Paper: https://arxiv.org/abs/1911.03863

Abstract: Self-supervised pre-training of transformer models has shown enormous success in improving performance on a number of downstream tasks. However, fine-tuning on a new task still requires large amounts of task-specific labelled data to achieve good performance. We consider this problem of learning to generalize to new tasks with few examples as a meta-learning problem. While meta-learning has shown tremendous progress in recent years, its application is still limited to simulated problems or problems with limited diversity across tasks. We develop a novel method, LEOPARD, which enables optimization-based meta-learning across tasks with different number of classes, and evaluate existing methods on generalization to diverse NLP classification tasks. LEOPARD is trained with the state-of-the-art transformer architecture and shows strong generalization to tasks not seen at all during training, with as few as 8 examples per label. On 16 NLP datasets, across a diverse task-set such as entity typing, relation extraction, natural language inference, sentiment analysis, and several other text categorization tasks, we show that LEOPARD learns better initial parameters for few-shot learning than self-supervised pre-training or multi-task training, outperforming many strong baselines, for example, increasing F1 from 49% to 72%.

Hello everyone,

We’ve just published the new article of Deep reinforcement Learning course where we study Open AI’s Paper “Exploration by Random Network Distillation

THE ARTICLE: https://medium.com/data-from-the-trenches/curiosity-driven-learning-through-random-network-distillation-488ffd8e5938

The bonus is that we give you a trained model on Montezuma’s Revenge during 21hours with 128 parallel environments in a Tesla K80.

Let me know what you think about this article.

PS: For people who follow me and follow Deep Reinforcement Learning Course I know that I’m totally late on this article (it was supposed to be published some months ago…) to be totally transparent with you, the publication rate is slow because since March I’m a RL research scientist at Dataiku so I have a lot of things to do. But stay tuned I’m currently working very hard on updating everything (especially the PR on github) and things will be announced in the 2 next weeks (and yes it will stay totally free and open source 🎉).

Cheers!

Hey y’all!

The past couple of days have been awesome. `gpt2-client` finally hit 12.5K downloads worldwide and we’re growing faster than ever. I’ve added a Buy me a coffee! link to the README in case any of you would donate to the project. Your continued support motivates me to continue building such nifty tools and your donations mean a lot to me!

Again, if you haven’t checked gpt2-client out, do visit https://github.com/rish-16/gpt2client . I’m now accepting feature requests (some have already been incorporated in!). So, feel free to drop me a message down below or on a Feature Requests template on GitHub.

Cheers!

Paper: https://openreview.net/forum?id=BJeGFs9FsH

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Abstract:

There were many attempts to explain the trade-off between accuracy and adversarial robustness. However, there was no clear understanding of the behaviors of a robust classifier which has human-like robustness.

We argue (1) why we need to consider adversarial robustness against varying magnitudes of perturbations not only focusing on a fixed perturbation threshold, (2) why we need to use different method to generate adversarially perturbed samples that can be used to train a robust classifier and measure the robustness of classifiers and (3) why we need to prioritize adversarial accuracies with different magnitudes.

We introduce Lexicographical Genuine Robustness (LGR) of classifiers that combines the above requirements. We also suggest a candidate oracle classifier called “Optimal Lexicographically Genuinely Robust Classifier (OLGRC)” that prioritizes accuracy on meaningful adversarially perturbed examples generated by smaller magnitude perturbations. The training algorithm for estimating OLGRC requires lexicographical optimization unlike existing adversarial training methods. To apply lexicographical optimization to neural network, we utilize Gradient Episodic Memory (GEM) which was originally developed for continual learning by preventing catastrophic forgetting.

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TL;DR: We try to design and train a classifier whose adversarial robustness is more resemblance to robustness of human.

Is the performance from a laptop with Intel Core i9-9980HK with NVIDIA Quadro RTX 3000 good or would it never be able to compete with a good desktop/tower computer with RTX 2080 Ti?

Either I need a super good laptop or combo of Desktop and laptop. Using TensorFlow.

Suppose I have trained a Cat-Dog classifier, open set handling means it can recognise classes that it has not seen while training. What classes are we talking about here? Different breeds of Dogs/Cats or what?

I’m training a PyTorch model on data stored in Google BigQuery (basically a SQL like database). What’s the best way to fetch data in batches for training a model such that I don’t bottleneck the training process. Querying too many times is slow and the dataset is way too large to fit into memory. Any best practices here or existing tools for this?