I just returned from my first ICLR in New Orleans — some notes from the conference available here.

I hope folks find them useful! Happy to discuss topics from the conference as well.

Hello,

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How do you think what is the best method to compare two GPT-2 (345M and 345M fined-tuned) models? How to choose which one is better?

Hello,

I have a report that is generated by the system, the report is in Text format, it is fairly structured, however certain sections in the report sometimes they can expand or shrink, sample of the report is attached .

I would like to convert this report into a CSV or Excel format, so:

1. can pattern recognition help me ? how ?
2. is there a library part of a programming language that i can use ?

sample

Hi all,

This is a question that isn’t really touched upon in most deep learning research, so thought I would reach out to the community for advice.

With neural networks, it is sensible practice to run the same experiment multiple times with different seeds, reporting the mean and standard deviation. This helps factor out the effects that random initialization has on the model.

However, how do you then perform significance testing on the results? For example, you have system A and system B, you run each of them with 10 different seeds over a dataset of size N, calculate a metric for each run (e.g., accuracy/F-score) and report the mean and std over different runs. You now want to determine whether system A is significantly better than system B or not.

Picking a single seed seems random and arbitrary. Using all of them makes the significance test think the test dataset is much bigger than it actually is, which also doesn’t sound right. Aligning the outputs only from the same seeds seems random as well, as all the seeds are supposed to be equivalent.

Do you have any suggestions on how to handle this? Or have you seen any papers attempting to solve this problem? Thanks!

Feel free to use it as a template for TF2.0 training codes. I’ll add more models and features when I have the time. 🙂

Hi, I’m Myeongjun Kim. My major is computer vision research using deep learning. This is the fourth paper implementation. There are many things to learn while implementing paper. Thank you for your continued interest. Feedback is always welcome. I are currently working on CIFAR-100, and I will be working on IMAGENET soon. However, please understand that it takes time because of lack of GPU.
Thank you for reading the long story.
[Github URL]: https://github.com/leaderj1001/MobileNetV3-Pytorch

I just put online a preprint of the “Importance Weighted Hierarchical Variational Inference” paper.

The paper proposes a novel and efficient multisample variational upper bound on log q(z|x) in case of hierarchical proposal q(z|x). This way one can use Neural Samplers (like VAE) as expressive proposal distributions, allowing us to learn more expressive models p(x). This is enabled by a novel multisample variational upper bound on the marginal log-density, which generalizes and bridges several prior results.

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Paper | Talk | Blogpost

Hello,

I want to get involved with whichever task of classification (sentiment analysis, hate speech, etc.) regarding text data, by using deep learning for my bachelor thesis.

I am confused about what should be the aim of the dissertation. I mean, I am not capable to come with something ground breaking or fancy (eg. A new complicated architecture). Beyond building some architectures from papers and testing data on them, what can I do in order to make the thesis more interesting?

Something I have thought of, is to create a pipeline that will retrieve tweets from the Twitter API from different locations and then pass them to a trained model in order to perform some kind of classification and then create a visualization of the world map regarding the topic (eg. brexit-preferences of each country). However, this doesn’t make a lot of sense since that procedure could be done more naively by counting specific hashtags on a geographical location. Also, how the dataset could be created and labeled?

Can you help me reconstruct my idea or guide me to a better direction?

I’d like to read some papers and maybe play with an open implementation of an audio classifier, in order to take advantage of this classifier in the construction of a GAN.

Is anyone aware of public research done on full body image generation?Computer generated fashion models by DataGrid