Structured prediction

baseNP: doesn’t contain any recursive parts. chunking: build the tree for the sentence Level of representation: * Brown Corpus (level1: pos) * Penn Trecbank (level2: sys) * PropBank (level3: sen) * Framenet (level4: ) All of these need lots of human labor.   h(x) = argmin(y in Y) E_(y~p(Y|X))[l(y,x,Y)] l (y*,x,y) = 1 – delta(y,y*) … Read moreStructured prediction

Talk: Learning efficiency of outcome in games

By Eva Tardos Repeated games player’s value/cost additive over periods, while playing players try to learn what is the best from past data what can we say about the outcome? how long do they have to stay to ensure OK social welfare? Result: routing, limit for very small users Theorem: In any network with continuous, … Read moreTalk: Learning efficiency of outcome in games

How to write MP4 with OpenCV3

When trying to write MP4 file (H264), I tired code of

And I got error saying:

This problem is solved by changing the fourcc to the ASCII number directly to cv2.VideoWriter(), i.e.


Paper Reading: View Direction and Bandwidth Adaptive 360 Degree Video Streaming using a Two-Tier System

Original paper: Each segment is coded as a base-tier (BT) chunk, and multiple enhancement-tier (ET) chunks. BT chunks: represent the entire 360 view at a low bit rate and are pre-fetched in a long display buffer to smooth the network jitters effectively and guarantee that any desired FOV can be rendered with minimum stalls. ET … Read morePaper Reading: View Direction and Bandwidth Adaptive 360 Degree Video Streaming using a Two-Tier System

PointNet, PointNet++, and PU-Net

point cloud -> deep network -> classification / segmentation / super-resolution traditional classification / segmentation: projection onto 2D plane and use 2D classification / segmentation unordered set point(Vec3) -> feature vector (Vec5) -> normalize (end with the bound of the pointcloud) N points: segmentation: feature from N points ->NxK classes of each point (each point … Read morePointNet, PointNet++, and PU-Net

Lecture 10: Neural Network

Deep learning Representation learning Rule-based high explainability Linguistic supervision Semi-supervision have small set of data with label has large set of data without label Recurrent-level supervision Language structure description lengths DL= size(lexicon) + size( encoding) lex1 do the kitty you like see Lex2 do you like see the kitty How to evaluate the two lexicons? … Read moreLecture 10: Neural Network

Questions about “Foveated 3D Graphics (Microsoft)” User Study

Problem1: They did test for only one scene. The first problem is that foveation level is highly depentent on scene. They may get totally different parameters if they change to another scene. Of course, this is the problem of all the user studies. Till now, only NVIDIA mentiond the multliple factors affecting vision. However, they … Read moreQuestions about “Foveated 3D Graphics (Microsoft)” User Study