Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset. It cycles between searching for images on the Internet with text queries, self-supervised training on downloaded images, determining which images were useful, and prioritizing what to search for next.

**Residual Multi-Layer Perceptrons**, or **ResMLP**, is an architecture built entirely upon [multi-layer perceptrons](https://paperswithcode.com/methods/category/feedforward-networks) for image classification. It is a simple [residual network](https://paperswithcode.com/method/residual-connection) that alternates (i) a [linear layer](https://paperswithcode.com/method/linear-layer) in which image patches interact, independently and identically across channels, and (ii) a two-layer [feed-forward network](https://paperswithcode.com/method/feedforward-network) in which channels interact independently per patch. At the end of the network, the patch representations are average pooled, and fed to a linear classifier.

[Layer normalization](https://paperswithcode.com/method/layer-normalization) is replaced with a simpler [affine transformation](https://paperswithcode.com/method/affine-operator), thanks to the absence of self-attention layers which makes training more stable. The affine operator is applied at the beginning ("pre-normalization") and end ("post-normalization") of each residual block. As a pre-normalization, Aff replaces LayerNorm without using channel-wise statistics. Initialization is achieved as $\mathbf{\alpha}=\mathbf{1}$, and $\mathbf{\beta}=\mathbf{0}$. As a post-normalization, Aff is similar to [LayerScale](https://paperswithcode.com/method/layerscale) and $\mathbf{\alpha}$ is initialized with the same small value.

ResMLP

ResMLP: Feedforward networks for image classification with data-efficient training

Internet Explorer

Internet Explorer: Targeted Representation Learning on the Open Web

**BIMAN**, or **Bot Identification by commit Message, commit Association, and author Name**, is a technique to detect bots that commit code. It is comprised of three methods that consider independent aspects of the commits made by a particular author: 1) Commit Message: Identify if commit messages are being generated from templates; 2) Commit Association: Predict if an author is a bot using a random forest model, with features related to files and projects associated with the commits as predictors; and 3) Author Name: Match author’s name and email to common bot patterns.

Source	Internet Explorer: Targeted Representation Learning on the Open Web
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com