476 karma · joined October 2, 2018
The volume rendering engine I have been working on uses a histogram for the value distribution, and on top of it, one can draw lines that indicate the opacity. Additionally, one can set colors to the control points, which are then linearly interpolated for the given ranges.
If you solve a problem that had been around for a while and LLMs offer a new way of approaching it, then it can definitely become a paper.
Of cause one has to verify in sophisticated experiments, that this approach is stable.
Typically, you can take a pre-trained model and retrain it on your new dataset by only changing the weights of the last layer(s).
Some loss functions even measures the difference between the high-level features of two images, typically extracted from a pre-trained CNN (Perceptual Loss).
[1]Matt Zeiler did an amazing work on these findings 10 years ago (https://arxiv.org/abs/1311.2901).
If you select a framework and work in the same scope as the framework intends to, then you can onboard new engineers much more easily by telling them: We are working as the framework defines, look into the documentation and you will understand the basic architecture of our software.
Of cause, devs can maintain their own documentation, but I guess this usually is something that is not done very well.
My personal experience: As soon as a company started their software without a framework, it became a huge mess. While onboarding, I observed very bad architectural decisions and even very severe security issues.
I'd say, only go with no framework, if you are experienced enough to technically create an own (good) framework. From my observations, going without a framework was usually a decision done by young engineers.
20% is huge and I am curios if there will ever be some comparable "official" numbers to that.
If your new gig is e.g. a random e-commerce software that contributes to the laziness of people or their consumerism, maybe you are the type of person that just don't care for the product, therefore won't put any effort in learning things for that?
To me I don't care if a certain stock is correlated by something, I would more like to know which stocks do have correlations or if there are correlations with a time lag
Therefore I would be carful when evaluating this study when a lot of the participants of this study came from here.
What if a group of trusted organizations run a blockchain together?