 |
 |
 |
 |
 |
 |
||
 |
 |
 |
 |
 |
| 7 | 1/1 | 返回列表 |
| 查看: 731 | 回復(fù): 6 | |||
| 【有獎交流】積極回復(fù)本帖子，參與交流，就有機(jī)會分得作者 shawn2047 的 36 個金幣 ，回帖就立即獲得 1 個金幣，每人有 1 次機(jī)會 | |||
[交流]
Special issue@Machine Learning Journal: Foundations of Data Science
|
|||
|
data science is a hot topic with an extensive scope, both in terms of theory and applications. machine learning forms one of its core foundational pillars. simultaneously, data science applications provide important challenges that can often be addressed only with innovative machine learning algorithms and methodologies. this special issue will highlight the latest development of the machine learning foundations of data science and on the synergy of data science and machine learning. we welcome new developments in statistics, mathematics, informatics and computing-driven machine learning for data science, including foundations, algorithms and models, systems, innovative applications and other research contributions. following the great success of the 2021 mlj special issue with dsaa'2021, this 2022 special issue will further capture the state-of-the-art machine learning advances for data science. accepted papers will be published in mlj and presented at a journal track of the 2022 ieee international conference on data science and advanced analytics (dsaa'2022) in shenzhen, october 2022. topics of interest we welcome original and well-grounded research papers on all aspects of foundations of data science including but not limited to the following topics: machine learning foundations for data science • auto-ml • information fusion from disparate sources • feature engineering, embedding, mining and representation • learning from network and graph data • learning from data with domain knowledge • reinforcement learning • non-iid learning, nonstationary, coupled and entangled learning • heterogeneous, mixed, multimodal, multi-view and multi-distributional learning • online, streaming, dynamic and real-time learning • causality and learning causal models • multi-instance, multi-label, multi-class and multi-target learning • semi-supervised and weakly supervised learning • representation learning of complex interactions, couplings, relations • deep learning theories and models • evaluation of data science systems • open domain/set learning emerging impactful machine learning applications • data preprocessing, manipulation and augmentation • autonomous learning and optimization systems • digital, social, economic and financial (finance, fintech, blockchains and cryptocurrencies) analytics • graph and network embedding and mining • machine learning for recommender systems, marketing, online and e-commerce • augmented reality, computer vision and image processing • risk, compliance, regulation, anomaly, debt, failure and crisis • cybersecurity and information disorder, misinformation/fake detection • human-centered and domain-driven data science and learning • privacy, ethics, transparency, accountability, responsibility, trust, reproducibility and retractability • fairness, explainability and algorithm bias • green and energy-efficient, scalable, cloud/distributed and parallel analytics and infrastructures • iot, smart city, smart home, telecommunications, 5g and mobile data science and learning • government and enterprise data science • transportation, manufacturing, procurement, and industry 4.0 • energy, smart grids and renewable energies • agricultural, environmental and spatio-temporal analytics and climate change contributions must contain new, unpublished, original and fundamental work relating to the machine learning journal's mission. all submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial. submission instructions submit manuscripts to: https://mach.edmgr.com. select this special issue as the article type. papers must be prepared in accordance with the journal guidelines: https://www.springer.com/journal/10994 all papers will be reviewed following standard reviewing procedures for the journal. key dates we will have a continuous submission/review process starting in oct. 2021. last paper submission deadline: 1 march 2022 paper acceptance: 1 june 2022 camera-ready: 15 june 2022 guest editors longbing cao, university of technology sydney, australia joão gama, university of porto, portugal nitesh chawla, university of notre dame, united states joshua huang, shenzhen university, china |
