markisus

7 ms·

31.
by markisus 1y ago
Can someone explain the bit counting argument in the reinforcement learning part? I don’t get why a trajectory would provide only one bit of information. Each step of the trajectory is at least giving information about what state transition
32.
by markisus 1y ago
These problems seem to have the flavor of interview questions I heard for quant positions.
33.
by markisus 1y ago
Interesting article. It’s actually very strange that the dataset needs to be “big” for the O(n log n) algorithm to beat the O(n). Usually you’d expect the big O analysis to be “wrong” for small datasets. I expect that in this case, like in
34.
by markisus 1y ago
Just speculating but proximity to a reference answer is a much denser reward signal. In contrast, parsing out a final answer into a pass/fail only provides a sparse reward signal.
35.
by markisus 1y ago
I’m not sure that LLMs are solely autocomplete. The next token prediction task is only for pretraining. After that I thought you apply reinforcement learning.
36.
by markisus 1y ago
Did this end up working? It sounds plausible but it needs some empirical validation.
37.
by markisus 1y ago
Yeah I try to make sure I do the extern c. I’m also on x86 so I just pretend that alignment is not an issue and I think it works.
38.
by markisus 1y ago
CBOR has some stuff that is nice but would be annoying to reimplement. Like using more bytes to store large numbers than small ones. If you need a quick multipurpose binary format, CBOR is pretty good. The only alternative I’d make manually
39.
by markisus 1y ago
Something seems off with equation (5). Just imagining Monte Carlo sampling it, the middle expectation will have a bunch of zeros due to the indicator function and the right expectation won’t. I can make the middle expectation be as close to
40.
by markisus 1y ago
The ultimate compression is to send just the user inputs and reconstitute the game state on the other end.
41.
by markisus 1y ago
I think you are missing that d, x, and y are variables that get optimized over. Any choice of d lower than the the solution to 1) is infeasible. Any d higher than the solution to 1) is suboptimal. edit: I see now that the problem 2) is miss
42.
by markisus 1y ago
Me too. I’m feeling that things have not changed that much. Many companies have been started since 2023 to see if they can get the giant neural net approach to work and we have seen incremental progress based on demo videos. Meanwhile Tesla
43.
by markisus 1y ago
Congrats on the launch! Your current market seems to be "niche toys for rich tech people" and the future market seems very uncertain. I am impressed that you were able to get funding for this idea. How do you get around the "
44.
by markisus 1y ago
Back in my earlier days working on autonomous vehicles, I dreamed of something like this. The issue with bounding boxes is missed detections, occlusions, and impoverished geometrical information. But if you have a hundred points being stabl
45.
by markisus 1y ago
Insurance appeals is an actual problem though. Medical practices have to hire staff to argue with insurance companies and it increases healthcare costs.
46.
by markisus 1y ago
The demos look great! I imagine it's not pure javascript. Are you using webgpu?
47.
by markisus 1y ago
It’s an abstraction that helps mathematicians study interesting phenomena. I believe the famous squaring the circle problem was resolved using the language of fields.
48.
by markisus 1y ago
I've uploaded a screenshot from LiveSplat where I zoomed in a lot on a piece of fabric. You can see that there is actually a lot of diversity in the shape, orientation, and opacity of the Gaussians produced [1]. [1] https://
49.
by markisus 1y ago
Here is an example of a view dependent effect produced by LiveSplat [1]. Look closely at the wooden chair handle as the view changes. I'll concede that ten years ago, someone could have done this. But no one did, as far as I know. [1]
50.
by markisus 1y ago
Yes this converts video stream (plus depth) into Gaussian splats on the fly. While the system is running you can move the camera around to view the splats at different angles. I took a screen recording of this system as it was running and c
51.
by markisus 1y ago
If the scene is static, the normal Gaussian splatting pipeline will give much better results. You take a bunch of photos and then let the optimizer run for a while to create the scene.
52.
by markisus 1y ago
I don't have a 3060 at hand so I'm not sure. Ideally someone with that setup will try it out and report back. There is no noticeable latency when comparing visually with standard pointcloud rendering. With framerate, there are two
53.
by markisus 1y ago
There is no gradient-based optimization. It's (RGBD input, Current Camera Pose) -> Neural Net -> Gaussian Splat output. I'm not aware of other live RGBD visualizations except for direct pointcloud rendering. Compared to poin
54.
by markisus 1y ago
Yup, this is the case for all neural nets.
55.
by markisus 1y ago
The application has this feature and lets you switch back and forth. What you are talking about is the standard pointcloud rendering algorithm. I have an older video where I display the corresponding pointcloud [1] in a small picture in pic
56.
by markisus 1y ago
I had to make a lot of concessions to make this work in real-time. There is no way that I know to replicate the fidelity of "actual" Gaussian splatting training process within the 33ms frame budget. However, I have not baked in th
57.
by markisus 1y ago
Actually there are multiple source cameras. The neural net learns to interpolate the source camera colors based on where the virtual camera is. Under the hood it's hard to say exactly what's going on in the mind of the neural net,
58.
by markisus 1y ago
I've tried to make it clear in the link that the actual application is closed source. I'm distributing it as a .whl full of binaries (see the installation instructions). I've considered publishing the source but the source co
59.
by markisus 1y ago
Note that the method you linked is "Splatting in Seconds" where as real-time requires splatting in tens of milliseconds. I'm also following this work https://guanjunwu.github.io/4dgs/ which produces temp
60.
by markisus 1y ago
There is no temporal accumulation, but I think that's the next logical step. Supervised learning actually does work. Suppose you have four cameras. You input the three of them into the net and use the fourth as the ground truth. The li

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