dplavery92
Joined 8 years ago
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Comment · 2 years ago
Comment · 2 years ago
Eight is a nice power of two.
Comment · 2 years ago
You can also construct multiple hypothesis trackers from multiple Kalman Filters, but there is a little more machinery. For example, Interacting Multiple Models (IMM) trackers may use Kalman Filters or Particle Filters, and a lot of the foundational work by Bar-Shalom and others focuses on Kalman Filters.
Comment · 2 years ago
The Kalman filter has a family of generalizations in the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF.)
Also common in robotics applications is the Particle Filter, which uses a Monte Carlo approximation of the uncertainty in the state, rather than enforcing a (Gaussian) distribution, as in the traditional Kalman filter. This can be useful when the mechanics are highly nonlinear and/or your measurement uncertainties are, well, very non-Gaussian. Sebastian Thrun (a CMU robotics professor in the DARPA "Grand Challenge" days of self-driving cars) made an early Udacity course on Particle Filters.
Comment · 2 years ago
I was encountering the same problem on my Intel MBP, and per another one of the comments here, find that switching from Chrome to Safari to view the page allows me to view the whole page, view it smoothly, and without my CPU utilization spiking or my fans spinning up.
Comment · 3 years ago
I don't think anyone in this thread knows what happened, but since we're in a thread speculating why the CEO of the leading AI company was suddenly sacked, the possibility of an unacceptable interpersonal scandal isn't any more outlandish than others' suggestions of fraud, legal trouble for OpenAI, or foundering financials. The suggestion here is simply that Altman having done something "big and dangerous" is not a foregone conclusion.
In the words of Brandt, "well, Dude, we just don't know."
Comment · 3 years ago
Correct, the article places UHZ1 at 13.2 billion light-years away, so roughly ~500 Gy into our 13.7-billion-year-old universe.
Comment · 3 years ago
Comment · 3 years ago
From the captioned art in the article: "Siege, from the Peterborough Psalter, early 14th century, via the KBR Museum, Belgium.
Yes, those defenders are all women."
Comment · 3 years ago
I think a great place to start is https://www.bzarg.com/p/how-a-kalman-filter-works-in-picture...
Unlike the OP article, it does make use of the math formalism for Kalman filters, but it is a relatively gentle introduction that does a very good job visualizing and explaining the intuition of each term. I have gotten positive feedback (no pun intended!) from interns or junior hires using this resource to familiarize themselves with the topic.
If you are making a deeper study and are ready to dive into a textbook that more thoroughly explores theory and application, there is a book by Gibbs[1] that I have used in the past and is well-regarded in some segments of industry that rely on these techniques for state estimation and GNC.
[1] https://onlinelibrary.wiley.com/doi/book/10.1002/97804708900...
Comment · 3 years ago
From Sections 3 and 4 of the VQGAN paper[1] upon this work is built: "To generate images in the megapixel regime, we ... have to work patch-wise and crop images to restrict the length of [the quantized encoding vector] s to a maximally feasible size during training. To sample images, we then use the transformer in a sliding-window manner as illustrated in Fig.3." ... "The sliding window approach introduced in Sec.3.2 enables image synthesis beyond a resolution of 256×256pixels."
From the Paella paper[2]: "Our proposal builds on the two-stage paradigm introduced by Esser et al. and consists of a Vector-quantized Generative Adversarial Network (VQGAN) for projecting the high dimensional images into a lower-dimensional latent space... [w]e use a pretrained VQGAN with an f=4 compression and a base resolution of 256×256×3, mapping the image to a latent resolution of 64×64indices." After training, in describing their token predictor architecture: "Our architecture consists of a U-Net-style encoder-decoder structure based on residual blocks,employing convolutional[sic] and attention in both, the encoder and decoder pathways."
U-Net, of course, is a convolutional neural network architecture. [3]. The "down" and "up" encoder/decoder blocks in the Paella code are batch-normed CNN layers. [4]
[1] https://arxiv.org/pdf/2012.09841.pdf [2] https://arxiv.org/pdf/2211.07292.pdf [3] https://arxiv.org/abs/1505.04597 [4] https://github.com/dome272/Paella/blob/main/src/modules.py#L...
Comment · 3 years ago
Transformers are not forced to use a specific input (or output) shape; the original ViT paper demonstrates interpolating positional embeddings to inference with arbitrary image shapes.
Comment · 3 years ago
Presumably a transformer model or similar that uses positional encodings for the tokens could do that, but the U-Net decoder here uses a fixed-shape output and learns relationships between tokens (and sizes of image features) based on the positions of those tokens in a fixed-size vector. You could still apply this process convolutionally and slide the entire network around to generate an image that is an arbitrary multiple of the token size, but image content in one area of the image will only be "aware" of image content at a fixed-size neighborhood (e.g. 256x256).
[0] https://arxiv.org/pdf/2411.02265 [1] https://llm.hunyuan.tencent.com/