Analog in-memory computing is a promising future technology for efficiently accelerating deep learning networks. While using in-memory computing to accelerate the inference phase has been studied extensively, accelerating the training phase has received
We show the ARI and AMI scores for both distance-based and density-based CLASSIX clustering. We observe that the method is not very sensitive to the [Math Processing Error] parameter. As long as this parameter is chosen within the interval 0.2 to 0.6, good to very good clustering results ...
Advances in neural information processing systems, NeurIPS, Long Beach, California, USA Ma JW, Jiang S, Liu ZY, Ren ZY, Lei DZ, Tan CH, Guo HX (2022) Machine learning models for slope stability classification of circular mode failure: an updated database and automated machine learning (...
A recent study compared the stability of different state-of-the-art MLFFs in short MD simulations and found that only the SO(3) convolution-based architectureNequIP36gave reliable results32. However, the excellent stability of such models comes at a substantial computational cost associated with t...
One of their most significant discoveries was a reliable description of the spatial distribution of GCRs. They found a small yet noticeable spatial (i.e., both latitudinal and radial) gradient in GCR intensity in the inner heliosphere, which decreased rapidly with increasing heliocentric distance. ...
Notice that unless there are extended segments of one language not corresponding to anything in the other language, the true points of correspondence should all be close to proportionately the same distance from the beginning of each text. For example, the only way a point 30% of the way ...
The value specified in this option represents the grammar-based distance threshold for two sequences to be consider grammatically identical. When a new sequence is added to a cluster, it has a distance less than one of the thresholds (specified by -C or -G). In the event that two ...
(<50%), suggesting that more SNVs are required before making reliable interpretations. All other methods recovered very distinct trees (Fig. S17B-F), and in the case of TNT, also with shallow bootstrap values. Unfortunately, infSCITE, SiFit, SCIPhI, and ScisTree do not assess branch ...
ordinal metric learning fine-tunes the SRH foundation model by maximizing the latent distance, or metric, between whole-slide SRH images with different degrees of tumour infiltration (Extended Data Fig.5a,b). Moreover, the increased efficiency of ordinal metric learning stems from enforcing that the...
variable-resolution models. The control parameterEmaxwas set to 10% for Algorithm III because the construction of a reliable metamodel for antenna frequency characteristics is considerably more challenging than representing feature point coordinates. Also, in this case, the maximum computational budget of...