Google Speakers: Khiem Pham, would like to follow up one item that is to share the details of the “Open Images Extended” dataset with the participants, as below:
https://ai.ntu.edu.tw/wp-content/uploads/2023/08/AIbootcamp.jpg640960ginadeng/wp-content/uploads/2019/07/logo01.pngginadeng2021-07-21 14:32:302024-07-09 10:29:372021 Google AI BootCamp /AI 創新研究營
Symbolic-neural learning involves deep learning methods in combination with symbolic structures. A “deep learning method” is taken to be a learning process based on gradient descent on real-valued model parameters. A “symbolic structure” is a data structure involving symbols drawn from a large vocabulary; for example, sentences of natural language, parse trees over such sentences, databases (with entities viewed as symbols), and the symbolic expressions of mathematical logic or computer programs.
Symbolic-neural learning has an innovative feature that allows to model interactions between different modals: speech, vision, and language. Such multimodal information processing is crucial for realizing research outcomes in real-word.
For growing needs and attention to multimodal research, SNL workshop this year features researches on “Beyond modality: Researches across speech, vision, and language boundaries.”
Topics of interests include, but are not limited to, the following areas:
Speech, vision, and natural language interactions in robotics
Multimodal and grounded language processing
Multimodal QA and translation
Dialogue systems
Language as a mechanism to structure and reason about visual perception
Image caption generation and image generation from text
General knowledge question answering
Reading comprehension
Textual entailment
Deep learning systems across these areas share various architectural ideas. These include word and phrase embeddings, self-attention neural networks, recurrent neural networks (LSTMs and GRUs), and various memory mechanisms. Certain linguistic and semantic resources may also be relevant across these applications. For example, dictionaries, thesauri, WordNet, FrameNet, FreeBase, DBPedia, parsers, named entity recognizers, coreference systems, knowledge graphs and encyclopedias.
https://ai.ntu.edu.tw/wp-content/uploads/2019/11/book.jpg607901ginadeng/wp-content/uploads/2019/07/logo01.pngginadeng2021-06-08 16:49:452023-11-21 12:12:01【活動訊息轉發】Fifth International Workshop on Symbolic-Neural Learning (SNL-2021)迎踴躍報名參加！
Object detection in the computer vision area has been extensively studied and making tremendous progress in recent years using deep learning methods. However, due to the heavy computation required in most deep learning-based algorithms, it is hard to run these models on embedded systems, which have limited computing capabilities. In addition, the existing open datasets for object detection applied in ADAS applications usually include pedestrian, vehicles, cyclists, and motorcycle riders in western countries, which is not quite similar to the crowded Asian countries like Taiwan with lots of motorcycle riders speeding on city roads, such that the object detection models training by using the existing open datasets cannot be applied in detecting moving objects in Asian countries like Taiwan.
In this competition, we encourage the participants to design object detection models that can be applied in Taiwan’s traffic with lots of fast speeding motorcycles running on city roads along with vehicles and pedestrians. The developed models not only fit for embedded systems but also achieve high accuracy at the same time.
Regular Awards
According to the points of each team in the final evaluation, we select the highest three teams for regular awards.
Champion: $USD 1,500
1st Runner-up: $USD 1,000
3rd-place $USD 750
Special Awards
Best accuracy award – award for the highest mAP in the final competition: $USD 200;
Best bicycle detection award – award for the highest AP of bicycle recognition in the final competition: $USD 200;
Best scooter detection award – award for the highest AP of scooter recognition in the final competition: $USD 200;
All the award winners must agree to submit contest paper and attend the ACM ICMR2021 Grand Challenge PAIR Competition Special Session to present their work.
2021 Google AI BootCamp /AI 創新研究營
/在： 相關活動2021 Google AI BootCamp /AI 創新研究營
【活動訊息轉發】Fifth International Workshop on Symbolic-Neural Learning (SNL-2021)迎踴躍報名參加！
/在： All, 相關訊息Fifth International Workshop on Symbolic-Neural Learning (SNL-2021)
Symbolic-neural learning involves deep learning methods in combination with symbolic structures. A “deep learning method” is taken to be a learning process based on gradient descent on real-valued model parameters. A “symbolic structure” is a data structure involving symbols drawn from a large vocabulary; for example, sentences of natural language, parse trees over such sentences, databases (with entities viewed as symbols), and the symbolic expressions of mathematical logic or computer programs.
Symbolic-neural learning has an innovative feature that allows to model interactions between different modals: speech, vision, and language. Such multimodal information processing is crucial for realizing research outcomes in real-word.
For growing needs and attention to multimodal research, SNL workshop this year features researches on “Beyond modality: Researches across speech, vision, and language boundaries.”
