Geometry-Aware Video Object Detection for Static Cameras. https://arxiv.org/abs/1909.03140

Geoffrey Hinton and Yann LeCun, 2018 ACM A.M. Turing Award Lecture

https://www.youtube.com/watch?v=VsnQf7exv5I&feature=youtu.be

Artificial Intelligence Detects Heart Failure From One Heartbeat With 100% Accuracy - Nicholas Fearn
https://www.forbes.com/sites/nicholasfearn/2019/09/12/artificial-intelligence-detects-heart-failure-from-one-heartbeat-with-100-accuracy

https://t.iss.one/ArtificialIntelligenceArticles

Reference : https://arxiv.org/abs/1710.09829 https://t.iss.one/ArtificialIntelligenceArticles

Master’s student position or internship Machine Learning / Deep Learning

Anomaly Detection on High Dimensional Time Series


Ref. 2019-26

Background

Many application domains increasingly require AD, when anomalies carry critical and actionable information. These include: (1) Cyber-security and intrusion detection in Cloud and IT systems, also in government, defense and security agencies; (2) Fraud detection in financial institutions; (3) Manufacturing, IoT, industry and resource exploration; (4) Healthcare; etc.

Project

We shall address the problem of detecting and predicting general anomalies in high-dimension KPI performance metrics, i.e., high dimension and dynamic range multivariate non-stationary time series collected from large Cloud / IT environments. Using Keras / TF etc., we will build an ML-based AD framework for transfer, attention and meta-learning that must remain robust also with reduced/missing and noisy training data. Besides feature engineering – e.g., selection, reduction, compression techniques – explainability will also be necessary for the model prototype.

Requirements

Data science/mining in general
Feature engineering and DL experience with RNN/CNN/xAE in particular
Hands-on experience with deep neural network models in Python, NumPy, Pandas, SciPy etc., applied to deep RNN/CNN/Autoencoders
Motivation to learn real-life time series and experiment with DL in Keras/TensorFlow/ PyTorch
About the position

The research is to be performed at IBM Research – Zurich Lab, Switzerland.

The expected duration is 3-6 months, starting as soon as possible from June 2019.

Diversity

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent, flexible working arrangements enable both women and men to strike the desired balance between their professional development and their personal lives.

https://ai-jobs.net/job/masters-student-position-or-internship-machine-learning-deep-learning-4/

https://t.iss.one/ArtificialIntelligenceArticles

Google Patents "Generating output sequences from input sequences using neural networks"
https://www.freepatentsonline.com/10402719.html

Open-Unmix for Music Separation

📜Paper: https://joss.theoj.org/papers/571753bc54c5d6dd36382c3d801de41d
🔊Demo: https://open.unmix.app
🔥PyTorch: https://github.com/sigsep/open-unmix-pytorch
🔻NNabla: https://github.com/sigsep/open-unmix-nnabla
🔶TF2: t.b.a.
📓Colab: https://colab.research.google.com/drive/1mijF0zGWxN-KaxTnd0q6hayAlrID5fEQ

A deep learning system for differential diagnosis of skin diseases

Liu et al.

Paper: https://arxiv.org/abs/1909.05382

Blog : https://ai.googleblog.com/2019/09/using-deep-learning-to-inform.html

#deeplearning #machinelearning #healthcare

ICYMI from CVPR 2019: 3D human pose estimation in video with temporal convolutions and semi-supervised training

https://www.profillic.com/paper/arxiv:1811.11742

The authors (Facebook AI researchers) demonstrate that 3D poses in a video can be effectively estimated with a fully convolutional model based on dilated temporal convolutions over 2D keypoints.

Can you classify two class circle data using neural network with only two neurons?

https://arxiv.org/abs/1901.00109

The Power and Limits of Deep Learning" with Yann LeCun

https://videoken.com/embed?videoID=zikdDOzOpxY&fbclid=IwAR3RKhlLVhJBz_uyGHTshJjgqrC5lldP0wwv7Z3eWsDMk9SY9_qltA-IrzI

Bayes' theorem explained with examples and implications for life.

Link: https://www.youtube.com/watch?v=R13BD8qKeTg

An NLP model that writes its own arXiv paper abstract?! Check out this great abstractive neural document summarization work by Element AI researchers Sandeep Subramanian, Raymond Li, Jonathan Pilault and Christopher Pal:

https://arxiv.org/abs/1909.03186

CvxNets: Learnable Convex Decomposition by Geoffrey Hinton
Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz,, Andrea Tagliasacchi : https://arxiv.org/abs/1909.05736
#ArtificialIntelligence #DeepLearning #MachineLearning https://t.iss.one/ArtificialIntelligenceArticles

GE Healthcare wins first FDA clearance for A.I. powered X-ray system

https://www.marktechpost.com/2019/09/12/ge-healthcare-wins-first-fda-clearance-for-a-i-powered-x-ray-system/

Edge-Informed Single Image Super-Resolution. https://arxiv.org/abs/1909.05305

Deep Reinforcement Learning Algorithm for Dynamic Pricing of Express Lanes with Multiple... https://arxiv.org/abs/1909.04760

Machine Learning Software Senior or Staff Engineer
https://ai-jobs.net/job/machine-learning-software-senior-or-staff-engineer/

What Kind of Language Is Hard to Language-Model?
Mielke et al.: https://arxiv.org/abs/1906.04726
#ArtificialIntelligence #MachineLearning #NLP

Air Force releases 2019 Artificial Intelligence Strategy
Secretary of the Air Force Public Affairs : https://www.af.mil/Portals/1/documents/5/USAF-AI-Annex-to-DoD-AI-Strategy.pdf
#ArtificialIntelligence #Defense #Strategy

Memorize-Generalize: An online algorithm for learning higher-order sequential structure with cloned Hidden Markov Models
Rikhye et al.: https://www.biorxiv.org/content/10.1101/764456v1
#Biology #Neuroscience #HiddenMarkovModels