Zobrazeno 1 - 10
of 3 017
pro vyhledávání: '"Niraj, K."'
Autor:
Wang, Hongjie, Ma, Chih-Yao, Liu, Yen-Cheng, Hou, Ji, Xu, Tao, Wang, Jialiang, Juefei-Xu, Felix, Luo, Yaqiao, Zhang, Peizhao, Hou, Tingbo, Vajda, Peter, Jha, Niraj K., Dai, Xiaoliang
Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the number of pixels. This makes minute-length video generation extremely expe
Externí odkaz:
http://arxiv.org/abs/2412.09856
Autor:
Nepal, Niraj K., Wang, Lin-Lin
We present a workflow that iteratively combines \textit{ab-initio} calculations with a machine-learning (ML) guided search for superconducting compounds with both dynamical stability and instability from imaginary phonon modes, the latter of which ha
Externí odkaz:
http://arxiv.org/abs/2409.18441
Autor:
Li, Chia-Hao, Jha, Niraj K.
Wearable medical sensors (WMSs) are revolutionizing smart healthcare by enabling continuous, real-time monitoring of user physiological signals, especially in the field of consumer healthcare. The integration of WMSs and modern machine learning (ML)
Externí odkaz:
http://arxiv.org/abs/2409.09549
Autor:
Nepal, Niraj K., Slade, Tyler J., Blawat, Joanna M., Eaton, Andrew, Palmstrom, Johanna C., Ueland, Benjamin G., Kaminski, Adam, McQueeney, Robert J., McDonald, Ross D., Canfield, Paul C., Wang, Lin-Lin
Quantum materials with stacked van der Waals (vdW) layers hosting non-trivial band structure topology and magnetism have shown many interesting properties. Using high-throughput density functional theory calculations, we design and predict tetragonal
Externí odkaz:
http://arxiv.org/abs/2407.20938
Autor:
Yue, Chang, Jha, Niraj K.
The ubiquity of neural networks (NNs) in real-world applications, from healthcare to natural language processing, underscores their immense utility in capturing complex relationships within high-dimensional data. However, NNs come with notable disadv
Externí odkaz:
http://arxiv.org/abs/2407.04168
Autor:
Lala, Sayeri, Jha, Niraj K.
Clinical randomized controlled trials (RCTs) collect hundreds of measurements spanning various metric types (e.g., laboratory tests, cognitive/motor assessments, etc.) across 100s-1000s of subjects to evaluate the effect of a treatment, but do so at
Externí odkaz:
http://arxiv.org/abs/2406.16351
Publikováno v:
Computational Materials Science, 244, 113247 (2024)
High-throughput $ab$ $initio$ calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoreti
Externí odkaz:
http://arxiv.org/abs/2406.04537
Deep neural networks exhibit remarkable performance, yet their black-box nature limits their utility in fields like healthcare where interpretability is crucial. Existing explainability approaches often sacrifice accuracy and lack quantifiable measur
Externí odkaz:
http://arxiv.org/abs/2406.00539
Diffusion Models (DMs) have exhibited superior performance in generating high-quality and diverse images. However, this exceptional performance comes at the cost of expensive architectural design, particularly due to the attention module heavily used
Externí odkaz:
http://arxiv.org/abs/2405.05252
Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we propose DynaMo, a suite of multi-token prediction language models that
Externí odkaz:
http://arxiv.org/abs/2405.00888