## Survey on knowledge graph embedding

Papers Q. Wang, Z. Mao, B. Wang and L. Guo, “Knowledge Graph Embedding: A Survey of Approaches and Applications,” in IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 12, pp. 2724-2743, 1 Dec. 2017. Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich. A Review of Relational Machine Learning for Knowledge Graphs. Proc. IEEE, […]

## Curriculum Learning | Bengio et al. | ICML 2009 | 2009

Brief information Authors: Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston Published year: 2009 Publication: ICML 2009 Abstract Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. We formalize such training strategies in the context of […]

## One-Shot Imitation Learning | Yan Duan et al. | 2017

Summary Abstract Ideally, robots should be able to learn from very few demonstrations of any given task, and instantly generalize to new situations of the same task, without requiring task-specific engineering. In this paper, we propose a meta-learning framework for achieving such capability, which we call one-shot imitation learning. Task examples: to stack all blocks […]

## Samsung Notebook 9 Pen NT940X3M-K716S

모델 정보 출시연월: 2017년 9월 나의 제품 만든 연월: 2017년 12월 화면크기: 13.3 inch 가격비교 삼성 노트북9 Pen NT940X3M-K716S | 에누리 가격비교 리뷰 동영상 갤럭시 노트의 장점을 합친 최신 2in1 PC 삼성 노트북 9 펜(Pen) [LINK] Samsung NoteBook 9 Pen 15인치, 13.3인치 모델 배터리 구동 테스트 [LINK] 13.3인치: 10시간 50분 S펜을 품은 삼성의 플래그십 노트북, […]

## Conditional Generative Adversarial Nets | M. Mirza, S. Osindero | 2014

Introduction Conditional version of Generative Adversarial Nets (GAN) where both generator and discriminator are conditioned on some data y (class label or data from some other modality). Architecture Feed y into both the generator and discriminator as additional input layers such that y and input are combined in a joint hidden representation.

## Lecture 2: Markov Decision Processes | Reinforcement Learning | David Silver | Course

1. Markov Process / Markov chain 1.1. Markov process A Markov process or Markov chain is a tuple $\langle S,P \rangle$ such that $S$ is a finite set of states, and $P$ is a transition probability matrix. In a  Markov process, the initial state should be given. How do we choose the initial state is not a role of […]

## Inception Module | Summary

References Udacity (2016. 6. 6.). Inception Module. YouTube. [LINK] Udacity (2016. 6. 6.). 1×1 Convolutions. YouTube. [LINK] Tommy Mulc (2016. 9. 25.). Inception modules: explained and implemented. [LINK] Szegedy et al. (2015). Going Deeper with Convolutions. CVPR 2015. [arXiv] Summary History The inception module was first introduced in GoogLeNet for ILSVRC’14 competition. Key concept Let a convolutional network decide […]

## Graduate School Guide | Summary

References A Survival Guide to a PhD. Andrej Karpathy blog. Sep 7, 2016 [LINK] HOWTO: Get into grad school for science, engineering, math and computer science [LINK] 대학원생을 위한 지극히 개인적인 10가지 조언 [LINK] 논문 읽기 초보자를 위한 Literature survey (문헌 조사) 팁! [LINK] 석사와 박사 [LINK] 내가 대학원에서 생존한 방법 [LINK] 박사 과정을 통해 배운 것들 […]

## CS231n: Convolutional Neural Networks for Visual Recognition | Course

Lecture 6 | Training Neural Networks I Sigmoid Problems of the sigmoid activation function Problem 1: Saturated neurons kill the gradients. Problem 2: Sigmoid outputs are not zero-centered. Suppose a given feed-forward neural network has hidden layers and all activation functions are sigmoid. Then, except the first layer, the other layers get only positive inputs. […]

## Applying to Ph.D. Programs in Computer Science

Author: Mor Harchol-Balter (Computer Science Department Carnegie Mellon University) Last updated: 2014 1 Introduction This document is intended for people applying to Ph.D. programs in computer science or related areas. The author is a professor of computer science at CMU, and has been involved in the Ph.D. admissions process at CMU, U.C. Berkeley, and MIT. 2 Do I […]