回復(fù)此樓» 猜你喜歡
一志愿東北大學(xué)080500，英一數(shù)二343分求調(diào)劑
已經(jīng)有3人回復(fù)
-
266求調(diào)劑
已經(jīng)有6人回復(fù)
-
材料與化工，291分，求調(diào)劑
已經(jīng)有3人回復(fù)
-
0856材料與化工求調(diào)劑！
已經(jīng)有5人回復(fù)
-
一志愿武漢理工大學(xué)-085602-總分296分-求調(diào)劑
已經(jīng)有3人回復(fù)
-
材料化工調(diào)劑
已經(jīng)有18人回復(fù)
-
085601 材料工程 320
已經(jīng)有4人回復(fù)
-
070300化學(xué) 280 一志愿太原理工 求調(diào)劑
已經(jīng)有3人回復(fù)
-
化學(xué)工程求調(diào)劑
已經(jīng)有3人回復(fù)
-
求調(diào)劑
已經(jīng)有3人回復(fù)
» 搶金幣啦！回帖就可以得到:
-
沈陽工業(yè)大學(xué)-環(huán)境電化學(xué)技術(shù)研究團(tuán)隊(duì)-招收碩士研究生（調(diào)劑）
+2/246
-
長期招收有色金屬、稀土、固體氧化物燃料電池等領(lǐng)域碩士和博士研究生
+1/87
-
湖北大學(xué)生科院邢瓊老師課題組招碩士/博士（導(dǎo)師人好！空間充裕！補(bǔ)貼到位�。�
+1/85
-
大理大學(xué)博士研究生招生
+1/59
-
招中藥學(xué)和藥學(xué)專業(yè)研究生
+1/37
-
華中科技大學(xué)材料學(xué)院國家級人才團(tuán)隊(duì)招收2026年碩士研究生
+1/34
-
華中科技大學(xué)牛廣達(dá)教授招聘博士后
+1/18
-
吉林大學(xué)張?jiān)綕淌趫F(tuán)隊(duì)長期招收博士后、2026年入學(xué)博士研究生和科研助理
+1/12
-
加拿大卡爾頓大學(xué)【招博后】【心理學(xué)人工智能】
+1/11
-
【接收調(diào)劑】澳科大誠招2026年秋季碩士研究生（3月5日下午18:00截止）
+1/9
-
濟(jì)南大學(xué)耿兵氟化學(xué)課題組現(xiàn)招收2026級化學(xué)工程學(xué)碩專碩共4名
+1/8
-
中南林業(yè)科技大學(xué)生物質(zhì)綠色轉(zhuǎn)化與功能材料課題組現(xiàn)招收2026年秋季入學(xué)博士研究生
+1/8
-
電子科技大學(xué)材料學(xué)院可持續(xù)發(fā)展未來技術(shù)創(chuàng)新中心招收博士生及科研助理
+1/7
-
中國科學(xué)院大學(xué)化學(xué)科學(xué)學(xué)院：接收有機(jī)化學(xué)專業(yè)調(diào)劑生（限報考中科院系統(tǒng)考生）
+1/3
-
有人帶、成果多、經(jīng)費(fèi)足的課題組招生�。�！
+1/3
-
深圳大學(xué)何傳新課題組誠聘“百人計(jì)劃”（副）教授、助理教授
+1/2
-
濟(jì)南大學(xué)耿兵氟化學(xué)課題組現(xiàn)招收2026級化學(xué)工程學(xué)碩專碩共4名
+1/1
-
26屆計(jì)算機(jī)、電子信息類、電科、控制、通信考研T j信息pp駿：74+08+76+6+20
+1/1
-
11408 372分 求調(diào)劑 有頂會論文國創(chuàng)競賽六級
+1/1
-
Human IL-1β ELISA試劑盒如何解析炎癥與抗腫瘤免疫的復(fù)雜關(guān)聯(lián)？
+1/1
簡單回復(fù)
tzynew2樓
2022-02-15 12:43
回復(fù)
shawn2047(金幣+1): 謝謝參與
看i 發(fā)自小木蟲Android客戶端
bjdxyxy3樓
2022-02-15 12:47
回復(fù)
shawn2047(金幣+1): 謝謝參與
。 發(fā)自小木蟲Android客戶端
nono20094樓
2022-02-15 12:48
回復(fù)
shawn2047(金幣+1): 謝謝參與
`
bjdxyxy5樓
2022-02-17 22:18
回復(fù)
bjdxyxy6樓
2022-02-18 22:54
回復(fù)
超級老快7樓
2022-02-20 07:41
回復(fù)
shawn2047(金幣+1): 謝謝參與
， 發(fā)自小木蟲Android客戶端
相關(guān)版塊跳轉(zhuǎn)
我要訂閱樓主 shawn2047 的主題更新
| 7 | 1/1 | 返回列表 |
| 最具人氣熱帖推薦 [查看全部] | 作者 | 回/看 | 最后發(fā)表 | |
|---|---|---|---|---|
|
[考研] 304分材料專碩求調(diào)劑 +8 | qiuzhigril 2026-03-03 | 10/500 |
|
|---|---|---|---|---|
|
|
[考研] 一志愿314求調(diào)劑 +6 | 202111120625 2026-03-03 | 6/300 |
|
|
|
[考研]
085600 英一數(shù)二272求調(diào)劑
5+6
|
vida_a 2026-03-01 | 47/2350 |
|
|
|
[考研] 一志愿西交大材料學(xué)碩 346 求調(diào)劑 +3 | zju51 2026-03-04 | 3/150 |
|
|
[考研] 0703化學(xué)調(diào)劑 +4 | G212 2026-03-03 | 5/250 |
|
|
|
[考研] 289求調(diào)劑 +8 | yang婷 2026-03-02 | 10/500 |
|
|
|
[考研] 復(fù)試調(diào)劑 +4 | 杰尼龜aaa 2026-03-03 | 4/200 |
|
|
|
[考研] 理學(xué)，工學(xué)，農(nóng)學(xué)調(diào)劑，少走彎路，這里歡迎您！ +8 | likeihood 2026-03-02 | 11/550 |
|
|
|
[考研] 085600材料與化工 298 調(diào)劑 +3 | 小西笑嘻嘻 2026-03-03 | 3/150 |
|
|
|
[考研] 289求調(diào)劑 +7 | BrightLL 2026-03-02 | 9/450 |
|
|
|
[考研] 299求調(diào)劑 +5 | kkcoco25 2026-03-02 | 9/450 |
|
|
|
[考研] 291求調(diào)劑 +3 | MuoLuo1312 2026-03-02 | 6/300 |
|
|
|
[考研] 沒上岸的看過來 +3 | tangxiaotian 2026-03-01 | 3/150 |
|
|
|
[論文投稿]
通訊作者寫誰，問題是你意想不到的問題
15+3
|
阿爾法啊 2026-03-01 | 3/150 |
|
|
|
[考研] 材料學(xué)調(diào)劑 +10 | 提神豆沙包 2026-02-28 | 12/600 |
|
|
|
[考研] 材料類求調(diào)劑 +11 | wana_kiko 2026-02-28 | 14/700 |
|
|
|
[基金申請] 成果系統(tǒng)訪問量大，請一小時后再嘗試。---NSFC啥時候好哦，已經(jīng)兩天這樣了 +4 | NSFC2026我來了 2026-02-28 | 4/200 |
|
|
|
[碩博家園] 博士自薦 +7 | 科研狗111 2026-02-26 | 11/550 |
|
|
|
[考研] 291分工科求調(diào)劑 +9 | science餓餓 2026-03-01 | 10/500 |
|
|
|
[考研] 290求調(diào)劑 +9 | 材料專碩調(diào)劑； 2026-02-28 | 11/550 |
|