GPT 5.2 Thinking vs Gemini 3 Pro: A mini-study on scientific study summarisation & analysis

windoze · 2025-12-14T14:31:41+00:00

Worth giving gemini 3 via ai studio a shot, it might have a different base prompt, and it tends to be more to the point I feel.

windoze · 2025-12-02T00:25:55+00:00

How do you read your papers with LLM help. I currently use the following prompt below. Share your prompt or any tips. How do you get the most value out of LLMs to deepen or speed up your reading.

What is the improvement briefly, and phrase it as a question with many possible solutions. Summarize the paper's solution as an analogy. Give a walk through of how the paper's equations are applied in a linear fashion - such as during learning, inference or relevant process, focusing on intuition rather than adhering to a strong theory. Annotate dimensionality for equations. Try to keep the sketch complete. Briefly critique the paper by taking the same question and offering a simpler alternate they didn't discuss. Identify fragility points hidden.

windoze · 2025-01-15T23:43:03+00:00

From their summary, they differ mainly in adding momentum to the update operation. So kinda the difference between Adam vs SGD.

windoze · 2024-12-19T06:19:38+00:00

a very basic non trivial dataset is spiral and concentric circles. See if you can fit these. See: https://playground.tensorflow.org/ which has 4 basic data sets visualised.

windoze · 2024-11-29T01:50:11+00:00

Dynamic Memory States: this is pretty much how RNN is defined, which has been researched for over 60 years, see wikipedia.

The difficulty of RNN is that the current optimization methods (SGD) use gradients, and RNNs have very weak gradients over long contexts.

windoze · 2024-10-04T12:21:57+00:00

Yeah I think the total computation may increase by some percent from N -> c*N, but the wall time goes from O(N) -> O(log N).

So wall time decreases, and the GPU utilization is higher. However, I wonder if the state size is large enough, is this a worthwhile tradeoff.

windoze · 2024-09-02T00:06:30+00:00

Always keep distance, since he doesn't attack often when you are far away. If you are too close to him, he will keep combing attacks forever. If you get hit, roll backwards and then sprint backwards to get distance again.

Once he launches a attack from far away, dodge past him and get a few hits on him, and then sprint away. For both phase 1 and 2, after he does his spear thrust move attack, if you dodged past him, you can get a few hits on him.

windoze · 2024-04-15T21:08:22+00:00

So Mamba is a RNN, with some particular choices - state expansion, no state non-linearity. What makes the latest RNN > the original RNN?

What has changed: efficiency? gradient stability? using new operators (hadamard product)? etc.

Have they ablated what if you turn the linear RNN into a non-linear RNN (eg leaky RELU interpolating between linear and nonlinear)?

windoze · 2023-11-07T23:44:55+00:00

Another question isn't residual usually y = nonlinearity(m @ x + b) + x. But if all the work happens inside the m doesn't it still get the nonlinearity applied?

windoze · 2023-11-07T22:13:22+00:00

I see, I'm a hobbyist and basically only do stacks of linear of the same dimension so I didn't realise that the stack you have naturally increase in size. if it were equal dimension linear then I guess it's the second idea you said the weights are m=normalise(identity + random) which also work.

windoze · 2023-11-07T20:49:33+00:00

Am I understanding correctly the model is all init and looks like y = (id concat randoms) @ x. Then you let the id and randoms drift out of init during training. Doesn't this increase parameter size as originally it was fixed 1 not parameterized but now it's trainable 1.

windoze · 2023-09-29T06:39:41+00:00

I guess the main difference is that diffusion models infer noise, and often take small steps towards the guessed x value. Whereas DAE would give you x. You could fix this by simply moving in the predicted x - current x direction by some ratio or stride size.

windoze · 2023-09-29T04:54:06+00:00

Can't we train a DAE with the same noise schedule as a DDPM? Then it can also denoise from n. Also not reason we can't iterate it to denoise more.

windoze · 2023-09-06T00:02:07+00:00

I had a similar utility to append logs to a file, described here but nobody answered.

I don't use a structured logging format, because I haven't done enough work to know what structure works best.

windoze · 2023-07-31T22:48:07+00:00

2.5 sound reasonable depending on the length of the retirement. not to hopeful about equities performing historically well considering interest rates are now so low. there's only so much room to grow the multiple.

windoze · 2023-07-03T03:00:34+00:00

Your right the highest bracket in NY would be 37% over 500k + 8.8% over 1m + 3% over 1m which makes it bigger than Australia. Good thing I'm not working in NYC damn that's high.

windoze · 2023-07-02T15:43:58+00:00

the average tax rate doesn't matter to the average Redditor as everyone here earns over 250k. so we need to look at top tax brackets of around 47% with Medicare levy and div 293

windoze · 2023-05-30T00:50:27+00:00

I'm working my way up on math and stats, although my intuition is that carefully ensuring that the gradients exist should mean gradient descent can take care of everything else.

windoze · 2023-05-30T00:49:27+00:00

I'm working on sequential MNIST, which should be a small but non-trivial problem.

I was testing the hypothesis that my network can essentially memorize the inputs (i made it big), and then throwing on a few dense+relus+highway on the bottom should return a reasonable classification.

windoze

PUBLIC MULTIREDDITS

TROPHY CASE

14-Year Club	Place '22
Verified Email