Zobrazeno 1 - 10
of 14 615
pro vyhledávání: '"Basheer, A"'
We prove that using global observables to train the matrix product state ansatz results in the vanishing of all partial derivatives, also known as barren plateaus, while using local observables avoids this. This ansatz is widely used in quantum machi
Externí odkaz:
http://arxiv.org/abs/2409.10055
Autor:
Basheer, Ramzan, Mishra, Deepak
Euclidean deep learning is often inadequate for addressing real-world signals where the representation space is irregular and curved with complex topologies. Interpreting the geometric properties of such feature spaces has become paramount in obtaini
Externí odkaz:
http://arxiv.org/abs/2409.07327
Autor:
Kalbouneh, Basheer, Santiago, Jessica, Marinoni, Christian, Maartens, Roy, Clarkson, Chris, Sarma, Maharshi
Studies show that the model-independent, fully non-perturbative covariant cosmographic approach is suitable for analyzing the local Universe $(z\lesssim 0.1)$. However, accurately characterizing large and inhomogeneous mass distributions requires the
Externí odkaz:
http://arxiv.org/abs/2408.04333
Autor:
Chauhan, Mihir, Satbhai, Abhishek, Hashemi, Mohammad Abuzar, Ali, Mir Basheer, Ramamurthy, Bina, Gao, Mingchen, Lyu, Siwei, Srihari, Sargur
Handwriting Verification is a critical in document forensics. Deep learning based approaches often face skepticism from forensic document examiners due to their lack of explainability and reliance on extensive training data and handcrafted features.
Externí odkaz:
http://arxiv.org/abs/2407.21788
Autor:
Chauhan, Mihir, Hashemi, Mohammad Abuzar, Satbhai, Abhishek, Ali, Mir Basheer, Ramamurthy, Bina, Gao, Mingchen, Lyu, Siwei, Srihari, Sargur
We present SSL-HV: Self-Supervised Learning approaches applied to the task of Handwriting Verification. This task involves determining whether a given pair of handwritten images originate from the same or different writer distribution. We have compar
Externí odkaz:
http://arxiv.org/abs/2405.18320
Autor:
Joudeh, Basheer, Škorić, Boris
We calculate the average differential entropy of a $q$-component Gaussian mixture in $\mathbb R^n$. For simplicity, all components have covariance matrix $\sigma^2 {\mathbf 1}$, while the means $\{\mathbf{W}_i\}_{i=1}^{q}$ are i.i.d. Gaussian vectors
Externí odkaz:
http://arxiv.org/abs/2404.07311
We explore the possibility of characterizing the expansion rate on local cosmic scales $(z \lesssim 0.1)$, where the cosmological principle is violated, in a model-independent manner, i.e. in a more meaningful and comprehensive way than is possible u
Externí odkaz:
http://arxiv.org/abs/2401.12291
The disagreement between low- and high-redshift measurements of the Hubble parameter is emerging as a serious challenge to the standard model of cosmology. We develop a covariant cosmographic analysis of the Hubble parameter in a general spacetime, w
Externí odkaz:
http://arxiv.org/abs/2312.09875
This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL disperses the learning process amon
Externí odkaz:
http://arxiv.org/abs/2310.18479
Autor:
Verma, Khushboo, Moore, Marina, Wottrich, Stephanie, López, Karla Robles, Aggarwal, Nishant, Bhatt, Zeel, Singh, Aagamjit, Unroe, Bradford, Basheer, Salah, Sachdeva, Nitish, Arora, Prinka, Kaur, Harmanjeet, Kaur, Tanupreet, Hood, Tevon, Marquez, Anahi, Varshney, Tushar, Deng, Nanfu, Ramani, Azaan, Ishwara, Pawanraj, Saeed, Maimoona, Peña, Tatiana López Velarde, Barksdale, Bryan, Guha, Sushovan, Kumar, Satwant
In response to the pressing need for advanced clinical problem-solving tools in healthcare, we introduce BooksMed, a novel framework based on a Large Language Model (LLM). BooksMed uniquely emulates human cognitive processes to deliver evidence-based
Externí odkaz:
http://arxiv.org/abs/2310.11266