Cutting Edge Deep Learning – Telegram
Cutting Edge Deep Learning
253 subscribers
193 photos
42 videos
51 files
363 links
📕 Deep learning
📗 Reinforcement learning
📘 Machine learning
📙 Papers - tools - tutorials

🔗 Other Social Media Handles:
https://linktr.ee/cedeeplearning
Download Telegram
Machine Learning Cheat Sheet 2015.pdf
1.9 MB
#Machine_Learning
#Cheat_Sheet

💥Machine learning algorithm cheat sheet, Classical Equations, Diagrams and Tricks
----------
@machinelearning_tuts
@autonomousvehicle
quickumls.pdf
314.2 KB
#a_fast, unsupervised approach
for medical concept extraction

#paper
----------
@machinelearning_tuts
Multi-agent System for Medical Records Processing .pdf
526.1 KB
#Towards a Multi-agent System for Medical Records Processing and
Knowledge Discovery
#paper
----------
@machinelearning_tuts
Top 10 #deeplearning research papers as per this website
https://lnkd.in/dPYayt9

Of course the choice remains biased but we do like these besides a few hundred other papers.

Remember, it is not the popular but the meaningful and industry relevant research that is worth paying attention to.

Here's the list:

1. Universal Language Model Fine-tuning for Text Classification
https://lnkd.in/dhj5SyM

2. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
https://lnkd.in/d44kt3Q

3. Deep Contextualized Word Representations
https://lnkd.in/dkP68Fb

4. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://lnkd.in/dAhYzge

5. Delayed Impact of Fair Machine Learning
https://lnkd.in/dvTvG2s

6. World Models

7. Taskonomy: Disentangling Task Transfer Learning
https://lnkd.in/dYxMjAd

8. Know What You Don’t Know: Unanswerable Questions for SQuAD
https://lnkd.in/d--grME

9. Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://lnkd.in/dY6psf4

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://lnkd.in/dgtnD7n
#machinelearning #research #deeplearning #artificialintelligence

----------
@machinelearning_tuts
Deep Learning in Software Engineering

--Abstract
Recent years, deep learning is increasingly prevalent in the field ofSoftware Engineering (SE). However, many open issues still remain to beinvestigated. How do researchers integrate deep learning into SE problems?Which SE phases are facilitated by deep learning? Do practitioners benefit fromdeep learning? The answers help practitioners and researchers develop practicaldeep learning models for SE tasks. To answer these questions, we conduct abibliography analysis on 98 research papers in SE that use deep learningtechniques. We find that 41 SE tasks in all SE phases have been facilitated bydeep learning integrated solutions. In which, 84.7% papers only use standarddeep learning models and their variants to solve SE problems. Thepracticability becomes a concern in utilizing deep learning techniques. How toimprove the effectiveness, efficiency, understandability, and testability ofdeep learning based solutions may attract more SE researchers in the future.


2018-05-13T06:01:39Z
@machinelearning_tuts
@selfdrivecar
@autonomousvehicle
----------
Link : http://arxiv.org/abs/1805.04825v1