# Seminar-Materials

**Repository Path**: oldshan/Seminar-Materials

## Basic Information

- **Project Name**: Seminar-Materials
- **Description**: 组会ppt与论文--每一次的精心准备都值得留下记录😛
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-06-23
- **Last Updated**: 2021-12-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 数据科学研讨会记录

### 2018-3-16
| Title                                    | Detail                                   | Author    |
| ---------------------------------------- | ---------------------------------------- | --------- |
| Why should I trust you                   | 提出了开源工具"Lime"，能够解释样本的预测结果，并且增加模型本身的可解释性  | Fuyingnan |
| Deep Residual Learning for Image Recognition | 提出了更深层次的卷积网络架构——残差网络，解决了传统模型中网络难以训练的问题   | Zhurenyu  |
| A Unified Probabilistic Framework for Name Disambiguation in Digital Library | 将姓名消歧问题formalize成一个隐马尔科夫随机场，并提出了参数估计的两阶段算法；提出了自动确定重名人数的auto K算法 | Lina      |
| JointExtractionofEntitiesandRelations    | 将实体识别和关系提取统一为序列标注问题，使用同一个模型同时进行实体识别和关系提取 | Kuangjun  |

### 2018-3-23
| Title                                    | Detail                     | Author      |
| ---------------------------------------- | -------------------------- | ----------- |
| Mask R-CNN                               | 提出了Mask R-CNN用于图像的实例分割     | yuruonan    |
| Deep Reinforcement Learning for Mention-Ranking | 采用神经网络和强化学习技术增加共指消解的准确率    | chenyuanzhe |
| Question Answering with Subgraph Embeddings | 采用基于子图嵌入的方法，进行问答系统的训练和答案预测 | tanglumin   |
| RNN学习心得                                  | 介绍了RNN相关概念，讲解了梯度消失和权重冲突问题  | yangkang    |

### 2018-3-30
| Title                                    | Detail                                   | Author     |
| ---------------------------------------- | ---------------------------------------- | ---------- |
| Reinforcement Learning for Relation Classification from Noisy Data | 提出一个新的关系分类模型，由实体选择器和关系分类器构成，能够在“Sentence Level”提取关系。将实体选择问题转换成强化学习问题。 | GuHang     |
| Pix2code: Generating Code from a Graphical User Interface Screenshot | 使用CNN和RNN的联合模型，将网页的UI图转化为对应的HTML代码       | E Shen     |
| JAVA GC机制                                | 讲解了java的内存分配机制和垃圾回收机制                    | YinJiaLing |

### 2018-4-13
| Title                                    | Detail                                   | Author     |
| ---------------------------------------- | ---------------------------------------- | ---------- |
| Convolutional Sequence to Sequence Learning | An architecture based entirely on convolutional neural networks for sequence to sequence learning(such as NMT) | CuiYiFeng  |
| DeepFM：A Factorization-Machine based Neural Network for CTR Predicti | 回顾了过去的CTR模型，以及介绍了一系列基于深度学习的CTR模型（FNN,PNN,WDL） | ChenLeiHui |
| Aspect Level Sentiment Classification with Deep Memory Network | 介绍了Memory Network，用于情感分析问题               | Void-Yu    |