Miguel Angel, Susskind, Joshua M., Schwing, Alexander G.
Rendering scenes observed in a monocular video from novel viewpoints is a challenging problem. For static scenes the community has studied both scene-specific optimization techniques, which optimize on every test scene, and generalized techniques, wh
Externí odkaz:
http://arxiv.org/abs/2310.08587
Autor:
Mazoure, Bogdan, Talbott, Walter, Bautista, Miguel Angel, Hjelm, Devon, Toshev, Alexander, Susskind, Josh
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As the appetite for large models increases, it is imperative to address,
Externí odkaz:
http://arxiv.org/abs/2306.07290
We present Manifold Diffusion Fields (MDF), an approach that unlocks learning of diffusion models of data in general non-Euclidean geometries. Leveraging insights from spectral geometry analysis, we define an intrinsic coordinate system on the manifo
Externí odkaz:
http://arxiv.org/abs/2305.15586