nothing0001

4 ms·

1.
by nothing0001 4y ago
I find a little sad that many people could change books and sharing experiences with others with using tools such as chatgpt. Those tools could be used to not discuss things like education and guide people to follow the agenda of the fine-t
2.
by nothing0001 4y ago
How can they avoid that someone perform a post-training from this model to obtain a different model that perhaps could not be protected by copyright?
3.
by nothing0001 4y ago
I think you are absolutely right, but those sames problems apply when the paper claim that the average of two models gives a good model. So in that case the weight space could have additional properties that could make the proposed appr
4.
by nothing0001 4y ago
A negative value cannot be produced but in the hidden layers the output of one neuron is multiplied by weights so the sign can be encoded in those weights.
5.
by nothing0001 4y ago
Reading the paper, I was thinking about the following: Given the weights of two models w1 and w2, then at each neuron k compute some average of the absolute difference of the outputs of neuron k over the training set. Then perhaps the neuro
6.
by nothing0001 4y ago
I wonder what happens when one change the activation function, is there some related results in that direction?
7.
by nothing0001 4y ago
I asked chatgpt to generate the different orders of 5 words, it failed at it. Then I ask it to generalize the problem and then it refers to permutations (giving the formula). Then I ask it to use that information to generate the differents
8.
by nothing0001 4y ago
Now that I am interested in large language model and as I begin reading Street Fighting Mathematics, the first thought is that large language model can learn a lot (statistical approximate inference) from dimensional analysis, that is usin
9.
by nothing0001 4y ago
It seems I used a lot of prompts: Some of them follow. Define the projection of word1 with respect to word2 as a word that is has a similar meaning to word1 and word2 and that is usually used with word1 -- The projection of word1 with respe
10.
Teaching GPT Word Reflections
3 points by nothing0001 4y ago|2 comments
11.
by nothing0001 4y ago
Just make a loss function that gives +1 for correct answer, -1 for incorrect answers and 0 for unknown. Since this idea took me like 10 seconds, I suppose something like this must/should have been used before. Unrelated, what happen wh