- Analyzing Scientific Articles with fine-tuned SciBERT NER Model and Neo4j - Dec 9, 2021.
In this article, we will be analyzing a dataset of scientific abstracts using the Neo4j Graph database and a fine-tuned SciBERT model.
BERT, Graph Analytics, Neo4j, NLP, Python, Research
- 2021: A Year Full of Amazing AI papers — A Review - Dec 2, 2021.
A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code.
AI, Papers with code, Research, Review, Trends
- A First Principles Theory of Generalization - Nov 4, 2021.
Some new research from University of California, Berkeley shades some new light into how to quantify neural networks knowledge.
Neural Networks, Research, UC Berkeley
Learn To Reproduce Papers: Beginner’s Guide - Oct 25, 2021.
Step-by-step instructions on how to understand Deep Learning papers and implement the described approaches.
Beginners, Deep Learning, Papers with code, Research
Geometric foundations of Deep Learning - Jul 14, 2021.
Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.
Deep Learning, Geometry, Research
- Great New Resource for Natural Language Processing Research and Applications - May 27, 2021.
The NLP Index is a brand new resource for NLP code discovery, combining and indexing more than 3,000 paper and code pairs at launch. If you are interested in NLP research and locating the code and papers needed to understand an implement the latest research, you should check it out.
Datasets, NLP, Research
- Introducing The NLP Index - Apr 29, 2021.
The NLP Index is a brand new resource for NLP code discovery, combining and indexing more than 3,000 paper and code pairs at launch. If you are interested in NLP research and locating the code and papers needed to understand an implement the latest research, you should check it out.
Datasets, NLP, Research
How Reading Papers Helps You Be a More Effective Data Scientist - Feb 24, 2021.
By reading papers, we were able to learn what others (e.g., LinkedIn) have found to work (and not work). We can then adapt their approach and not have to reinvent the rocket. This helps us deliver a working solution with lesser time and effort.
Career Advice, Data Science, Data Scientist, Research
- Baidu Research: 10 Technology Trends in 2021 - Jan 29, 2021.
Understanding future technology trends may never have been as important as it is today. Check out the prediction of the 10 technology trends in 2021 from Baidu Research.
2021 Predictions, Baidu, Research, Trends
- Deep Learning Pioneer Geoff Hinton on his Latest Research and the Future of AI - Jan 26, 2021.
Geoff Hinton has lived at the outer reaches of machine learning research since an aborted attempt at a carpentry career a half century ago. He spoke to Craig Smith about his work In 2020 and what he sees on the horizon for AI.
AI, Capsule Networks, Deep Learning, Geoff Hinton, Neural Networks, Research
Top 10 Computer Vision Papers 2020 - Jan 8, 2021.
The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.
AI, Computer Vision, Research
- CatalyzeX: A must-have browser extension for machine learning engineers and researchers - Jan 6, 2021.
CatalyzeX is a free browser extension that finds code implementations for ML/AI papers anywhere on the internet (Google, Arxiv, Twitter, Scholar, and other sites).
Implementation, Machine Learning, Programming, Research
- 2020: A Year Full of Amazing AI Papers — A Review - Dec 28, 2020.
So much happened in the world during 2020 that it may have been easy to miss the great progress in the world of AI. To catch you up quickly, check out this curated list of the latest breakthroughs in AI by release date, along with a video explanation, link to an in-depth article, and code.
AI, Research, Trends
AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2020 and Key Trends for 2021 - Dec 3, 2020.
2020 is finally coming to a close. While likely not to register as anyone's favorite year, 2020 did have some noteworthy advancements in our field, and 2021 promises some important key trends to look forward to. As has become a year-end tradition, our collection of experts have once again contributed their thoughts. Read on to find out more.
2021 Predictions, AI, Ajit Jaokar, Analytics, Brandon Rohrer, Daniel Tunkelang, Data Science, Deep Learning, Machine Learning, Pedro Domingos, Predictions, Research, Rosaria Silipo
Facebook Open Sourced New Frameworks to Advance Deep Learning Research - Nov 17, 2020.
Polygames, PyTorch3D and HiPlot are the new additions to Facebook’s open source deep learning stack.
Deep Learning, Facebook, Open Source, PyTorch, Research
- Doing the impossible? Machine learning with less than one example - Nov 9, 2020.
Machine learning algorithms are notoriously known for needing data, a lot of data -- the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as "few-shot" or "one-shot" learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.
Algorithms, K-nearest neighbors, Machine Learning, Research
- 5 Must-Read Data Science Papers (and How to Use Them) - Oct 20, 2020.
Keeping ahead of the latest developments in a field is key to advancing your skills and your career. Five foundational ideas from recent data science papers are highlighted here with tips on how to leverage these advancements in your work, and keep you on top of the machine learning game.
Data Science, Machine Learning, P-value, Research, Software, Technical Debt, Transformer
Annotated Machine Learning Research Papers - Oct 9, 2020.
Check out this collection of annotated machine learning research papers, and no longer fear their reading.
Machine Learning, Research
- AI Papers to Read in 2020 - Sep 10, 2020.
Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.
AI, Attention, Convolutional Neural Networks, Data Science, GANs, Neural Networks, Reformer, Research
Must-read NLP and Deep Learning articles for Data Scientists - Aug 21, 2020.
NLP and deep learning continue to advance, nearly on a daily basis. Check out these recent must-read guides, feature articles, and other resources to keep you on top of the latest advancements and ahead of the curve.
Deep Learning, Google, GPT-3, NLP, OpenAI, Privacy, Research, Self-Driving, TensorFlow, Trends
- KDD-2020 (virtual), the leading conference on Data Science and Knowledge Discovery, Aug 23-27 – register now - Aug 18, 2020.
Using an interactive VR platform, KDD-2020 brings you the latest research in AI, Data Science, Deep Learning, and Machine Learning with tutorials to improve your skills, keynotes from top experts, workshops on state-of-the-art topics and over 200 research presentations.
ACM SIGKDD, COVID-19, Data Science, Deep Learning, KDD, KDD-2020, Machine Learning, Meetings, Research
- HOSTKEY GPU Grant Program - Aug 10, 2020.
The HOSTKEY GPU Grant Program is open to specialists and professionals in the Data Science sector performing research or other projects centered on innovative uses of GPU processing and which will glean practical results in the field of Data Science, with the objective of supporting basic scientific research and prospective startups.
Data Science, GPU, Research
- Is depth useful for self-attention? - Jul 27, 2020.
Learn about recent research that is the first to explain a surprising phenomenon where in BERT/Transformer-like architectures, deepening the network does not seem to be better than widening (or, increasing the representation dimension). This empirical observation is in contrast to a fundamental premise in deep learning.
Attention, BERT, Deep Learning, Research, Scalability, Transformer
- 5 Essential Papers on Sentiment Analysis - Jun 9, 2020.
To highlight some of the work being done in the field, here are five essential papers on sentiment analysis and sentiment classification.
NLP, Research, Sentiment Analysis, Text Classification
- 5 Essential Papers on AI Training Data - Jun 4, 2020.
Data pre-processing is not only the largest time sink for most Data Scientists, but it is also the most crucial aspect of the work. Learn more about training data and data processing tasks from 5 leading academic papers.
AI, Data Preparation, Data Preprocessing, Research, Training Data
13 must-read papers from AI experts - May 20, 2020.
What research articles do top AI experts in the field recommend? Find out which ones and why, then be sure to add each to your reading to do list.
AI, Andrew Ng, Hyperparameter, Kirk D. Borne, LSTM, Research
5 Papers on CNNs Every Data Scientist Should Read - Apr 20, 2020.
In this article, we introduce 5 papers on CNNs that represent both novel approaches and baselines in the field.
Convolutional Neural Networks, Data Scientist, Research
- Microsoft Research Uses Transfer Learning to Train Real-World Autonomous Drones - Mar 31, 2020.
The new research uses policies learned in simulations in real world drone environments.
Microsoft, Research, Transfer Learning
- Made With ML: Discover, build, and showcase machine learning projects - Mar 23, 2020.
This is a short introduction to Made With ML, a useful resource for machine learning engineers looking to get ideas for projects to build, and for those looking to share innovative portfolio projects once built.
