We evenly split a pre-trained backbone into 4 subsets of blocks(e.g., 6 in each subset for the 24-block ViT-L).

We propose a cross-covariance based self-attention function that operates along the featuredimension, rather than along the token dimension as in token self-attention.

We restrict the magnitude of the query and key matrices by `2-normalising them,such that each column of length N of the normalised matrices ˆQ and ˆK has unit norm, andevery element in d×d cross-covariance matrix ˆK> ˆQ is in the range [−1,1].

Relationship between Gram and covariance matrices. To motivate our cross-covarianceattention operation, we recall the relation between Gram and covariance matrices. Theunnormalised d×d covariance matrix is obtained as C=X>X. The N×N Gram matrixcontains all pairwise innerproducts: G=XX>. The non-zero part of the eigenspectrum ofthe Gram and covariance matrix are equivalent, and the eigenvectors of C and G can becomputed in terms of each other. If V are the eigenvectors of G, then the eigenvectors of Care given by U=XV . To minimise the computational cost, the eigendecomposition of eitherthe Gram or covariance matrix can be obtained in terms of the decomposition of the other,depending on which of the two matrices is the smallest.

Touvron et al. [65]

[22]

We live in a connected world, and modern software has to navigate this world. So the building blocks for tomorrow's very largest solutions are connected and massively parallel. It's not enough for code to be "strong and silent" any more. Code has to talk to code. Code has to be chatty, sociable, well-connected. Code has to run like the human brain, trillions of individual neurons firing off messages to each other, a massively parallel network with no central control, no single point of failure, yet able to solve immensely difficult problems. And it's no accident that the future of code looks like the human brain, because the endpoints of every network are, at some level, human brains.

connectionism

So far, we have dealt with self-reference, but the situation is quite similar with the notion of self-modification. Partial self- modification is easy to achieve; the complete form goes beyond ordinary mathematics and anything we can formulate. Consider, for instance, recursive programs. Every recursive program can be said to modify itself in some sense, since (by the definition of recursiveness) the exact operation carried out at time t depends on the result of the operation at t-1, and so on: therefore, the final "shape" of the transformation is getting defined iteratively, in runtime (a fact somewhat obscured by the usual way in which recursion is written down in high-level programming languages like C). At the same time, as we can expect, to every finite recursive program there belongs an equivalent "straight" program, that uses no recursion at all, and is perfectly well defined in advance, so that it does not change in any respect; it is simply a fixed sequence of a priori given elementary operations.

It seems that one-hot encoding isn’t necessary for tree-based algorithms, but you may run into a problem using this on linear models. I personally find ordinal encoding is more than sufficient and it significantly reduces your memory footprint if you have quite a few categorical features.

DEEP learning refers to a network of connected artificialneurons in multiple layers, which can perform featureextraction from observed data and learn the complicatedrelationships among the features of data.

Can I get an RSS feed to follow new annotations on a document? Yes! You can get RSS and Atom feeds by URL, Tag and User.

The web today is full of disposable speech acts, that are not maintained, enriched, or returned to. Tweets, Facebook posts, contextually dependent blog posts. Consequently entering new conversations feels like sifting through the Nixon tapes.

There is a conceptual and language gap. The sciences of neural networks and probability models do not have a shared language. My goal is to bridge this idea gap and allow for more collaboration and discussion between these fields, and provide a consistent implementation (Github link).

Critics have suggested this way of thinking about the stock market is outdated. In other words, “this time is different.” And even if they admit that comparing equity valuations to net worth has some value they insist that value does not include timing the market.

“10,000 hours” theory of expertise

Accept Everything. But Don’t Be Passive

This is a picture of the first HTTP web server in the world. It was Tim Berners-Lee's NeXT computer at CERN. Pasted on the machine is an ominous sticker: "This machine is a server, do not power it down!!". The reason it couldn't be powered down is that web sites on other servers were starting to link to it. Once they linked to it, they then depended on that machine continuing to exist. If the machine was powered down, the links stopped working. If the machine failed or was no longer accessible at the same location, a far worse thing happened: the chain between sites becomes permanently broken, and the ability to access that content is lost forever. That sticker perfectly highlights the biggest problem with HTTP: it erodes.

In the 1970s, software problems were more accessible in the sense that fewer regulations were applied to restrict programmers to access key materials (codes peculiarly). There was a so-called "collaborative hacker" approach, sharing knowledge and improving each other's work, that encouraged programmers to continually redefine the boundaries of the problem (e.g. conducting reverse engineering to deconstruct a software to understand how a code was written). As a result, a wide range of software programming tools (languages, editors, compilers etc.) was created in the 1970s (Ceruzzi, 2003).