Facebook is open-sourcing QNNPACK, a kernel library that is optimized for mobile AI.
Site: https://code.fb.com/ml-applications/qnnpack/
Github: https://github.com/pytorch/QNNPACK
#dl #mobile #facebook
Site: https://code.fb.com/ml-applications/qnnpack/
Github: https://github.com/pytorch/QNNPACK
#dl #mobile #facebook
Facebook Engineering
QNNPACK: Open source library for optimized mobile deep learning
Facebook open-sources QNNPACK, a high-performance kernel library optimized for mobile AI. QNNPACK speeds up many advanced neural network operations.
An agent which learned to play Mario without rewards. Instead, it was incentivized to avoid "boredom" (that is, getting into states where it can predict what will happen next). Discovered warp levels, how to defeat bosses, etc.
Link: https://blog.openai.com/reinforcement-learning-with-prediction-based-rewards/
#RL #openai
Link: https://blog.openai.com/reinforcement-learning-with-prediction-based-rewards/
#RL #openai
Facebook open sourced Horizon, an end-to-end applied reinforcement learning platform built on #PyTorch 1.0. Horizon uses RL to optimize systems in large-scale production environments and we're excited to make it accessible to anyone using #RL at scale.
https://code.fb.com/ml-applications/horizon/
#facebook
https://code.fb.com/ml-applications/horizon/
Engineering at Meta
Horizon: The first open source reinforcement learning platform for large-scale products and services
An end-to-end platform built on PyTorch 1.0 that is designed to jump start RLβs transition from research papers to production
XNLI dataset published by Facebook AI & NYU.
New dataset have been released recently to promote cross-lingual approaches to natural language understanding (#NLU).
This dataset builds on the commonly used Multi-Genre Natural Language Inference (MultiNLI) corpus, adding 14 languages to that English-only data set, including two low-resource languages: Swahili and Urdu.
Link: https://code.fb.com/ai-research/xlni/
#NLP #facebook
New dataset have been released recently to promote cross-lingual approaches to natural language understanding (#NLU).
This dataset builds on the commonly used Multi-Genre Natural Language Inference (MultiNLI) corpus, adding 14 languages to that English-only data set, including two low-resource languages: Swahili and Urdu.
Link: https://code.fb.com/ai-research/xlni/
#NLP #facebook
Facebook Engineering
Facebook, NYU expand available languages for natural language understanding systems
The XLNI dataset, a collaboration between Facebook and NYU, builds on the MultiNLI corpus, adding 14 languages including low-resource languages.
#DeepMind βs library for deep learning on graphs.
ArXiV: https://arxiv.org/abs/1806.01261
Github: https://github.com/deepmind/graph_nets
ArXiV: https://arxiv.org/abs/1806.01261
Github: https://github.com/deepmind/graph_nets
GitHub
GitHub - google-deepmind/graph_nets: Build Graph Nets in Tensorflow
Build Graph Nets in Tensorflow. Contribute to google-deepmind/graph_nets development by creating an account on GitHub.
Reversible RNNs
Paper about how to reduce memory costs of GRU and LSTM networks by 10-15x without loss in performance. Also 5-10x for attention-based architectures. New paper with Matt MacKay, Paul Vicol, and Jimmy Ba, to appear at NIPS.
Link: https://arxiv.org/abs/1810.10999
#dl #RNN #NIPS2018
Paper about how to reduce memory costs of GRU and LSTM networks by 10-15x without loss in performance. Also 5-10x for attention-based architectures. New paper with Matt MacKay, Paul Vicol, and Jimmy Ba, to appear at NIPS.
Link: https://arxiv.org/abs/1810.10999
#dl #RNN #NIPS2018
Faster R-CNN and Mask R-CNN in #PyTorch 1.0
Another release from #Facebook.
Mask R-CNN Benchmark: a fast and modular implementation for Faster R-CNN and Mask R-CNN written entirely in @PyTorch 1.0. It brings up to 30% speedup compared to mmdetection during training.
Webcam demo and ipynb file are available.
Github: https://github.com/facebookresearch/maskrcnn-benchmark
#CNN #CV #segmentation #detection
Another release from #Facebook.
Mask R-CNN Benchmark: a fast and modular implementation for Faster R-CNN and Mask R-CNN written entirely in @PyTorch 1.0. It brings up to 30% speedup compared to mmdetection during training.
Webcam demo and ipynb file are available.
Github: https://github.com/facebookresearch/maskrcnn-benchmark
#CNN #CV #segmentation #detection
GitHub
GitHub - facebookresearch/maskrcnn-benchmark: Fast, modular reference implementation of Instance Segmentation and Object Detectionβ¦
Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch. - facebookresearch/maskrcnn-benchmark
A set of best where-to-start-with-python resources.
This is the collection of beginners resources from a tweet by fast.ai cofounder, covering any resource which helped people to learn python from scratch.
https://telegra.ph/A-collection-of-where-to-start-python-resources-11-05
#beginner #novice #CS #python #tutorial
This is the collection of beginners resources from a tweet by fast.ai cofounder, covering any resource which helped people to learn python from scratch.
https://telegra.ph/A-collection-of-where-to-start-python-resources-11-05
#beginner #novice #CS #python #tutorial
Telegraph
A collection of where-to-start python resources
CodeAcademy (learn through practice) https://jeffknupp.com Python Numpy Tutorial as ipynb file Learn Python The Hard Way PyVideo Youtube playlist MIT open cource Rosalind (learn through practice platform) Coursera Python for everybody specialisation Pythonβ¦
Making floating point math highly efficient for AI hardware.
