Hi, don't know where else to ask but I just don't know what else I could try out with my code. I'm trying to reimplement CycleGAN in a Jupyter notbook and (for me) the code looks good, but somehow my generators just learn to map an input to itself (so what I put into it comes out at the other end). What's odd is that the GAN loss is going up, which is probably why the generators don't learn anything meaningful other than the identity mapping. I also got the feeling that my discriminators just learn to distinguish fake from real images, but nothing about horses or zebras. I would be so happy if somebody could give me a hint.

In order to be able to sample from p(x) all generative models attempt to learn a function from a known prior distribution p(z) to the natural distribution p(x). I don't think this is true. Some generative models are capable of sampling the learned p(x) directly, like autoregressive models which for example might model the joint distribution over all pixels in an image by using the probability product rule (e.g. Many common language models do the same over words or characters. Been meaning to read more about flow-based generative modeling.

I have been using sci-kit optimize to do hyperparameter search (using gp_minimize specifically) for a neural network. I am working on a binary classification problem with a significant class imbalance. I have been using a batch size of 10, but just came across a tweet and notebook by Francois Chollet where he recommended using a high batch size in class imbalance problems in order so that each batch contains at least a few positive examples. My question is can I just take the networks with the best network architectures I found via my hyperparameter search where I used a batch size of 32, but just retrain them using the same hyperparameters but using a higher batch size? Or, would batch size have a significant effect on hyperparameter optimization, and I would be better off just redoing hyperparameter optimization but this time with a larger batch size?

I know that a reinforcement learning framework can become superhumanly good at a given task over time, but what if I need it to be good at multiple tasks, given that all of them follow similar kind of steps.

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Hey! I'm new to this community and also I'm working on a project that might have a good scenario to implement text recognition in order to output images for them. Please, if anyone knows where I could start or which tools I could try it would be great!

Abstract: Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilization of simulated environments and 3D LiDAR sensors for this task started to get some popularity. With simulated sensors and environments, the process for obtaining an annotated synthetic point cloud data became much easier. However, the generated synthetic point cloud data are still missing the artifacts usually exist in point cloud data from real 3D LiDAR sensors. As a result, the performance of the trained models on this data for perception tasks when tested on real point cloud data is degraded due to the domain shift between simulated and real environments.

Recently, Graph Neural Networks have gained increasing attention from the Machine Learning researchers and the community. With its strong expressiveness, they are likely to be the next game-changing Neural Networks. PyTorch Geometric is one of the fastest Graph Neural Networks frameworks in the world. This article about the basic usage of PyTorch Geometric and how to use it on real-world data.

Business managers currently spend 54% of an average work day on administration, according to an Accenture survey published in the Harvard Business Review. The research surveyed 1,770 managers from 14 countries and interviewed 37 executives in charge of digital transformation at their organisations. It found that after administrative chores, only 7% of time was left to develop people and engage with stakeholders, with 10% of time spent on strategy and innovation, and 30% on problem solving. If you ask me, this sounds like my dream role! Just take The Associated Press, for example, which expanded quarterly earnings by using AI to increase reporting from 300 stories to 4,400.

Abstract: Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS).