Top 8 trends from ICLR 2019

Overview of trends on #ICLR2019:
1. Inclusivity
2. Unsupervised representation learning & transfer learning
3. Retro ML
4. RNN is losing its luster with researchers
5. GANs are still going on strong
6. The lack of biologically inspired deep learning
7. Reinforcement learning is still the most popular topic by submissions
8. Most accepted papers will be quickly forgotten

Link: https://huyenchip.com/2019/05/12/top-8-trends-from-iclr-2019.html

Paper on RoBERTa, the current holder of the pole position on the GLUE leaderboard ( https://gluebenchmark.com/leaderboard/ )

https://arxiv.org/abs/1907.11692

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu et al.: https://arxiv.org/abs/1907.11692

#bert #naturallanguageprocessing #unsupervisedlearning

Now AI can also be used to identify fake text.
These researchers have released a tool called has GLTR - Giant Language Model Test Room
1) Test yourself here GLTR tool page: https://gltr.io/
2) GitHub link: https://github.com/HendrikStrobelt/detecting-fake-text
3) Paper link: https://arxiv.org/pdf/1906.04043.pdf

https://t.iss.one/ArtificialIntelligenceArticles

Facebook Research at ACL 2019

https://ai.facebook.com/blog/facebook-research-at-acl-2019/

Deep Learning with MATLAB: Transfer Learning in 10 Lines of MATLAB Code
https://nl.mathworks.com/videos/deep-learning-with-matlab-transfer-learning-in-10-lines-of-matlab-code-1487714838381.html

Machine Learning Top 10 Articles for the Past Month (v.July 2019)

https://medium.com/@Mybridge/machine-learning-top-10-articles-for-the-past-month-v-july-2019-178436f99201 https://t.iss.one/ArtificialIntelligenceArticles

Low-Rank Matrix Completion: A Contemporary Survey. arxiv.org/abs/1907.11705

DEAM: Accumulated Momentum with Discriminative Weight for Stochastic Optimization. arxiv.org/abs/1907.11307

Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation. arxiv.org/abs/1907.11292

Yann LeCun :


"EGG is a new toolkit that allows researchers and developers to quickly create game simulations in which two neural network agents devise their own discrete communication system in order to solve a task together."


https://code.fb.com/ai-research/egg-toolkit/

https://t.iss.one/ArtificialIntelligenceArticles

The ACM Turing Lecture on "The #DeepLearning Revolution" is now available
Presented by each winner on the topic of their choice at a forum of their choice in the year they received the ACM A.M. Turing Award https://amturing.acm.org/lectures.cfm
#ArtificialIntelligence #SelfSupervisedLearning

Tensorflow implementation of U-GAT-IT

Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

GitHub, by Junho Kim : https://github.com/taki0112/UGATIT

#tensorflow #unsupervisedlearning #generativemodels

ConvNet Playground

An interactive visualization tool for exploring Convolutional #NeuralNetworks applied to the task of semantic image search.

Created by Victor Dibia at Fast Forward Labs : https://convnetplayground.fastforwardlabs.com/#/

#datavisualization #deeplearning #machinelearning

Implicit Generation and Generalization in Energy-Based Models

Yilun Du and Igor Mordatch : https://arxiv.org/abs/1903.08689

#EnergyBasedModels #MachineLearning #GenerativeModels

OpenAI’s GPT-2: A Simple Guide to Build the World’s Most Advanced Text Generator in Python
https://www.analyticsvidhya.com/blog/2019/07/openai-gpt2-text-generator-python/

Quantum Computers, Neural Computers, and the future of 0s and 1s.
https://medium.com/@normandipalo/quantum-computers-neural-computers-and-the-future-of-0s-and-1s-ac790ca1f5e4