Assalamu’alaikum Wr. Wb. Berikut kami sampaikan link Grup WA, distribusi Dosen dan Jadwal Perkuliahan Sertifikasi Al Islam dan Kemuhammadiyahan Semester Gasal 2022/2023 untuk mahasiswa FTI UAD, Informasi selengkapnya silahkan download link dibawah ini: LINK GRUP WA KULIAH AIK FTI 2022-2023 Distribusi dosen Jadwal Perkuliahan Sertifikasi AIK Semester Gasal TA 2022-2023
Program Studi Informatika UAD akan melaksanakan Responsi Praktikum semester Gasal Tahun akademik 2022-2023, dijadwalkan tanggal 09-14 Januari 2023. Mahasiswa peserta Praktikum dimohon mempersiapkan dengan baik, agar dapat mengikuti Responsi dengan lancar dan memperoleh hasil yang terbaik.
Matakuliah Metodologi Penelitian (Metopen) akan melaksankan Ujian Akhir Semester (UAS) Semester Ganjil T.A. 2022/2023 berupa Seminar Proposal. Untuk dapat mengikuti UAS Metopen (Seminar Proposal), wajib memenuhi ketentuan sebagai berikut: Pendaftaran: Tanggal: 5 – 16 Januari 2023 Link pendaftaran: https://s.uad.id/reg_semprop_tif Pelaksanaan: Tanggal : 9 – 20 Januari 2023 Sifat : secara online/offline (undangan melalui Email dan […]
Federated optimization in heterogeneous networks by Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith MLSys Proceedings Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems […]
Graph convolutional neural networks for web-scale recommender systems by Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton ACM Digital Library Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions […]
Convolutional neural networks: an overview and application in radiology by R Yamashita, M Nishio, RKG Do, K Togashi Insights into imaging Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to […]
The unreasonable effectiveness of deep features as a perceptual metric by Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are […]
A comprehensive survey on graph neural networks by Zonghan Wu; Shirui Pan; Fengwen Chen; Guodong Long; Chengqi Zhang Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia IEEE Transactions on Neural Networks and Learning Systems Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video […]
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations by M Raissi, P Perdikaris, GE Karniadakis Journal of Computational physics We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In […]
Kepala Laboratorium telah selesai masa jabatannya, maka akan dilakukan pemilihan kandidat baru. Pelaksanaan pemilihan hari Rabu 5, Januari 2023. Sebelum pemilihan akan dilakukan Sosialisasi tata cara pemilihan yang dipimpin oleh Kaprodi , Dr. Murinto. Selanjutnya akan dilakukan pemilihan secara bertahap. Tahap Pertama: Pemilihan Kepala Laboratorium Riset Tahap Kedua: Pemilihan Kepala Laboratorium Praktikum Adapun tata cara pemilihan dengan menggunakan […]
Kampus 4
Jalan Ahmad Yani (Ringroad Selatan) Tamanan, Banguntapan, Bantul, Yogyakarta 55166
Telepon : (0274) 563515, 511830, 379418, 371120
Faximille : 0274-564604
Email : prodi[at]tif.uad.ac.id
Daftar di UAD dan kembangkan potensimu dengan banyak program yang bisa dipilih untuk calon mahasiswa
Informasi PMB
Universitas Ahmad Dahlan
Telp. (0274) 563515
Hotline PMB
S1 – 0853-8500-1960
S2 – 0878-3827-1960
© 2021-2023 Program Studi Informatika | Home |