Topics of interests include, but are not limited to, the following areas:
Deep learning systems across these areas share various architectural ideas. These include word and phrase embeddings, self-attention neural networks, recurrent neural networks (LSTMs and GRUs), and various memory mechanisms. Certain linguistic and semantic resources may also be relevant across these applications. For example, dictionaries, thesauri, WordNet, FrameNet, FreeBase, DBPedia, parsers, named entity recognizers, coreference systems, knowledge graphs and encyclopedias.
NExT Forum：AI and Next-Generation Communication(2021/6/6)
/在： 相關活動本次論壇邀請來自國內外領域專家，分享 AI 及 B5G 的前瞻技術研發，強化技術與創新應用。同時香港城市大學與鴻海研究院也將攜手合作，開展兩地知識發展的新篇章，期待透過海內外產官學各界的能量，開創台灣下一個科技成長動能。
主辦單位（依首字筆畫順序）
CityU CENG香港城市大學工學院
國立臺灣大學AI中心
國立臺灣大學HPC中心
財團法人人工智慧科技基金會 AIF
財團法人三創育成基金會 Star Rocket
鴻海研究院 Hon Hai Research Institute
協辦單位
中華電信股份有限公司 Emome
贊助單位
鴻海科技集團 Foxconn
MLSS 2021 TAIPEI (Machine Learning Summer School)
/在： 相關活動MLSS 2021 TAIPEI (Machine Learning Summer School)
■活動名稱：MLSS 2021 TAIPEI
■課程時間：
2021/8/2 – 2021/8/6 (週一至週五) 上午9 時至下午5 時30 分
2021/8/9–2021/8/20 (週一至週五) 晚上8 時至10 時30 分
MLSS(http://mlss.cc)為一國際機器學習暑期課程組織，始於2002 年，主要目的為推廣統計機器學習和推論的最新技術及方法。本組織每年皆有各國家申請辦理活動，邀請的講師為國際各相關領域的專家教授，提供的主題涵蓋基礎知識，到最新機器學習的實踐。
主辦單位希望藉此活動，鼓勵臺灣優秀AI 研究人才與國際社群交流，並形塑臺灣成為亞洲AI 領域區域人才中心。藉由MLSS 的多年累積之領域資源之連結，邀請各國知名領域專家授課指導，徵集世界各國優秀學生參與。
■歡迎追蹤官方Twitter掌握活動最新消息 https://twitter.com/2021Mlss
■主要學員對象：
– Students: Early-mid stage Ph.D., Graduate
– Academics: Post-doctoral Researcher, Professor
– Professionals: Corporate Specialist, Executives
■報名截止日期：
– General Program: 2021/6/30
– Standard Program: 2021/6/20
■收費方式：
– General Program: Student: free / Non-student: NTD$1500
– Standard Program: Student: NTD$1200/ Non-student: NTD$3600
■注意事項：
1. 本課程期間詳細議程及活動持續規劃中，請參看活動官網公告
2. 本課程為全英文授課，無中文翻譯，需備有中高級以上之英文聽力聽取全英文課程
3. 各program 之權益義務請仔細閱覽網頁，待收到審核通知才需要繳費
👉👉👉 MLSS 2021 TAIPEI 活動介紹-0518
主辦單位：臺灣大學人工智慧研究中心 (AINTU)、臺灣大學資訊工程學系、 臺灣大學電機工程學系、中華民國計算語言學學會 (ACLCLP)
指導單位：科技部
**本課程隨時會有更新，敬請上官網查詢最新訊息及資料，不另通知**
**主辦單位保留修改的權利**
【活動訊息轉發】「2021科研發光」系列講座歡迎踴躍報名參加！
/在： All, 相關訊息「2021科研發光」系列講座歡迎踴躍報名參加！
《科學人雜誌》與師範大學合作於2021年舉辦「2021科研發光」系列講座，今年將於北中南三地進行AI、5G，以及精準醫療三個不同主題之演講座談，三場活動分別邀請了學界與產業界的重量級講師來與學子及社會大眾分享其科學研究與最新應用。
誠摯邀請 貴會會員共同參與、交流。
2. 個人線上報名：
■ 台北場/AI智慧闖入未來新生活：https://sa.ylib.com/event/20210411/EDM.html
■ 台中場/推動5G智慧新世代：https://sa.ylib.com/event/20210417/EDM.html
■ 台南場/精準醫療新藍海：
https://sa.ylib.com/event/20210424/EDM.html
【活動訊息轉發】ACM ICMR 2021 Grand Challenge: PAIR Competition歡迎踴躍報名參加！
/在： All, 相關訊息ACM ICMR 2021 Grand Challenge: PAIR Competition歡迎踴躍報名參加！
Challenge Title:
Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries
Registration URL:
https://aidea-web.tw/icmr
Competition Start Date:
01/04/2021
Challenge Description:
Object detection in the computer vision area has been extensively studied and making tremendous progress in recent years using deep learning methods. However, due to the heavy computation required in most deep learning-based algorithms, it is hard to run these models on embedded systems, which have limited computing capabilities. In addition, the existing open datasets for object detection applied in ADAS applications usually include pedestrian, vehicles, cyclists, and motorcycle riders in western countries, which is not quite similar to the crowded Asian countries like Taiwan with lots of motorcycle riders speeding on city roads, such that the object detection models training by using the existing open datasets cannot be applied in detecting moving objects in Asian countries like Taiwan.
In this competition, we encourage the participants to design object detection models that can be applied in Taiwan’s traffic with lots of fast speeding motorcycles running on city roads along with vehicles and pedestrians. The developed models not only fit for embedded systems but also achieve high accuracy at the same time.
Regular Awards
According to the points of each team in the final evaluation, we select the highest three teams for regular awards.
Special Awards
All the award winners must agree to submit contest paper and attend the ACM ICMR2021 Grand Challenge PAIR Competition Special Session to present their work.
👇👇👇
2021年科技部AI專案計畫跨域交流觀摩會
/在： 相關活動【活動資訊】2021年科技部AI專案計畫跨域交流觀摩會
指導單位:科技部、國立交通大學
主辦單位:科技部補助人工智慧普適研究中心 (PAIR Labs)協辦單位: 科技部補助人工智慧技術暨全幅健康照護聯合研究中心 (AINTU)、科技部補助人工智慧製造系統研究中心 (AIMS)、科技部補助人工智慧生技醫療創新研究中心 (AIBMRC)
更多照片請參考：
https://www.facebook.com/media/set/?vanity=AI.ntucenter&set=a.746691999275842
臺大AI中心國際鏈結亮點(2020)
/在： 相關活動【臺大AI中心國際鏈結亮點(2020)】
統計至2020/12/31
⮚中心主任陳信希團隊與日本AIST AIRC及東京工業大學合作，研究成果發表在ECIR 2020會議。
⮚中心主任陳信希團隊與日本AIST AIRC於NTCIR-15共同舉辦學術競賽: “FinNum-2: Numeral Attachment in Financial Social Media Data”
⮚與日本RIKEN AIP於NTCIR-15共同舉辦競賽：“SHINRA2020-ML: Categorizing Wikipedia Entities into fine-grained named entity categories in 9 languages”
⮚中心主任陳信希團隊於IJCAI-PRICAI 2020 舉辦 workshop – “The Second Workshop on Financial Technology and Natural Language Processing (FinNLP)”
⮚臺大AI中心取得台灣第一次資訊檢索領域頂級學術會議 ACM SIGIR 2023 主辦權
⮚中心主任陳信希於AAAI 2020 (New York, USA) 擔任Area Chair與NLP: Machine Translation Session Chair
⮚MAHC 截至 109 年底成果亮點
http://mahc.ntu.edu.tw/news_view.php?id=126
臺大AI中心產學交流與合作亮點(2020)
/在： 相關活動【臺大AI中心產學交流與合作亮點(2020)】
統計至2020/12/31
⮚中心轄下計畫簽訂合作案共37件，衍生合作金額約6千8百萬元
⮚中心執行capstone計畫與華碩集團/華碩雲端合作
⮚與中華軟協進行合作，進行產學媒合
⮚中心新增與多個單位進行合作交流:
上圖：中心與中國信託合作
⮚2020年中心轄下計畫產學鏈結的主要成果:
⮚MAHC 截至 109 年底成果亮點
http://mahc.ntu.edu.tw/news_view.php?id=126
2020台灣醫療科技展，本中心與臺大醫院智慧急診共同舉辦之「集點填問卷抽大獎」活動，中獎名單出爐囉!
/在： All, 相關訊息2020台灣醫療科技展，本中心與臺大醫院智慧急診共同舉辦之「集點填問卷抽大獎」活動，中獎名單出爐囉!
📌首獎：ASUS筆記型電腦ROG Strix G15 G512LU一台（市價44,900元）一台
中獎人：
📌二獎：ASUS智慧手錶VivoWatch SP（市價10,900）五支
中獎人：
📌三獎：臺大醫院及臺灣大學紀念品（市價1,080元）25份
中獎人：
恭喜以上中獎人!
本中心將後續以Email與簡訊或電話通知，敬請務必回覆並提供相關所需資料，以利獎品領取及相關稅務事宜。
另提醒，本活動進行當下，您已同意以下事項：
敬請於12/21 23:59前回覆，逾期將放棄得獎資格，不得異議。
若有任何問題，請聯繫
張先生: yoyuchang@ntu.edu.tw
抽獎注意事項：