​​SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

Abstract: CNN typically encodes an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue that encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that are learned on an object detection task by Neural Architecture Search. SpineNet achieves the SOTA performance of a one-stage object detector on COCO with 60% less computation and outperforms ResNet-FPN counterparts by 6% AP. SpineNet architecture can transfer to classification tasks, achieving 6% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset.

So, by Google's beloved method of creating a new SOTA, there is a new one! They just permute ResNet layers by NAS with adding resample cross-scale connections for correct connection scales output between layers. It seems that no need FPN cause the whole backbone is FPN. They train from scratch on RetinaNet just replace ResNet backbone with SpineNet and get SOTA. On two-stage detectors, there is the same result by replacing the backbone with SpineNet. If you want just classify something with that backbone it is performed very well too. So new architecture for any application!
Good job.

paper: https://arxiv.org/abs/1912.05027
code: Very wanted, but not release yet

#CV #ObjectDetection #GoogleResearch #NAS #SOTA

ODS breakfast in Paris! 🇫🇷 As an exception, because of the manifestations, this Saturday, we meet at 9:45 at Malongo Café, 50 Rue Saint-André des Arts. We can have lunch after that as well. See you tomorrow.

​​New tutorial on QA task

The T5 team competed against T5 in a "pub quiz" on (context-free) questions from the TriviaQA/NQ validation sets.
Result: team got 20% right; T5 got 35%.
Colab link: https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb
Source: twitter

#NLU #NLP #Colab #Tutorial #QA

​​Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One


Classifiers are secretly energy-based models! Every softmax giving p(c|x) has an unused degree of freedom, which we use to compute the input density p(x). This makes classifiers into #generative models without changing the architecture.

The authors did math tricks to the joint Energy-Based Model (EBM) to a usual #classifier with #softmax. So it turns that in this usual classifier hiding the EBM.

Our key observation in this work is that one can slightly re-interpret the logits obtained from fθ to define p(x, y) and p(x) as well. Without changing fθ, one can re-use the logits to define an energy-based model of the joint distribution of data point x and labels y. The normalizing constant cancels out, yielding the standard Softmax parameterization. Thus, we have found a generative model hidden within every standard discriminative model!

In their result, they show that approach gives more adversarial robustness and for generated images maximize class confidence than its classified. It also gives more unify the density of confidence classes of all data. And of course, it can generate pictures. Interesting result by math trick!


paper: http://arxiv.org/abs/1912.03263
tweet: http://twitter.com/DavidDuvenaud/status/1204143678865866752
github: https://github.com/wgrathwohl/JEM

Story of Parisian ODS Breakfasts (and tips for setting up one at your city)

ODS breakfasts are informal regular meetings of data science enthusiasts to discuss professional themes as long with personal ones. It is like coffee break after a conference but without any conference.

Link: https://medium.com/@isprokin/ods-breakfast-is-coming-to-your-city-paris-and-562b1244febd

P.S. please press the clap button in the bottom of medium article to give the article 50 claps, to help promoting data breakfasts.

#NLP #News (by Sebastian Ruder):
* 2020 NLP wish lists
* #HuggingFace + #fastai
* #NeurIPS 2019
* #GPT2 things
* #ML Interviews

blog post: http://newsletter.ruder.io/archive/211277

ODS breakfast in Paris! ☕️ 🇫🇷 See you this Saturday at 10:30 (many people come around 11:00) at Malongo Café, 50 Rue Saint-André des Arts.

Clear explanation of how attention works

Link: https://distill.pub/2016/augmented-rnns/

#NLU #NLP #attention

​​BETO: Spanish BERT

#BETO trained on a big Spanish corpus (3B number of tokens).
Similar to a BERT-Base & was trained with the Whole Word Masking technique.
Weight available for tensorflow & pytorch (cased & uncased versions)

blog post: https://medium.com/dair-ai/beto-spanish-bert-420e4860d2c6
github: https://github.com/dccuchile/beto

​​Driverless DeLorean drifting

#Stanford researchers taught autonomous car driving AI to drift to handle hazardous conditions better.

Link: https://news.stanford.edu/2019/12/20/autonomous-delorean-drives-sideways-move-forward/

#Autonomous #selfdriving #RL #CV #DL #DeLorean

Becoming an Independent Researcher and getting published in ICLR with spotlight

* It is possible to get published as an independent researcher, but it is really HARD.
* Now you need a top tier publication (ACL/EMNLP/CVPR/ICCV/ICLR/NeurIPS or ICML paper) to get accepted into PhD program.
* Mind acceptance rate of 20% and keep on grinding.

