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pro vyhledávání: '"Li, Kevin"'
Vision Language Models (VLMs) have demonstrated strong capabilities across various visual understanding and reasoning tasks. However, their real-world deployment is often constrained by high latency during inference due to substantial compute require
Externí odkaz:
http://arxiv.org/abs/2411.03312
In this paper, we present a novel framework for the analysis of Riemann Hypothesis [27], which is composed of three key components: a) probabilistic modeling with cross entropy optimization and reasoning; b) the application of the law of large number
Externí odkaz:
http://arxiv.org/abs/2409.19790
We use images of cars of a wide range of varieties to compose an image of an animal such as a bird or a lion for the theme of environmental protection to maximize the information about cars in a single composed image and to raise the awareness about
Externí odkaz:
http://arxiv.org/abs/2409.13941
In this paper, we present a cross-entropy optimization method for hyperparameter optimization in stochastic gradient-based approaches to train deep neural networks. The value of a hyperparameter of a learning algorithm often has great impact on the p
Externí odkaz:
http://arxiv.org/abs/2409.09240
We present an axiomatic approach to combination theorems for various homological properties of groups and, more generally, of chain complexes. Examples of such properties include algebraic finiteness properties, $\ell^2$-invisibility, $\ell^2$-acycli
Externí odkaz:
http://arxiv.org/abs/2409.05774
Transformer architectures have become a dominant paradigm for domains like language modeling but suffer in many inference settings due to their quadratic-time self-attention. Recently proposed subquadratic architectures, such as Mamba, have shown pro
Externí odkaz:
http://arxiv.org/abs/2408.10189
The Android ecosystem is profoundly fragmented due to the frequent updates of the Android system and the prevalent customizations by mobile device manufacturers. Previous research primarily focused on identifying and repairing evolution-induced API c
Externí odkaz:
http://arxiv.org/abs/2408.01810
Theoretical developments in sequential Bayesian analysis of multivariate dynamic models underlie new methodology for causal prediction. This extends the utility of existing models with computationally efficient methodology, enabling routine explorati
Externí odkaz:
http://arxiv.org/abs/2406.02320
Autor:
Lee, Seongmin, Hoover, Benjamin, Strobelt, Hendrik, Wang, Zijie J., Peng, ShengYun, Wright, Austin, Li, Kevin, Park, Haekyu, Yang, Haoyang, Chau, Polo
Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex internal structures and operations often pose challenges for non-experts to grasp. We introduce Diffusion Explaine
Externí odkaz:
http://arxiv.org/abs/2404.16069
Autor:
Zheng, Xinyuan, Mehrabad, Mahmoud Jalali, Vannucci, Jonathan, Li, Kevin, Dutt, Avik, Hafezi, Mohammad, Mittal, Sunil, Waks, Edo
Non-Hermitian models describe the physics of ubiquitous open systems with gain and loss. One intriguing aspect of non-Hermitian models is their inherent topology that can produce intriguing boundary phenomena like resilient higher-order topological i
Externí odkaz:
http://arxiv.org/abs/2402.14946