DataScienceLab pinned «https://jobinja.ir/companies/hospital-company-of-academics/jobs/84b»
Let’s Invent the Future Together at NVIDIA GTC
Join us for breakthroughs in AI, data center, accelerated computing, healthcare, intelligent networking, game development, and more. Discover the advanced technologies that are transforming today's industries.
Register to view the over 1400 free sessions offered during GTC.
https://www.nvidia.com/en-us/gtc/
Join us for breakthroughs in AI, data center, accelerated computing, healthcare, intelligent networking, game development, and more. Discover the advanced technologies that are transforming today's industries.
Register to view the over 1400 free sessions offered during GTC.
https://www.nvidia.com/en-us/gtc/
General Purpose Vision (GVP)
link: https://prior.allenai.org/projects/gpv
Paper: https://arxiv.org/abs/2104.00743
link: https://prior.allenai.org/projects/gpv
Paper: https://arxiv.org/abs/2104.00743
prior.allenai.org
Perceptual Reasoning and Interaction Research - General Purpose Vision
Perceptual Reasoning and Interaction Research (PRIOR) is a computer vision research team within the Allen Institute for AI. PRIOR seeks to advance computer vision to create AI systems that see, explore, learn, and reason about the world.
Forwarded from ahforoughi
Unsupervised 3D Neural Rendering of Minecraft Worlds
Work on unsupervised neural rendering framework for generating photorealistic images of Minecraft (or any large 3D block worlds).
Why this is cool: this is a step towards better graphics for games.
Project Page: https://nvlabs.github.io/GANcraft/
YouTube: https://www.youtube.com/watch?v=1Hky092CGFQ&t=2s
Work on unsupervised neural rendering framework for generating photorealistic images of Minecraft (or any large 3D block worlds).
Why this is cool: this is a step towards better graphics for games.
Project Page: https://nvlabs.github.io/GANcraft/
YouTube: https://www.youtube.com/watch?v=1Hky092CGFQ&t=2s
Forwarded from ahforoughi
دوره های سایت دیتاکمپ رو میتونید تا ۳۰ اپریل رایگان استفاده کنید
https://datacamp.com/freeweek
https://datacamp.com/freeweek
Forwarded from ahforoughi
بعد از ثبت نام میتونید تو چلنج هاش شرکت کنید و گوگل بج بگییرید.
Forwarded from ahforoughi
دو منبع خوووب برای ترنسفورمرها
https://theaisummer.com/transformer/
https://theaisummer.com/vision-transformer/
#transformer
https://theaisummer.com/transformer/
https://theaisummer.com/vision-transformer/
#transformer
AI Summer
How Transformers work in deep learning and NLP: an intuitive introduction | AI Summer
An intuitive understanding on Transformers and how they are used in Machine Translation. After analyzing all subcomponents one by one such as self-attention and positional encodings , we explain the principles behind the Encoder and Decoder and why Transformers…
Forwarded from CS50x Iran
● آغاز ثبت نام دومین دوره CS50 در ایران.
دوره مبانی علوم کامپیوتر هاروارد
برای اطلاعات بیشتر و ثبتنام به آدرس زیر مراجعه کنید:
CS50x.ir/summer2021
@CS50xIran
دوره مبانی علوم کامپیوتر هاروارد
برای اطلاعات بیشتر و ثبتنام به آدرس زیر مراجعه کنید:
CS50x.ir/summer2021
@CS50xIran
*** Call for participation: SCAI QReCC 21 Conversational Question
Answering Challenge***
We are pleased to announce that this year we will be hosting a shared
task on open-domain conversational question answering (QA) co-located
with SCAI 2021 (https://scai.info). The shared task aims to establish a
reliable benchmark for conversational QA and encourage collaboration
between communities working on dialogue response generation, passage
retrieval and reading comprehension.
Web page: https://scai.info/scai-qrecc
Leaderboard: https://www.tira.io/task/scai-qrecc
Registration: https://forms.gle/JFBXZXPtWPqbtLhu8
Register to receive further updates, make leaderboard submissions, get
access to the TIRA forum and keep in touch with other participating teams.
Submission deadline: September 8, 2021
Results announcement: September 30, 2021
Workshop presentations: October 8, 2021
* Task *
Conversational question answering (QA) is one of the core applications
for retrieval-based chatbots. In conversational QA, the task is to
answer a series of contextually-dependent questions like they may occur
in a natural human-to-human conversation. It is also possible to
participate with non-conversational approaches in the conversational
task by using our question rewrites. For more details on question
rewriting see [1].
* Dataset *
The challenge uses the QReCC dataset [1] for evaluation, to appear in
NAACL’21 proceedings. The dataset contains 14K conversations with 81K
question-answer pairs and 54M passages [2]. The passage collection was
constructed by processing 10M web pages from the Common Crawl and the
Wayback Machine [3]. We will use the submitted runs to pull alternative
answers and relevant passages that are discovered by the participating
systems to extend the coverage of the ground truth annotations and
improve the evaluation metrics.
* Evaluation *
The primary evaluation metrics is F1 and EM performance on the QA task.
However, we also, optionally, report MRR and ROUGE for passage retrieval
and question rewriting subtasks to provide better insights on the
performance over the intermediate steps.
* Workshop *
Participating teams will be invited to submit a short notebook paper
detailing their approach for publication in conjunction with the SCAI’21
workshop [4].