Fresh FB article on accelerating ML.
https://code.fb.com/ai-research/floating-point-math/
#facebook
Fresh FB article on accelerating ML.
https://code.fb.com/ai-research/floating-point-math/
Engineering at Meta
Making floating point math highly efficient for AI hardware
In recent years, compute-intensive artificial intelligence tasks have prompted creation of a wide variety of custom hardware to run these powerful new systems efficiently. Deep learning models, sucβ¦
Analyzing Experiment Outcomes: Beyond Average Treatment Effects
Cool piece from Uber's engineering department about why you can't just use the average customer experience to see if product changes are worth it. You have to consider the DISTRIBUTIONAL changes of the customer experience.
Link: https://eng.uber.com/analyzing-experiment-outcomes/
#statistics #uber #abtest
Cool piece from Uber's engineering department about why you can't just use the average customer experience to see if product changes are worth it. You have to consider the DISTRIBUTIONAL changes of the customer experience.
Link: https://eng.uber.com/analyzing-experiment-outcomes/
#statistics #uber #abtest
ββTL-GAN: transparent latent-space GAN
GANs to generate photo-realistic faces with some control over characteristics.
Demo: https://www.kaggle.com/summitkwan/tl-gan-demo
Medium Post: https://blog.insightdatascience.com/generating-custom-photo-realistic-faces-using-ai-d170b1b59255
Github: https://github.com/SummitKwan/transparent_latent_gan#1-instructions-on-the-online-demo
#GAN #cv
GANs to generate photo-realistic faces with some control over characteristics.
Demo: https://www.kaggle.com/summitkwan/tl-gan-demo
Medium Post: https://blog.insightdatascience.com/generating-custom-photo-realistic-faces-using-ai-d170b1b59255
Github: https://github.com/SummitKwan/transparent_latent_gan#1-instructions-on-the-online-demo
#GAN #cv
Amazonβs SageMaker Object2Vec, a highly customizable algorithm that can learn embeddings of various types high-dimensional objects.
Link: https://aws.amazon.com/ru/blogs/machine-learning/introduction-to-amazon-sagemaker-object2vec/
#Object2Vec #Amazon #Embeddings
Link: https://aws.amazon.com/ru/blogs/machine-learning/introduction-to-amazon-sagemaker-object2vec/
#Object2Vec #Amazon #Embeddings
Prototypical Clustering Networks for Dermatological Disease Diagnosis
Paper will be presented at the ML4D workshop at #NIPS2018
Link: https://arxiv.org/abs/1811.03066
#nn #bio #medical
Paper will be presented at the ML4D workshop at #NIPS2018
Link: https://arxiv.org/abs/1811.03066
#nn #bio #medical
β€1
Monitor Your PyTorch Models With Five Extra Lines of Code
Ever felt like manually managing your Visdom / TensorBoard server and logs is a pain across experiments, projects and teams?
Weights & Biases provides a simple cloud-based experiment logging and plotting system, with easy integration for PyTorch models.
Link: https://www.wandb.com/blog/monitor-your-pytorch-models-with-five-extra-lines-of-code
#pytorch
Ever felt like manually managing your Visdom / TensorBoard server and logs is a pain across experiments, projects and teams?
Weights & Biases provides a simple cloud-based experiment logging and plotting system, with easy integration for PyTorch models.
Link: https://www.wandb.com/blog/monitor-your-pytorch-models-with-five-extra-lines-of-code
#pytorch
wandb.ai
Monitor Your PyTorch Models With Five Extra Lines of Code on Weights & Biases
by Lukas Biewald β I love PyTorch and I love experiment tracking, here's how to do both!
New paper on Lipschitz neural net architectures. Uses sorting as an activation function, with matrix norm constrained weights. Universal Lipschitz function approx. Enforce adversarial robustness (margin) using hinge loss.
Link: https://arxiv.org/abs/1811.05381
#nn #lipschitz
Link: https://arxiv.org/abs/1811.05381
#nn #lipschitz
ββNeural network 3D visualization framework. Very nice in-depth visualizations.
Now you can actually see how the layers look.
Github: https://github.com/tensorspace-team/tensorspace
LiveDemo (!): https://tensorspace.org/html/playground/vgg16.html
#visualization #nn
Now you can actually see how the layers look.
Github: https://github.com/tensorspace-team/tensorspace
LiveDemo (!): https://tensorspace.org/html/playground/vgg16.html
#visualization #nn
ββReally interesting talk at MLconfSF by Franziska Bell on how #Uber uses NLP for customer experience. Most of what was described are recent advances in their COTA platform.
Link: https://eng.uber.com/cota/
Link: https://eng.uber.com/cota/
ββDeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution
Using GANs to generate MasterFingerPrints that unlock 22-78% phones sensors (dep. on security level of sensor). It doesn't get much more "adversarial" than that.
This work can be potentially used to create fingerprint which can be used to match 22-78% of fingerprints in the wild, creating Skeleton key, fitting any security system, including home alarm or phone lock.
ArXiV: https://arxiv.org/pdf/1705.07386.pdf
#GAN #security #fingerprint
Using GANs to generate MasterFingerPrints that unlock 22-78% phones sensors (dep. on security level of sensor). It doesn't get much more "adversarial" than that.
This work can be potentially used to create fingerprint which can be used to match 22-78% of fingerprints in the wild, creating Skeleton key, fitting any security system, including home alarm or phone lock.
ArXiV: https://arxiv.org/pdf/1705.07386.pdf
#GAN #security #fingerprint