qmatch

4 ms·

1.
by qmatch 6mo ago
Did you try asking it that? The example in the video was beyond what the typical apps provide (pulling race data from the BAA, adding temperature data from other sources Claude is familiar with). There are definitely limits with the amount
2.
by qmatch 9mo ago
Did you use Cursor with Gemini and GPT?
3.
by qmatch 11mo ago
I personally believe TF1 was serving the need of its core users. It provided a compileable compute graph with autodiff, and you got very efficient training and inference from it. There was a steep learning curve, but if you got past it, t
4.
by qmatch 11mo ago
As a loyal JAX user, I hope they can play catchup. PyTorch has dominated the AI scene since TF1 fumbled the ball at 10th yard line. What Matt Johnson has done turning Autograd into JAX is hopefully going to be worthy of as much praise as wh
5.
by qmatch 1y ago
I see your point, but not totally buying it. The US innovates for a global population, one that’s still growing. The best way to infer causality is through experimentation. If regulation does go away, we’ll measure and learn if it actually
6.
by qmatch 1y ago
How are so confident in causality here?
7.
by qmatch 1y ago
Similarly curious if anyone has an API, that they ultimately subsidize through some other service or product.
8.
by qmatch 1y ago
M, N, O, P, …
9.
by qmatch 2y ago
Need to read the details, but removing the norm can be big. It’s always a pain to make sure that your network is normalized properly when trying new architectures. Likely there will still be other implications of the tanh, since the norm
10.
by qmatch 2y ago
This is awesome—nice job! It was so cool to see when I started running and what year I started to get injured :/
11.
by qmatch 2y ago
What did the line represent? It’s always surprising how predictable it is to estimate people’s race times given what you know about their fitness. That said, about 10% of the time someone surprises you with a time you wouldn’t expect.
12.
by qmatch 2y ago
Try adjusting the slider! Gives us hope.
13.
by qmatch 2y ago
Is this similar to the Universal Approximator Theorem?
14.
by qmatch 2y ago
Google stopped providing predictions about 9 years ago. https://en.m.wikipedia.org/wiki/Google_Flu_Trends#:~:text=wi... .
15.
by qmatch 4y ago
A nice simple walkthrough in this post, but would be nice if it was updated to show how to do this with sharding and the new jax.Array type introduced not too long ago https://github.com/google/jax/pull/11233&
16.
by qmatch 4y ago
Link to paper: https://arxiv.org/abs/2302.05444
17.
by qmatch 4y ago
From Google. We did most of our studies on tabular data, which has been a less studied area in SSL.
18.
Non-Contrastive Self-Supervised Learning(arxiv.org)
1 points by qmatch 4y ago|1 comments