* Contact *
scai-qrecc@googlegroups.com
Kind regards,
Organisers of the SCAI QReCC shared task,
Svitlana, Johannes and Maik
[1] Anantha, R., Vakulenko, S., Tu, Z., Longpre, S., Pulman, S., &
Chappidi, S. (2020). Open-Domain Question Answering Goes Conversational
via Question Rewriting. NAACL’21 (to appear)
https://arxiv.org/abs/2010.04898
[2] https://github.com/apple/ml-qrecc
[3] QReCC - Question Rewriting in Conversational Context (Version
2021-05-19) [Data set] https://zenodo.org/record/4772532#.YK48Fy0Rp0s
[4] SCAI: Search-Oriented Conversational AI Online Event 8 October 2021
https://scai.info
Answering Challenge***
We are pleased to announce that this year we will be hosting a shared
task on open-domain conversational question answering (QA) co-located
with SCAI 2021 (https://scai.info). The shared task aims to establish a
reliable benchmark for conversational QA and encourage collaboration
between communities working on dialogue response generation, passage
retrieval and reading comprehension.
Web page: https://scai.info/scai-qrecc
Leaderboard: https://www.tira.io/task/scai-qrecc
Registration: https://forms.gle/JFBXZXPtWPqbtLhu8
Register to receive further updates, make leaderboard submissions, get
access to the TIRA forum and keep in touch with other participating teams.
Submission deadline: September 8, 2021
Results announcement: September 30, 2021
Workshop presentations: October 8, 2021
* Task *
Conversational question answering (QA) is one of the core applications
for retrieval-based chatbots. In conversational QA, the task is to
answer a series of contextually-dependent questions like they may occur
in a natural human-to-human conversation. It is also possible to
participate with non-conversational approaches in the conversational
task by using our question rewrites. For more details on question
rewriting see [1].
* Dataset *
The challenge uses the QReCC dataset [1] for evaluation, to appear in
NAACL’21 proceedings. The dataset contains 14K conversations with 81K
question-answer pairs and 54M passages [2]. The passage collection was
constructed by processing 10M web pages from the Common Crawl and the
Wayback Machine [3]. We will use the submitted runs to pull alternative
answers and relevant passages that are discovered by the participating
systems to extend the coverage of the ground truth annotations and
improve the evaluation metrics.
* Evaluation *
The primary evaluation metrics is F1 and EM performance on the QA task.
However, we also, optionally, report MRR and ROUGE for passage retrieval
and question rewriting subtasks to provide better insights on the
performance over the intermediate steps.
* Workshop *
Participating teams will be invited to submit a short notebook paper
detailing their approach for publication in conjunction with the SCAI’21
workshop [4].
* Contact *
scai-qrecc@googlegroups.com
Kind regards,
Organisers of the SCAI QReCC shared task,
Svitlana, Johannes and Maik
[1] Anantha, R., Vakulenko, S., Tu, Z., Longpre, S., Pulman, S., &
Chappidi, S. (2020). Open-Domain Question Answering Goes Conversational
via Question Rewriting. NAACL’21 (to appear)
https://arxiv.org/abs/2010.04898
[2] https://github.com/apple/ml-qrecc
[3] QReCC - Question Rewriting in Conversational Context (Version
2021-05-19) [Data set] https://zenodo.org/record/4772532#.YK48Fy0Rp0s
[4] SCAI: Search-Oriented Conversational AI Online Event 8 October 2021
https://scai.info
اگر کسی از شما یا دوستان شما تخصص های فوق را دارند می توانند مستقیما در سایت جاب اینجا اپلای کنند. متشکرم
Forwarded from کانال اطلاعرسانی آزمایشگاه سپهر (Ali Reza Feizi Derakhshi)
#فراخوان_مقاله
Artificial Intelligence in Biomedical Big Data and Digital Healthcare
مجله:
Future Generation Computer Systems
موضوعات:
· Deep-learning-empowered artificial intelligence models for biomedical applications
· Biomedical knowledge reasoning
· Biomedical knowledge graph construction and completion
· Biomedical knowledge graph embedding
· Biomedical knowledge extraction from electronic medical record
· Explainable diagnostics support system
· Diagnostics support system based on electronic medical record
· Multi-modal biomedical data analysis models
· Multi-modal transformer models for biomedical data
· Data anonymization methods for biomedical data
· Privacy-aware biomedical data analysis
· Federated learning for biomedical data
· Causal Inference for multi-modal biomedical data
· Self-supervised learning for multi-modal biomedical data
· Biomedical image/signal processing
· Sensing, detection, and recognition in biomedical image/signal
· Natural language processing and knowledge discovery in biomedical documents
· Emerging digital healthcare applications
· Wearable medical wireless sensors
· Mobile and cloud computing for digital healthcare
· Security, trust, and privacy in digital healthcare
لینک فراخوان
لینک مجله
@cominsys_channel
Artificial Intelligence in Biomedical Big Data and Digital Healthcare
مجله:
Future Generation Computer Systems
موضوعات:
· Deep-learning-empowered artificial intelligence models for biomedical applications
· Biomedical knowledge reasoning
· Biomedical knowledge graph construction and completion
· Biomedical knowledge graph embedding
· Biomedical knowledge extraction from electronic medical record
· Explainable diagnostics support system
· Diagnostics support system based on electronic medical record
· Multi-modal biomedical data analysis models
· Multi-modal transformer models for biomedical data
· Data anonymization methods for biomedical data
· Privacy-aware biomedical data analysis
· Federated learning for biomedical data
· Causal Inference for multi-modal biomedical data
· Self-supervised learning for multi-modal biomedical data
· Biomedical image/signal processing
· Sensing, detection, and recognition in biomedical image/signal
· Natural language processing and knowledge discovery in biomedical documents
· Emerging digital healthcare applications
· Wearable medical wireless sensors
· Mobile and cloud computing for digital healthcare
· Security, trust, and privacy in digital healthcare
لینک فراخوان
لینک مجله
@cominsys_channel