Link: https://medium.com/@andreas_madsen/becoming-an-independent-researcher-and-getting-published-in-iclr-with-spotlight-c93ef0b39b8b

#Academia #PhD #conference #learning

​​top podcast episodes from 2k19 by @lexfridman:
⚫️ on nature:
Glenn Villeneuve on @joerogan #1395 | link
⚫️ on perception:
Donald Hoffman on @SamHarrisOrg's Making Sense #178 | link
⚫️ on physics:
Garrett Lisi on @EricRWeinstein's The Portal #15 | link
⚫️ on consciousness:
Philip Goff on @seanmcarroll's Mindscape #71 | link

Happy new year 🎆
Thank you for being awesome 👏

​​SberQuAD – Russian Reading Comprehension Dataset: Description and Analysis

#SberQuAD – a large scale analog of #Stanford #SQuAD in the Russian language – is a valuable resource that has not been properly presented to the scientific community.

SberQuAD creators generally followed a procedure described by the SQuAD authors, which resulted in the similarly high lexical overlap between questions and sentences with answers.

paper: https://arxiv.org/abs/1912.09723
link to SDSJ Task B dataset: http://files.deeppavlov.ai/datasets/sber_squad-v1.1.tar.gz

ODS breakfast in Paris! ☕️ 🇫🇷 See you this Saturday (tomorrow) at 10:30 (some people come around 11:00) at Malongo Café, 50 Rue Saint-André des Arts. We are expecting from 3 to 7 people.

​​Uber AI Plug and Play Language Model (PPLM)

PPLM allows a user to flexibly plug in one or more simple attribute models representing the desired control objective into a large, unconditional language modeling (LM). The method has the key property that it uses the LM as is – no training or fine-tuning is required – which enables researchers to leverage best-in-class LMs even if they don't have the extensive hardware required to train them.

PPLM lets users combine small attribute models with an LM to steer its generation. Attribute models can be 100k times smaller than the LM and still be effective in steering it

PPLM algorithm entails three simple steps to generate a sample:
* given a partially generated sentence, compute log(p(x)) and log(p(a|x)) and the gradients of each with respect to the hidden representation of the underlying language model. These quantities are both available using an efficient forward and backward pass of both models;
* use the gradients to move the hidden representation of the language model a small step in the direction of increasing log(p(a|x)) and increasing log(p(x));
* sample the next word

more at paper: https://arxiv.org/abs/1912.02164

blogpost: https://eng.uber.com/pplm/
code: https://github.com/uber-research/PPLM
online demo: https://transformer.huggingface.co/model/pplm

#nlp #lm #languagemodeling #uber #pplm

​​10 ML & NLP Research Highlights of 2019
by Sebastian Ruder @ huggingface

The full list of highlights:
* Universal unsupervised pretraining
* Lottery tickets
* The Neural Tangent Kernel
* Unsupervised multilingual learning
* More robust benchmarks
* ML and NLP for science
* Fixing decoding errors in NLG
* Augmenting pretrained models
* Efficient and long-range Transformers
* More reliable analysis methods

blogpost: https://ruder.io/research-highlights-2019/

📚Guest post on great example of book abandonment at GoodReads

An excellent new article from Gwern on analyzing abandoned (hard to finish, hard to read) books on Goodreads. This write up includes step by step instructions with source code, even the way he parsed the data from the website without an API.

It’s a shame analysis like this does not come from an online book subscription service like Bookmate or MyBook. They have vastly superior datasets and many able data scientists. I am quite sure amazon kindle team does prepare internal reports like that for some evil business purposes, but that’s a whole different story.

During my time at video game database company RAWG.io we’ve compiled ‘most abandoned’ and ‘most addictive’ reports for video games.

Do you make a popular service with valuable user behavior data? Funny data analysis reports are a good way to get some attention to your product. Take a lead from Pornhub, they are great at publicizing their data.

Link: https://www.gwern.net/GoodReads
Pornhub Insights: https://www.pornhub.com/insights/

—
This is a guest post by Samat Galimov, who writes about technology, programming and management in Russian on @ctodaily.


#DataAnalysis #GoodReads #statistics #greatstats #talkingnumbers

Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach

ArXiV: https://arxiv.org/abs/1912.11027

#Cancer #BreastCancer #DL #CV #biolearning

​​:true-story: