Zobrazeno 1 - 10
of 1 360
pro vyhledávání: '"Ullah Ihsan"'
Autor:
Ullah Ihsan, Murtaza Khadijah, Ammara Hafiza, Misbah, Bhinder Munir Ahmad, Riaz Amjad, Shehzad Wasim, Zahoor Muhammad Yasir
Publikováno v:
Open Life Sciences, Vol 17, Iss 1, Pp 81-90 (2022)
Claudin-14 protein plays an essential role in regulating calcium ions in the kidney and ear. Two phenotypes, hearing loss and kidney stones, were reportedly associated with variations in the CLDN14 gene. This study aimed to understand CLDN14 mutation
Externí odkaz:
https://doaj.org/article/5e61af5fd33640d9b4ca711bc1b06e96
Autor:
Bhimji, Wahid, Calafiura, Paolo, Chakkappai, Ragansu, Chou, Yuan-Tang, Diefenbacher, Sascha, Dudley, Jordan, Farrell, Steven, Ghosh, Aishik, Guyon, Isabelle, Harris, Chris, Hsu, Shih-Chieh, Khoda, Elham E, Lyscar, Rémy, Michon, Alexandre, Nachman, Benjamin, Nugent, Peter, Reymond, Mathis, Rousseau, David, Sluijter, Benjamin, Thorne, Benjamin, Ullah, Ihsan, Zhang, Yulei
The FAIR Universe -- HiggsML Uncertainty Challenge focuses on measuring the physics properties of elementary particles with imperfect simulators due to differences in modelling systematic errors. Additionally, the challenge is leveraging a large-comp
Externí odkaz:
http://arxiv.org/abs/2410.02867
Autor:
Couto, Paulo Henrique, Ho, Quang Phuoc, Kumari, Nageeta, Rachmat, Benedictus Kent, Khuong, Thanh Gia Hieu, Ullah, Ihsan, Sun-Hosoya, Lisheng
Publikováno v:
Conf{\'e}rence sur l'Apprentissage Automatique 2024, Jul 2024, Lille, France
Recent advancements in Artificial Intelligence (AI), particularly the widespread adoption of Large Language Models (LLMs), have significantly enhanced text analysis capabilities. This technological evolution offers considerable promise for automating
Externí odkaz:
http://arxiv.org/abs/2406.10294
Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation method mainly relies on the chosen lo
Externí odkaz:
http://arxiv.org/abs/2404.07686
In novelty detection, the goal is to decide if a new data point should be categorized as an inlier or an outlier, given a training dataset that primarily captures the inlier distribution. Recent approaches typically use deep encoder and decoder netwo
Externí odkaz:
http://arxiv.org/abs/2404.04456
Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limit
Externí odkaz:
http://arxiv.org/abs/2402.06969
Autor:
Ullah, Ihsan, Carrión-Ojeda, Dustin, Escalera, Sergio, Guyon, Isabelle, Huisman, Mike, Mohr, Felix, van Rijn, Jan N, Sun, Haozhe, Vanschoren, Joaquin, Vu, Phan Anh
Publikováno v:
36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks., NeurIPS, Nov 2022, New Orleans, United States
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open datasets, each having at least 20 classes with 40 examples per class, wit
Externí odkaz:
http://arxiv.org/abs/2302.08909
Autor:
Carrión-Ojeda, Dustin, Chen, Hong, Baz, Adrian El, Escalera, Sergio, Guan, Chaoyu, Guyon, Isabelle, Ullah, Ihsan, Wang, Xin, Zhu, Wenwu
We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new t
Externí odkaz:
http://arxiv.org/abs/2208.14686
Autor:
Baz, Adrian El, Ullah, Ihsan, Alcobaça, Edesio, Carvalho, André C. P. L. F., Chen, Hong, Ferreira, Fabio, Gouk, Henry, Guan, Chaoyu, Guyon, Isabelle, Hospedales, Timothy, Hu, Shell, Huisman, Mike, Hutter, Frank, Liu, Zhengying, Mohr, Felix, Öztürk, Ekrem, van Rijn, Jan N., Sun, Haozhe, Wang, Xin, Zhu, Wenwu
Publikováno v:
NeurIPS 2021 Competition and Demonstration Track, Dec 2021, On-line, United States
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are avai
Externí odkaz:
http://arxiv.org/abs/2206.08138
Autor:
Ullah, Ihsan, An, Sion, Kang, Myeongkyun, Chikontwe, Philip, Lee, Hyunki, Choi, Jinwoo, Park, Sang Hyun
Publikováno v:
In Neural Networks November 2024 179