ISSCC2018 - 50 Years of Computer Architecture:From Mainframe CPUs to Neural-Network TPUs


https://www.youtube.com/watch?v=NZS2TtWcutc

Theorizing from Data by Peter Norvig (Video Lecture)

https://catonmat.net/theorizing-from-data-by-peter-norvig-video-lecture

Deep Learning: Alchemy or Science?

Topic: Reproducible, Reusable, and Robust Reinforcement Learning


https://www.youtube.com/watch?v=wVkViYY_fwA

TSNE-CUDA

GPU Accelerated t-SNE for CUDA with Python bindings

GitHub, by Canny Lab: https://github.com/CannyLab/tsne-cuda

Make music with GANs!

GANSynth is a new method for fast generation of high-fidelity audio.

🎵 Examples: https://goo.gl/magenta/gansynth-examples
⏯ Colab: https://goo.gl/magenta/gansynth-demo
📝 Paper: https://goo.gl/magenta/gansynth-paper
💻 Code: https://goo.gl/magenta/gansynth-code
⌨️ Blog: https://magenta.tensorflow.org/gansynth

Diagnosing Bottlenecks in Deep Q-learning Algorithms

Fu et al.: https://arxiv.org/abs/1902.10250

#artificialintelligence #deeplearning #reinforcementlearning

How to train Keras model x20 times faster with TPU for free

Blog by Chengwei Zhang: https://medium.com/swlh/how-to-train-keras-model-x20-times-faster-with-tpu-for-free-cac6cf5089cb

Coloring Big Graphs with AlphaGoZero

https://arxiv.org/abs/1902.10162

How do Mixture Density RNNs Predict the Future?

Ellefsen et al.: https://arxiv.org/abs/1901.07859

#MachineLearning #DeepLearning #ArtificialIntelligence

Integrating Domain-Knowledge into Deep Learning

By Russ Salakhutdinov: https://www.cs.cmu.edu/~rsalakhu/NY_2019_v3.pdf

#artificialintelligence #deeplearning #machinelearning

A birds-eye view of optimization algorithms

By Fabian Pedregosa: https://fa.bianp.net/teaching/2018/eecs227at/

A Closed-form Solution to Photorealistic Image Stylization

Li et al.: https://arxiv.org/abs/1802.06474

Code: https://github.com/NVIDIA/FastPhotoStyle

#artificialintelligence #deeplearning #machinelearning

Deep Learning for Science School

July 15 - 19th, 2019

Lawrence Berkeley National Laboratory, Berkeley, CA

Hosted by Computing Sciences at Berkeley Lab: https://dl4sci-school.lbl.gov/

H / T : Mr Prabhat

#artificialintelligence #deeplearning #sciences

Neural MMO — A Massively Multiagent Game Environment

By OpenAI: https://blog.openai.com/neural-mmo/

- Code: https://github.com/openai/neural-mmo
- 3D Client: https://github.com/jsuarez5341/neural-mmo-client

#artificialintelligence #deeplearning #multiagent #reinforcementlearning

Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure

https://www.aies-conference.com/wp-content/papers/main/AIES-19_paper_220.pdf

#artificialintelligence #deeplearning #machinelearning

AI & Architecture
An Experimental Perspective

By Stanislas Chaillou, Harvard Graduate School of Design:

https://towardsdatascience.com/ai-architecture-f9d78c6958e0

#artificialintelligence #architecture #design #deeplearning #technology

Launching TensorFlow Lite for Microcontrollers

https://petewarden.com/2019/03/07/launching-tensorflow-lite-for-microcontrollers/


#artificialintelligence #deeplearning #microcontrollers #tensorflow #tensorflow20

Viewing Matrices & Probability as Graphs

A nice fact I like: Every matrix corresponds to a graph, and so familiar things (e.g. matrix multiplication) have nice pictures! Another nice fact: joint probability distributions *also* correspond to graphs. They have telling pictures, too. New blog post

Blog by Math3ma: https://www.math3ma.com/blog/matrices-probability-graphs

#graphs #matrices #probability

Machine Learning Holographic Mapping by Neural Network Renormalization Group

Hu et al.: https://arxiv.org/abs/1903.00804

#ArtificialIntelligence #NeuralNetworks #MachineLearning #StatisticalMechanics #Physics

TensorFlow Dev Summit 2019

https://www.youtube.com/playlist

A Few Unusual Autoencoders


https://colinraffel.com/talks/vector2018few.pdf