GitHub, Kaggle, Machine Learning, Research
- NLP Year in Review — 2019 - Jan 23, 2020.
In this blog post, I want to highlight some of the most important stories related to machine learning and NLP that I came across in 2019.
AI, Ethics, NLP, Research, Review
Top 10 Technology Trends for 2020 - Jan 16, 2020.
With integrations of multiple emerging technologies just in the past year, AI development continues at a fast pace. Following the blueprint of science and technology advancements in 2019, we predict 10 trends we expect to see in 2020 and beyond.
2020 Predictions, AI, AutoML, Baidu, Blockchain, IoT, NLP, Quantum Computing, Research
- Disentangling disentanglement: Ideas from NeurIPS 2019 - Jan 15, 2020.
This year’s NEURIPS-2019 Vancouver conference recently concluded and featured a dozen papers on disentanglement in deep learning. What is this idea and why is it so interesting in machine learning? This summary of these papers will give you initial insight in disentanglement as well as ideas on what you can explore next.
AI, Deep Learning, Disentanglement, NeurIPS, Representation, Research
- Towards a Quantitative Measure of Intelligence: Breaking Down One of the Most Important AI Papers of 2019, Part II - Dec 31, 2019.
AI scientist Francois Chollet proposes a better framework for measuring the intelligence of AI systems.
AI, Francois Chollet, Research
- NeurIPS 2019 Outstanding Paper Awards - Dec 11, 2019.
NeurIPS 2019 is underway in Vancouver, and the committee has just recently announced this year's Outstanding Paper Awards. Find out what the selections were, along with some additional info on NeurIPS papers, here.
Conference, NeurIPS, Research
AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020 - Dec 9, 2019.
As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.
2020 Predictions, AI, Ajit Jaokar, Analytics, Andriy Burkov, Anima Anandkumar, Daniel Tunkelang, Data Science, Deep Learning, Machine Learning, Pedro Domingos, Research, Rosaria Silipo, Xavier Amatriain
- Research Guide for Depth Estimation with Deep Learning - Nov 12, 2019.
In this guide, we’ll look at papers aimed at solving the problems of depth estimation using deep learning.
Deep Learning, Neural Networks, Research
- Research Guide: Advanced Loss Functions for Machine Learning Models - Nov 6, 2019.
This guide explores research centered on a variety of advanced loss functions for machine learning models.
Machine Learning, Research
- Research Guide for Transformers - Oct 30, 2019.
The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.
BERT, NLP, Research, Transformer, ULMFiT
12 Deep Learning Researchers and Leaders - Sep 23, 2019.
Our list of deep learning researchers and industry leaders are the people you should follow to stay current with this wildly expanding field in AI. From early practitioners and established academics to entrepreneurs and today’s top corporate influencers, this diverse group of individuals is leading the way into tomorrow’s deep learning landscape.
Andrej Karpathy, Andrew Ng, Deep Learning, Demis Hassabis, Fei-Fei Li, Geoff Hinton, Ian Goodfellow, Influencers, Jeremy Howard, Research, Yann LeCun
Advice on building a machine learning career and reading research papers by Prof. Andrew Ng - Sep 5, 2019.
This blog summarizes the career advice/reading research papers lecture in the CS230 Deep learning course by Stanford University on YouTube, and includes advice from Andrew Ng on how to read research papers.
Andrew Ng, Career, Machine Learning, Research
- Gender Diversity in AI Research - Aug 21, 2019.
Through an analysis of 1.5M papers from arXiv, this study reviews the evolution of gender diversity across disciplines, countries, and institutions as well as the semantic differences between AI papers with and without female co-authors.
AI, Diversity, Research, Women
12 NLP Researchers, Practitioners & Innovators You Should Be Following - Aug 12, 2019.
Check out this list of NLP researchers, practitioners and innovators you should be following, including academics, practitioners, developers, entrepreneurs, and more.
Influencers, Jeremy Howard, NLP, Rachel Thomas, Research, Richard Socher
- High-Quality AI And Machine Learning Data Labeling At Scale: A Brief Research Report - Jul 25, 2019.
