# Tools

# AMineropen in new window 🌟

AMiner open in new window(aminer.orgopen in new window) aims to provide comprehensive search and mining services for researcher social networks. In this system, we focus on:

(1) creating a semantic-based profile for each researcher by extracting information from the distributed Web;

(2) integrating academic data (e.g., the bibliographic data and the researcher profiles) from multiple sources;

(3) accurately searching the heterogeneous network;

(4) analyzing and discovering interesting patterns from the built researcher social network. The main search and analysis functions in AMiner include:

• Profile searchopen in new window: input a researcher name (e.g.,Jie Tangopen in new window), the system will return the semantic-based profile created for the researcher using information extraction techniques. In the profile page, the extracted and integrated information include: contact information, photo, citation statistics, academic achievement evaluation, (temporal) research interest, educational history, personal social graph, research funding (currently only US and CN), and publication records (including citation information, and the papers are automatically assigned to several different domains).
• Expert findingopen in new window: input a query (e.g., data mining), the system will return experts on this topic. In addition, the system will suggest the top conference and the top ranked papers on this topic. There are two ranking algorithms, VSM and ACT. The former is similar to the conventional language model and the latter is based on our Author-Conference-Topic (ACT) model. Users can also provide feedbacks to the search results.
• Conference analysisopen in new window: input a conference name (e.g., KDD), the system returns who are the most active researchers on this conference, and the top-ranked papers.
• Course searchopen in new window: input a query (e.g., data mining), the system will tell you who are teaching courses relevant to the query.
• Sub-graph searchopen in new window: input a query (e.g., data mining), the system first tells you what topics are relevant to the query (e.g., five topics "Data mining", "XML Data", "Data Mining / Query Processing", "Web Data / Database design", "Web Mining" are relevant), and then display the most important sub-graph discovered on each relevant topic, augmented with a summary for the sub-graph.
• Topic browseropen in new window: based on our Author-Conference-Topic (ACT) model, we automatically discover 200 hot topics from the publications. For each topic, we automatically assign a label to represent its meanings. Furthermore, the browser presents the most active researchers, the most relevant conferences/papers, and the evolution trend of the topic is discovered.
• Academic ranksopen in new window: we define 8 measures open in new windowto evaluate the researcher's achievement. The measures open in new windowinclude "h -index", "Citation", "Uptrend, "Activity", "Longevity", "Diversity, "Sociability", "New Star". For each measure, we output a ranking list in different domains. For example, one can search who have the highest citation number in the "data mining" domain.
• User managementopen in new window: one can register as a user to: (1) modify the extracted profile information; (2) provide feedback on the search results; (3) follow researchers in AMiner; (4) create an AMiner page (which can be used to advertise confs/workshops, or recruit students).

Keep you updated with state-of-the-art technology is really important in the Machine learning and deep learning field.

There are some tips from Andre NG on how to read research papers. https://www.youtube.com/watch?v=733m6qBH-jIopen in new window

So, where can we find the popular papers?

# Paperswithcode — Browse State-of-the-Artopen in new window

The papers are well categorized so you can follow what Andre said, choose an area of interest like semantic segmentation, and read 15–20 papers to get a good understanding of this field. More importantly, you can find the paper’s code.

# labml.ai Deep Learning Paper Implementationsopen in new window

59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, …), optimizers (adam, adabelief, …), gans(cyclegan, stylegan2, …), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, … 🧠

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations. So you are able to read the paper while understanding how to implement it by Pytorch.

# labml.aiopen in new window — Trending Research Papersopen in new window

The most popular research papers on social media like Twitter. You can easily find links to download papers, paper summaries, explanation videos, and discussions.

The chrome extension is really helpful as well.

This extension shows you the following details about research papers:
✨ 2-line summary
✨ Availability source code, videos, and discussions
✨ Popularity on Twitter
✨ Conferences

# Paper lists made by Amanopen in new window

A summary of key papers in Computer Vision, NLP, and Speech recognition.

# Deep Learning Monitoropen in new window

Another website where you can find the hot papers on social media.

The great feature is that you can create some monitors with the keywords related to the topic of interest and check new updates every one or two weeks. Once you find a good paper and you log in Mendeley on this website, you can directly send it to your Mendeley account.