In this case, in addition to processing the visual features, one must mentally “rotate” one or both objects to decide their similarity. Such sensorimotor and mental transformations are ubiquitous and can power
Physics-informed neural networks (PINNs) leverage data and knowledge about a problem. They provide a nonnumerical pathway to solving partial differential equations by expressing the field solution as an artificial neural network. This approach has been applied successfully to various types of differential...
This approach is particularly useful where neighbouring target distributions in the sequence are similar to each other, and in this case has the following advantages over running T separate MCMC algorithms. The similarity of neighbouring targets can be exploited since particles approximating \(\pi _{...
George Box and Sir David Cox collaborated on one paper (Box, 1964). The story is that while Cox was visiting Box at Wisconsin, they decided they should write a paper together because of the similarity of their names (and that both are British). In fact, Professor Box is married to the...
Image Retrieval: We also add a simple retrieval experiment on Pairwise gen- eration results using pixelwise similarity. We count retrievals within reasonable distance (20 % of video length) to the ground truth as correct, achieving average accuracies on top-1/5 of p mse + adv: 0.68/0.94...
[18] proposed to learn the sample relations and perform joint optimiza- tion in a mini-batch. CuDi [21] proposes curve distillation to extract knowledge from large curve-based teacher net- works. CLIP-LIT [24] proposes a prompt learning frame- work includ...
One may hand-craft a heuristic "similarity function" between columns that may work for this simple example, but imagine the common scenario where all these column have similar-looking integer num- bers (e.g., with no dollar signs and percentage signs), which is much more challenging to ...
At least in some situations (low shape similarity), rather than picking an object because subjects knew it was the correct one, they picked an object because the alternatives were just less likely than the correct ones. The higher the perceived shape similarity, the less reliably they would be...
The count table, a numeric matrix of genes × cells, is the basic input data structure in the analysis of single-cell RNA-sequencing data. A common preprocessing step is to adjust the counts for variable sampling efficiency and to transform them so
4). The control ANN’s hidden layer also had significantly weaker similarity to the empirical RSMs at each parcel (Supplementary Fig. 4d). Task-performing neural network simulations via empirical connectivity The previous sections provided the groundwork for constructing an ENN model from empirical ...