Zobrazeno 1 - 10
of 12 665
pro vyhledávání: '"Zań, A."'
Autor:
Liang, Hanxue, Ren, Jiawei, Mirzaei, Ashkan, Torralba, Antonio, Liu, Ziwei, Gilitschenski, Igor, Fidler, Sanja, Oztireli, Cengiz, Ling, Huan, Gojcic, Zan, Huang, Jiahui
Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle with generalizability across diverse environments and fail to effectivel
Externí odkaz:
http://arxiv.org/abs/2412.03526
Autor:
Lin, Hexiang, Zhu, Huihui, Tang, Zan, Luo, Wei, Wang, Wei, Mak, Man-Wai, Jiang, Xudong, Chin, Lip Ket, Kwek, Leong Chuan, Liu, Ai Qun
Quantum computing holds promise across various fields, particularly with the advent of Noisy Intermediate-Scale Quantum (NISQ) devices, which can outperform classical supercomputers in specific tasks. However, challenges such as noise and limited qub
Externí odkaz:
http://arxiv.org/abs/2412.02955
Autor:
Chaudhry, Zan
We introduce an algorithm for efficiently representing convolution with zero-padding and stride as a sparse transformation matrix, applied to a vectorized input through sparse matrix-vector multiplication (SpMV). We provide a theoretical contribution
Externí odkaz:
http://arxiv.org/abs/2411.19419
Autor:
Ahmad, Zan, Chen, Shiyi, Yin, Minglang, Kumar, Avisha, Charon, Nicolas, Trayanova, Natalia, Maggioni, Mauro
A computed approximation of the solution operator to a system of partial differential equations (PDEs) is needed in various areas of science and engineering. Neural operators have been shown to be quite effective at predicting these solution generato
Externí odkaz:
http://arxiv.org/abs/2411.18014
Autor:
Iskarous, Mark M., Chaudhry, Zan, Li, Fangjie, Bello, Samuel, Sankar, Sriramana, Slepyan, Ariel, Chugh, Natasha, Hunt, Christopher L., Greene, Rebecca J., Thakor, Nitish V.
Humans have an exquisite sense of touch which robotic and prosthetic systems aim to recreate. We developed algorithms to create neuron-like (neuromorphic) spiking representations of texture that are invariant to the scanning speed and contact force a
Externí odkaz:
http://arxiv.org/abs/2411.17060
ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks
Autor:
Chaudhry, Zan, Mizuno, Naoko
Hyperparameter tuning remains a significant challenge for the training of deep neural networks (DNNs), requiring manual and/or time-intensive grid searches, increasing resource costs and presenting a barrier to the democratization of machine learning
Externí odkaz:
http://arxiv.org/abs/2411.16975
Recent advancements in Large Language Models (LLMs) have enhanced efficiency across various domains, including protein engineering, where they offer promising opportunities for dry lab and wet lab experiment workflow automation. Previous work, namely
Externí odkaz:
http://arxiv.org/abs/2411.06029
The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgent need for unified and accessible search methods in protein engineerin
Externí odkaz:
http://arxiv.org/abs/2411.06024
Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep learning (DL) models have shown promise, their training or implementation into protein engineering rema
Externí odkaz:
http://arxiv.org/abs/2411.04440
Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes. The primary challenge in this field is to overcome the complex interplay
Externí odkaz:
http://arxiv.org/abs/2411.00330