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Hi,
I am curious what is the math background for the logsunexp and softplus case in FLAG.objective.

Thanks.

Hi,

Thanks for making your code available!

I'm having problem evaluating log likelihood on the MNIST dataset. When I run

python ais.py --exp exp-name --dataset mnist  --resume_iter 15000 --pdist 5000

the output is like

Positive sample probability  -0.00042194081 0.0062082466
Total values of lower value based off forward sampling -0.62608874 0.0075839674
Total values of upper value based off backward sampling -0.6260886 0.007481599

It seems that the code should output the test log likelihood, but the magnitude of the output doesn't match (the numbers in Fig 22 of your paper are around 10^2 to 10^3). This is strange since I've checked visually that the model is generating plausible samples. So is there anything wrong in my experiment configuration, or should I do some scaling to obtain the log likelihood figures in the table?

The model is obtained with

 --dataset=mnist --num_steps=60 --batch_size=128 --step_lr=10.0 --proj_norm=0.01 --zero_kl --replay_batch --epoch_num 50

In the paper the langevin dynamics scales the gradient as 0.5 * λ + w sampled from normal distribution with 0 mean and λ standard deviation. However, in the code (e.x. imagenet_demo.py), gradient is multiplied by step_lr which is 180 by default and the noise is generated with 0.005 standard deviation. Am I missing something or the paper shows equation that is justified by theory but in practice it is better to scale energy gradient different than noise?

I've found that the repo has already implemented self-attention. Have the authors tried using self-attention during training ebm and does self-attention improve ebm? Looking forward to your reply. Thanks in advance.

Could you kindly guide me on how to generate samples from the CIFAR10 pre-trained model, similar to the process in imagenet_demo.py? @yilundu

I have attempted to install the packages from requirements.txt but they are seemingly incompatible. Moreover, the baselines requirement is out of sync with the others. After downgrading to CUDA 9.0, and install CUDNN 7.1.4 with Tensorflow 1.12.0 and Torch 0.3.4 it seems that this code still will not run. Is there an updated version that can use the latest packages? Is there a workaround for needing to install the "baselines" directory? Is there anybody who has a docker container for these packages, or a recently used python virtual environment?

In section 4.4 "Out-of-Distribution Generalization" how do you compute the AUROC scores from the EBM model? The score function is un-normalized, as opposed to the likelihood which is between 0 and 1. So how is the AUROC score computed then?

Dear openAI researchers,
Thanks for your code release of ebm. I've been really interested in energy-based models since I read Ignor Mordatch's paper Concept Learning with energy-based models last year. However it is difficult to reproduce the experiments since there's no code or dataset release for that work. Is openAI still working on that topic and can you release a single case implementation of that work? I've sent some e-mails but there's no reply. Looking forward to your help. Thank you.

The current url leads to a 404. Could you please fix it?

The following link returns a 404 error.

https://www.dropbox.com/s/g1jwagoofhrjwvy/cachedir.tar.gz?dl=0