Zobrazeno 1 - 10
of 1 246
pro vyhledávání: '"AHMED, IMTIAZ"'
Autor:
Era, Israt Zarin, Zhou, Fan, Raihan, Ahmed Shoyeb, Ahmed, Imtiaz, Abul-Haj, Alan, Craig, James, Das, Srinjoy, Liu, Zhichao
Directed Energy Deposition (DED) offers significant potential for manufacturing complex and multi-material parts. However, internal defects such as porosity and cracks can compromise mechanical properties and overall performance. This study focuses o
Externí odkaz:
http://arxiv.org/abs/2411.12028
Autor:
Raihan, Ahmed Shoyeb, Harper, Austin, Era, Israt Zarin, Al-Shebeeb, Omar, Wuest, Thorsten, Das, Srinjoy, Ahmed, Imtiaz
Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects such as keyhole, balling, and lack of fusion can significantly compro
Externí odkaz:
http://arxiv.org/abs/2411.10822
Autor:
Amin, Al, Hasan, Kamrul, Zein-Sabatto, Saleh, Hong, Liang, Shetty, Sachin, Ahmed, Imtiaz, Islam, Tariqul
Healthcare industries face challenges when experiencing rare diseases due to limited samples. Artificial Intelligence (AI) communities overcome this situation to create synthetic data which is an ethical and privacy issue in the medical domain. This
Externí odkaz:
http://arxiv.org/abs/2410.12245
THz band enabled large scale massive MIMO (M-MIMO) is considered as a key enabler for the 6G technology, given its enormous bandwidth and for its low latency connectivity. In the large-scale M-MIMO configuration, enlarged array aperture and small wav
Externí odkaz:
http://arxiv.org/abs/2409.16420
Autor:
Vyas, Kushal, Humayun, Ahmed Imtiaz, Dashpute, Aniket, Baraniuk, Richard G., Veeraraghavan, Ashok, Balakrishnan, Guha
Implicit neural representations (INRs) have demonstrated success in a variety of applications, including inverse problems and neural rendering. An INR is typically trained to capture one signal of interest, resulting in learned neural features that a
Externí odkaz:
http://arxiv.org/abs/2409.09566
Autor:
Alemohammad, Sina, Humayun, Ahmed Imtiaz, Agarwal, Shruti, Collomosse, John, Baraniuk, Richard
The artificial intelligence (AI) world is running out of real data for training increasingly large generative models, resulting in accelerating pressure to train on synthetic data. Unfortunately, training new generative models with synthetic data fro
Externí odkaz:
http://arxiv.org/abs/2408.16333
Autor:
Humayun, Ahmed Imtiaz, Amara, Ibtihel, Schumann, Candice, Farnadi, Golnoosh, Rostamzadeh, Negar, Havaei, Mohammad
Deep generative models learn continuous representations of complex data manifolds using a finite number of samples during training. For a pre-trained generative model, the common way to evaluate the quality of the manifold representation learned, is
Externí odkaz:
http://arxiv.org/abs/2408.08307
In this paper, we overview one promising avenue of progress at the mathematical foundation of deep learning: the connection between deep networks and function approximation by affine splines (continuous piecewise linear functions in multiple dimensio
Externí odkaz:
http://arxiv.org/abs/2408.04809
We develop Scalable Latent Exploration Score (ScaLES) to mitigate over-exploration in Latent Space Optimization (LSO), a popular method for solving black-box discrete optimization problems. LSO utilizes continuous optimization within the latent space
Externí odkaz:
http://arxiv.org/abs/2406.09657
Reducing Carbon dioxide (CO2) emission is vital at both global and national levels, given their significant role in exacerbating climate change. CO2 emission, stemming from a variety of industrial and economic activities, are major contributors to th
Externí odkaz:
http://arxiv.org/abs/2405.02340