Analyst firm Cognilytica estimates that as much as 80% of machine learning project time is spent on aggregating, cleaning, labeling, and augmenting machine learning model data. So, how do innovative machine learning teams prepare data in such a way that they can trust its quality, cost of preparation, and the speed with which it’s delivered?
AI, Cloudfactory, Data Labeling, Machine Learning, Report, Research
Papers with Code: A Fantastic GitHub Resource for Machine Learning - Dec 31, 2018.
Looking for papers with code? If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out.
GitHub, Machine Learning, Research
- Bringing Machine Learning Research to Product Commercialization - Nov 27, 2018.
In this blog post I want to share some of the insights into the differences between academia and industry when applying deep learning to real-world problems as we experienced them at Merantix over the last two years.
Academics, Machine Learning, Products, Research
- Affordable online news archives for academic research - Aug 10, 2018.
Many researchers need access to multi-year historical repositories of online news articles. We identified three companies that make such access affordable, and spoke with their CEOs.
API, Research, Text Analytics, Text Mining, Webhose
- How to Make AI More Accessible - Apr 30, 2018.
I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here.
Accessibility, AI, Deep Learning, Rachel Thomas, Research
- The Qualitative Side of Quantitative Research - Nov 9, 2017.
Kevin and Koen may buy the same brand for the same reasons. On the other hand, they may buy the same brand for different reasons, or buy different brands for the same reasons, or even different brands for different reasons. The brands they purchase and the reasons why may vary by occasion, too.
Qualitative Analytics, Qualitative Research, Quantitative Analytics, Research
- Credible Sources of Accurate Information About AI - Oct 9, 2017.
I want to recommend several credible sources of accurate information. Most of the writing on this list is intended to be accessible to anyone—even if you aren’t a programmer or don’t work in tech.
AI, fast.ai, Hype, Jeremy Howard, Rachel Thomas, Research, Twitter, Zeynep Tufekci
Awesome Deep Learning: Most Cited Deep Learning Papers - Apr 21, 2017.
This post introduces a curated list of the most cited deep learning papers (since 2012), provides the inclusion criteria, shares a few entry examples, and points to the full listing for those interested in investigating further.
Deep Learning, Neural Networks, Research
Top 20 Recent Research Papers on Machine Learning and Deep Learning - Apr 6, 2017.
Machine learning and Deep Learning research advances are transforming our technology. Here are the 20 most important (most-cited) scientific papers that have been published since 2014, starting with "Dropout: a simple way to prevent neural networks from overfitting".
Deep Learning, Machine Learning, Research, Top list, Yoshua Bengio
- How To Stay Competitive In Machine Learning Business - Jan 4, 2017.
To stay competitive in machine learning business, you have to be superior than your rivals and not the best possible – says one of the leading machine learning expert. Simple rules are defined here to make that happen. Let’s see how.
Business, Business Analytics, Data Management, Machine Learning, Research
- What is Academic Torrents and Where is Data Sharing Going? - Oct 26, 2016.
Learn more about Academic Torrents, a platform for researchers to share data consisting of a site where users can search for datasets, and a BitTorrent backbone which makes sharing data scalable and fast.
Datasets, Reproducibility, Research
- Research Leaders on Data Mining, Data Science and Big Data key advances, top trends - Jan 18, 2016.
Research Leaders in Data Science and Big Data reflect on the most important research advances in 2015 and the key trends expected to dominate throughout 2016.
Pages: 1 2
Bing Liu, Charu Aggarwal, Deep Learning, Ingo Mierswa, Internet of Things, IoT, Michael Berthold, Mohammed Zaki, Neural Networks, Padhraic Smyth, Pedro Domingos, Research, Trends
- Tour of Real-World Machine Learning Problems - Dec 26, 2015.
The tour lists 20 interesting real-world machine learning problems for data science enthusiasts to learn by solving.
Datasets, Kaggle, Learning from Data, Machine Learning, Research, UCI
- Big Data accelerates medical research? Or not? - Oct 26, 2014.
Take a look at how big data in healthcare brings big opportunities, but along with those opportunities come great risk if statistics aren't carefully applied to those large datasets.
Big Data, Healthcare, Overfitting, Research
