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Found 6 papers, 3 papers with code
Tur[k]ingBench: A Challenge Benchmark for Web Agents
We evaluate the performance of state-of-the-art models, including language-only, vision-only, and layout-only models, and their combinations, on this benchmark.
Class Density and Dataset Quality in High-Dimensional, Unstructured Data
We provide a definition for class density that can be used to measure the aggregate similarity of the samples within each of the classes in a high-dimensional, unstructured dataset.
Towards an Analytical Definition of Sufficient Data
We show that, for each of five datasets of increasing complexity, certain training samples are more informative of class membership than others.
On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs
We present a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories.
No Routing Needed Between Capsules
Most capsule network designs rely on traditional matrix multiplication between capsule layers and computationally expensive routing mechanisms to deal with the capsule dimensional entanglement that the matrix multiplication introduces.
Ranked #1 on
Image Classification
on MNIST
Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks
Capsules, on the other hand, produce a vector of values, which Hinton argues correspond to a single, composite feature wherein the values of the components of the vectors indicate properties of the feature such as transformation or contrast.
