RISE Analysis 
Described in Bodily, Nyland, and Wiley (2017) <doi:10.19173/irrodl.v18i2.2952>. Automates the method of figuring out studying supplies that aren’t successfully supporting scholar studying in technology-mediated programs by synthesizing details about entry to course content material and efficiency on assessments.
The RISE (Useful resource Inspection, Choice, and Enhancement) Framework is a framework supporting the continual enchancment of open instructional assets (OER). The framework is an automatic course of that identifies studying assets that needs to be evaluated and both eradicated or improved. That is significantly helpful in OER contexts the place the copyright permissions of assets enable for remixing, enhancing, and bettering content material. The RISE Framework presents a scatterplot with useful resource utilization on the x-axis and grade on the assessments related to that useful resource on the y-axis. This scatterplot is damaged down into 4 completely different quadrants (the imply of every variable being the origin) to search out assets which might be candidates for enchancment. Assets that reside deep inside their respective quadrant (farthest from the origin) needs to be additional analyzed for steady course enchancment. We current a case examine making use of our framework with an Introduction to Enterprise course. Mixture useful resource use information was collected from Google Analytics and mixture evaluation information was collected from a web-based evaluation system. Utilizing the RISE Framework, we efficiently recognized assets, time intervals, and modules within the course that needs to be additional evaluated for enchancment. …
HyperFusion-Net
Salient object detection (SOD), which goals to search out a very powerful area of curiosity and phase the related object/merchandise in that space, is a vital but difficult imaginative and prescient process. This downside is impressed by the truth that human appears to understand predominant scene components with excessive priorities. Thus, correct detection of salient objects in complicated scenes is crucial for human-computer interplay. On this paper, we current a novel characteristic studying framework for SOD, through which we solid the SOD as a pixel-wise classification downside. The proposed framework makes use of a densely hierarchical characteristic fusion community, named HyperFusion-Internet, robotically predicts a very powerful space and segments the related objects in an end-to-end method. Particularly, impressed by the human notion system and picture reflection separation, we first decompose enter photographs into reflective picture pairs by content-preserving transforms. Then, the complementary data of reflective picture pairs is collectively extracted by an interweaved convolutional neural community (ICNN) and hierarchically mixed with a hyper-dense fusion mechanism. Primarily based on the fused multi-scale options, our technique lastly achieves a promising method of predicting SOD. As proven in our intensive experiments, the proposed technique persistently outperforms different state-of-the-art strategies on seven public datasets with a big margin. …
Ludwig
Ludwig is a toolbox that enables to coach and check deep studying fashions with out the necessity to write code. …
RedSync
Knowledge parallelism has already change into a dominant technique to scale Deep Neural Community (DNN) coaching to a number of computation nodes. Contemplating that the synchronization of native mannequin or gradient between iterations could be a bottleneck for large-scale distributed coaching, compressing communication visitors has gained widespread consideration lately. Amongst a number of latest proposed compression algorithms, Residual Gradient Compression (RGC) is likely one of the most profitable approaches—it will probably considerably compress the message measurement (0.1% of the unique measurement) and nonetheless protect accuracy. Nevertheless, the literature on compressing deep networks focuses nearly completely on discovering good compression charge, whereas the effectivity of RGC in actual implementation has been much less investigated. On this paper, we discover the potential of software RGC technique in the actual distributed system. Concentrating on the broadly adopted multi-GPU system, we proposed an RGC system design name RedSync, which features a set of optimizations to scale back communication bandwidth whereas introducing restricted overhead. We study the efficiency of RedSync on two completely different a number of GPU platforms, together with a supercomputer and a multi-card server. Our check instances embody picture classification and language modeling duties on Cifar10, ImageNet, Penn Treebank and Wiki2 datasets. For DNNs featured with excessive communication to computation ratio, which have lengthy been thought of with poor scalability, RedSync reveals important efficiency enchancment. …
