TL;DR: We propose a new training paradigm called Guided Complement Entropy (GCE) that is capable of achieving “adversarial defense for free,” which involves no additional procedures in the process of improving adversarial robustness. In addition to maximizing model probabilities on the ground-truth class like cross-entropy, we neutralize its probabilities on the incorrect classes along with a “guided” term to balance between these two terms. We show in the experiments that our method achieves better model robustness with even better performance compared to the commonly used cross-entropy training objective.

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Full paper: https://arxiv.org/abs/1903.09799

Github: https://github.com/henry8527/GCE

https://pytorch.devpost.com/

I had the pleasure of attending their in person hackathon at Menlo Park yesterday. If you want some inspiration for potential projects, checkout their submissions page here, they were really good.

https://pytorchmpk.devpost.com/submissions

Pytorch rolled a bunch of new features out a few days ago. They seem to be really stepping up in response to TF 2.0.

If you’re looking for teammates, signup on the page, then you can look at other profiles of those looking for teammates

https://pytorch.devpost.com/participants

Assuming we want to learn k tasks jointly, and the data for all tasks are available. We may either train a model with parallel multi-task learning (eg. each batch is a mixture of samples from the k tasks), or present tasks sequentially (eg. switch to a different task once every 5k time steps). The latter is kind of like continual learning, except that the set of tasks is fixed and there won’t be new ones. Which training paradigm yields better results? Any paper that gives theoretical analysis or makes empirical comparisons?

Are there math dataset out there with step-by-step solutions?

Something like Deepmind maths dataset, but includes step-by-step workings like Mathematica or Wolfram Alpha.

Also are there papers related to fully differentiable models of Mathematica/Wolfram Alpha? Deepmind’s paper seems to skip straight to answers without showing how it get through intermediate steps.

Hello everyone,

Where can I find an algorithm that plots the steering wheel data like on the figure four of this paper?

Any help is much appreciated. I can’t seem to find anything online.

Thank you in advance.

A victim of billionaire Jeffrey Epstein testified that she was forced to have sex with MIT professor Marvin Minsky, as revealed in a newly unsealed deposition. Epstein was registered as a sex offender in 2008 as part of a controversial plea deal. More recently, he was arrested on charges of sex trafficking amid a flood of new allegations.

Minsky, who died in 2016, was known as an associate of Epstein, but this is the first direct accusation implicating the AI pioneer in Epstein’s broader sex trafficking network. The deposition also names Prince Andrew of Britain and former New Mexico governor Bill Richardson, among others.

The accusation against Minsky was made by Virginia Giuffre, who was deposed in May 2016 as part of a broader defamation suit between her and an Epstein associate named Ghislaine Maxwell. In the deposition, Giuffre says she was directed to have sex with Minsky when he visited Epstein’s compound in the US Virgin Islands.

As part of the defamation suit, Maxwell’s counsel denied the allegations, calling them “salacious and improper.” Representatives for Giuffre and Maxwell did not immediately respond to a request for comment.

A separate witness lent credence to Giuffre’s account, testifying that she and Minsky had taken a private plane from Teterboro to Santa Fe and Palm Beach in March 2001. Epstein, Maxwell, chef Adam Perry Lang, and shipping heir Henry Jarecki were also passengers on the flight, according to the deposition. At the time of the flight, Giuffre was 17; Minsky was 73.

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A pivotal member of MIT’s Artificial Intelligence Lab, Marvin Minsky pioneered the first generation of self-training algorithms, establishing the concept of artificial neural networks in his 1969 book Perceptrons. He also developed the first head-mounted display, a precursor to modern VR and augmented reality systems.

Minsky was one of a number of prominent scientists with ties to Jeffrey Epstein, who often called himself a “science philanthropist” and donated to research projects and academic institutions. Many of those scientists were affiliated with Harvard, including physicist Lawrence Krauss, geneticist George Church, and cognitive psychologist Steven Pinker. Minsky’s affiliation with Epstein went particularly deep, including organizing a two-day symposium on artificial intelligence at Epstein’s private island in 2002, as reported by Slate. In 2012, the Jeffrey Epstein Foundation issued a press release touting another conference organized by Minsky on the island in December 2011.

That private island is alleged to have been the site of an immense sex trafficking ring. But Epstein associates have argued that those crimes were not apparent to Epstein’s social relations, despite the presence of young women at many of his gatherings.

“These people were seen not only by me,” Alan Dershowitz argued in a 2015 deposition. “They were seen by Larry Summers, they were seen by [George] Church, they were seen by Marvin Minsky, they were seen by some of the most eminent academics and scholars in the world.”

“There was no hint or suggestion of anything sexual or improper in the presence of these people,” Dershowitz continued.

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https://www.theverge.com/2019/8/9/20798900/marvin-minsky-jeffrey-epstein-sex-trafficking-island-court-records-unsealed

It turns out my GPU has only around 1GB of storage and even comparatively little models create OOM errors.

Are there ways around that? Can maybe the regular RAM act to hold the data?

Or is 1GB just too little room to work with and I need a new card?

Hello I am implementing deep Q learning for a 2 player board game. After every move it is the turn of the other player. I want to calculate max(Q’,a’) for getting the max Q value for the next state but my problem is that the next state represents the quality for the opponent player. So max Q is the max quality value for my opponent(But I want to maximize MY win chances) How do I proceed? Should I calculate all states where it is again my turn?(2 depth)

A step-by-step tutorial on how to implement and adapt recurrent language model to Wikipedia text.

A pre-trained BERT, XLNET is publicly available ! But, for NLP beginners, like me, It could be hard to use/adapt after full understanding. For them, I covered whole, end-to-end implementation process for language modeling, using recurrent network, we already know. + do not use torchtext !

I hope that this repo can be a good solution for people who want to have their own language model 🙂

https://github.com/lyeoni/pretraining-for-language-understanding