Nah, joint research with the military is always 100% evil, regardless of the country of origin.

As a Brazilian, I'd take that tbh. Trump won't win any elections here and Bolsonaro won't win any elections there

Indigenous peoples being murdered by the illegal miners and deforestors that he supported, and 700k covid deaths, a lot of which are direct consequences of his covid denial ("it's just a flu"), refusal to buy vaccines in time ("the hurry for a vaccine isn't justified" or "the covid vaccine might give you AIDS") and outright spreading of misinformation on fake medicines ("I took chloroquine when I had covid and recovered just fine")

Being a full-time commitment and being remote aren't mutually exclusive though

Did you train using a public dataset and public ML techniques? If so, the model is not your intellectual property regardless of what any country may say

You don't need to use a single seed for the noise patch, you can use random numbers and it will work just fine

There are Reversible Neural Networks where this is possible to do, they're used for things such as for Normalizing Flows and even for very large NNs that don't fit in memory, since if the outputs are reversible you don't need to save intermediate activations during the forward pass, you can just recalculate them in the backward pass.

MLP Mixer would like to speak to you

I strongly disagree with your first paragraph. There is still a lot of work to be done on lossless compression, and I don't believe we are as close to the Shannon Bound as you seem to imply.

For instance, there are recent methods that use neural networks to do lossless compression by using a combination of ANS, Bits Back and VAEs, and they can often achieve much better compression rates than traditional methods. For an example, check this paper: https://arxiv.org/abs/1901.04866

Depends on the person, there's probably many people who prefer the general theoretical aspects too