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.
由臺灣大學與日本京都大學所共同舉辦之NTU-KU Joint Symposium on Digital Health至今已邁入第五屆,本年度將由臺灣大學與日本京都大學主辦,臺大國際產學聯盟、全幅健康照護中心、NTU SPARK共同合辦,並邀請廣達電腦贊助舉辦。
本次大會主題為 “AI & Smart Medicine for Digital Health”,邀請來自臺灣大學及京都大學8位專家學者進行2場Keynote Speech及6場專題演講,安排6位來自學界及產業界專家於下午進行Innovation Presentation,除演講之外,現場也有海報展示,誠摯地邀請您一同共襄盛舉及廣傳活動訊息!
以下為活動資訊,敬請參考:
時間:2020/12/16 (三) 臺灣時間上午9:15 –下午5:00 (上午8:45開始報到)
地點:臺大博理館一樓 101演講廳 (臺北市羅斯福路四段1號)
報名連結報名已截止:https://forms.gle/acv9SbRniFHgSUEM9
詳細活動議程請參考附件活動海報,若有任何問題麻煩與活動聯絡人聯繫:
盧小姐 (rflu74193@ntu.edu.tw) 邱小姐 (tosyc@ntu.edu.tw)
主辦單位:國立臺灣大學、京都大學
協辦單位:臺大國際產學聯盟、科技部補助全幅健康照護中心、NTU SPARK
贊助單位:廣達電腦
注意事項:
1. 時值新冠肺炎疫情期間,現場參與者請協助配合主辦單位相關防疫措施:
* 進入場館配合量額溫 (若體溫高於37.5度者恕無法進入場館)、酒精消毒雙手
* 入場前須協助掃描QR code,於台大防疫管制系統填寫實名制資料
* 活動進行期間請全程配戴口罩
* 若於活動前14天有國外旅遊史或接觸史者,請勿報名參與現場活動
2. 本次活動進行將以英文為主 (Event held in English.)
3. 本活動全程免費,人數有限,滿額即結束報名
4. 活動相關資訊及報名通知信將以E-mail寄發,請留意信箱,若有任何問題請與活動聯絡人聯繫
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https://ai.ntu.edu.tw/wp-content/uploads/2020/11/活動海報_NTU-KU-Joint-Symposium-on-Digital-Health.jpg23841684ginadeng/wp-content/uploads/2019/07/logo01.pngginadeng2020-11-27 10:10:362023-11-21 10:43:23NTU-KU Joint Symposium on Digital Health