I came across the paper of Wachter, Mittelstadt, et al, who propose Counterfactual Explanations as remedy against the opacity of ML decision-making (see e.g. https://arxiv.org/abs/1711.00399 or https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3278331).

I am a lawyer myself and cannot assess the technical feasibility of this approach. How would you, Ml experts, evaluate their idea? I am really looking forward to your thoughts and insights.

(If this was already subject to discussion elsewhere please kindly direct me to the post).

what would be a good apporach for deploying stylegan encoder https://github.com/Puzer/stylegan-encoder to mobile devices? Save the model as a pb or tflite?

We have recently developed an Adjoint based Neural ODE (ANODE) which computes unconditionally accurate gradients for Neural ODEs. This is very important as the approach presented in arxiv:1806.07366 is numerically unstable and may result in divergent training (in several cases we observed >20% accuracy degradation because of this)

Link to Pytorch code:

https://github.com/amirgholami/anode

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Link to papers:

https://arxiv.org/pdf/1902.10298.pdf

https://arxiv.org/pdf/1906.04596.pdf

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We hope this library would be helpful. Please let us know if you have any feedback and feel free to reach out if there was any questions

Hey all I was wondering if you guys had any recommendations for the type of architecture I should use to estimate the link duration between two vehicles in a VANET.

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What I’m thinking right now is to have a MLP network that takes certain parameters (vehicle speed, lane, number of lanes, distance to intersection, traffic conditions, etc.) and feeds the output of that into an LSTM. The reason I suggest an LSTM is because the link duration, or lifetime, is constantly changing based on the parameters that I previously mentioned. To my knowledge an LSTM is good for temporal data. However, I have a feeling that my intuition may be off here and I was wondering if somebody would care to chime in.

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Thanks!

https://www.pycm.ir

https://github.com/sepandhaghighi/pycm

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• Adjusted F-score (AGF) added #209
• Overlap coefficient (OC) added #212
• Otsuka-Ochiai coefficient (OOC) added #213
• save_stat and save_vector parameters added to save_obj method #210
• Document modified #221
• README.md modified
• Parameters recommendation for imbalance dataset modified
• Minor bug in Compare class fixed
• pycm_help function modified
• Benchmarks color modified #221

Have people settled on anything better than the uniform that’s commonly used? Hopefully something unbiased & minimum variance. I’m finding a few papers 1, 2 and thought to check here before trying to implement any of them.

If it’s relevant, my data has 2e14 points and I can afford to do 150M of them.

Hey guys!

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Finished my experimental project where I tried to come up with algorithm to find similar images using pre-trained ResNet50 model for image features generation and cosine similarity as distance metric. At this experiment I used DeepFashion image dataset.

Take a look! https://github.com/tomasrasymas/simimg

https://arxiv.org/abs/1906.10306

Proximal policy optimization and trust region policy optimization (PPO and TRPO) with actor and critic parametrized by neural networks achieve significant empirical success in deep reinforcement learning. However, due to nonconvexity, the global convergence of PPO and TRPO remains less understood, which separates theory from practice. In this paper, we prove that a variant of PPO and TRPO equipped with overparametrized neural networks converges to the globally optimal policy at a sublinear rate. The key to our analysis is the global convergence of infinite-dimensional mirror descent under a notion of one-point monotonicity, where the gradient and iterate are instantiated by neural networks. In particular, the desirable representation power and optimization geometry induced by the overparametrization of such neural networks allow them to accurately approximate the infinite-dimensional gradient and iterate.