开课单位--佐治亚理工学院
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Modeling and Visualizing Tonality in North Indian Classical Music[北印度古典音乐音调的建模与可视化]
  Parag Chordia(佐治亚理工学院) North Indian classical music (NICM) is based on raag, a melodic structure within which musicians improvise. Raags define hierarchical pitch relationsh...
热度：25

2
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping[SUSTain：张量的可扩展无监督评分及其在表型分析中的应用]
  Ioakim Perros(佐治亚理工学院) This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers. ...
热度：22

3
Cooperative Visual Dialogue with Deep RL[与Deep RL合作进行视觉对话]
  Dhruv Batra(佐治亚理工学院) Cooperative Visual Dialogue with Deep RL
热度：30

4
Nano Social Science: An Emerging Specialization[社会科学：一个新兴的专业化纳米]
  Alan L. Porter(佐治亚理工学院) 我在乔治亚科技集团（艾伦L.波特，菲利普夏皮罗和Jan youtie）继续分析扩大编制的纳米科学和纳米工程学（“纳米”）从三个数据库的研究文献，以及nano...
热度：62

5
Automatic Whiteout++: Correcting Mini-QWERTY Typing Errors Using Keypress Timing[自动白化++：纠正迷你全键盘打字错误使用按键时机]
  James Clawson(佐治亚理工学院) By analyzing features of users typing, Automatic Whiteout ++ detects and corrects up to 32.37% of the errors made by typists while using a mini-QWERTY...
热度：104

6
Celebratory Technology: New Directions for Food Research in HCI[在人机交互技术：庆祝食品研究的新方向]
  Andrea Grimes(佐治亚理工学院) We describe the existing and potential HCI design space around human-food interaction and in particular motivate future researchon designing technolog...
热度：34

7
Bounding Excess Risk in Machine Learning[机器学习中的边界超额风险]
  Vladimir Koltchinskii(佐治亚理工学院) We will discuss a general approach to the problem of bounding the excess risk of learning algorithms based on empirical risk minimization (possibly pe...
热度：326

8
How Does the Data Sampling Strategy Impact the Discovery of Information Diffusion in Social Media? [数据抽样策略如何影响社会媒体中信息扩散的发现？]
  Munmun De Choudhury(佐治亚理工学院) Platforms such as Twitter have provided researchers with ample opportunities to analytically study social phenomena. There are however, significant co...
热度：23

9
Discovering Options from Example Trajectories[从示例轨迹发现选项]
  Peng Zang(佐治亚理工学院) We present a novel technique for automated problem decomposition to address the problem of scalability in Reinforcement Learning. Our technique makes ...
热度：33

10
Learning Dissimilarities by Ranking: From SDP to QP[按排名学习差异：从SDP到QP]
  Hua Ouyang(佐治亚理工学院) We consider the problem of learning dissimilarities between points via formulations which preserve a specified ordering between points rather than the...
热度：104
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