In Supervised ML handling Unseen Data and As generic post processing and avoiding generalization error or mis classification and have more representative samples and retrining
This presentation focuses on Feature Engineering and the Heuristics which can be extracted pre modelling whihc can be used post modelling for change detectors, explain ability etc.
Learning from Data - Various Approaches - Postermadhucharis
Big Data, Thick Data, Wide Data, Structured Neural Learning
Knowledge Representation, Graph and Neural Learning
Collage from different Images, to emphasize the Theme
A Brief Walk through into Convolutional Neural Networks on
Classical Machine Learning vs Deep Learning
Autonomous Car, Medical
Recognition Challenges, Accuracy and Adversary Attacks
This presentation focuses on Feature Engineering and the Heuristics which can be extracted pre modelling whihc can be used post modelling for change detectors, explain ability etc.
Learning from Data - Various Approaches - Postermadhucharis
Big Data, Thick Data, Wide Data, Structured Neural Learning
Knowledge Representation, Graph and Neural Learning
Collage from different Images, to emphasize the Theme
A Brief Walk through into Convolutional Neural Networks on
Classical Machine Learning vs Deep Learning
Autonomous Car, Medical
Recognition Challenges, Accuracy and Adversary Attacks
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Innovative Methods in Media and Communication Research by Sebastian Kubitschk...
Machine Learning , Decision Tress and Novelty Detection
1. Machine Learning Algorithms
(MLAs) and Novelty Detection
Rangaprasad Sampath (ranga.sampath@gmail.com)
Madhusoodhana Chari S (madhucharis@gmail.com)
2. Teaching a Child and a MLA (Machine
Learning Algorithm)
Orange
Apple
3. Responses to Unseen/Novel Objects
Insights
Humans may not classify Unseen/Novel objects into known classes.
MLAs classify Unseen/Novel objects into the most probable class.
Orange Orange (30%), Apple (70%)
Apple
I don’t know Orange (72%), Apple (28%)
No response
4. Decision Tree Classifier
Decision Tree Classifier labels as a Car or Not based on the
measurements of length (x2) and width (x1) of a vehicle.
4
x1
Width
(m)
x2
Length
(m)
Label
4 9 !Car
4 10 !Car
4 12 Car
3 11 Car
7 10 !Car
8 16 Car
12 18 Car
10 18 Car
Training Data
5. Novelty Classification
Test Data that is very different from what has
been seen during training i.e. Novelty, is
classified into one of the known classes.
5
x1 x2 Label
4 9 !Car
4 10 !Car
4 12 Car
3 11 Car
Test Data (Red dots)
6. Recognition of an Unknown class
Proposal: Label test data points that
represent Novelty as Unknown.
6
x1 x2 Label
4 9 Unknown
4 10 Unknown
4 12 Unknown
3 11 Unknown
Test Data (Red dots) Unknown
Unknown
Unknown
Unknown
7. Benefits
• Avoidance – of Novel data misclassification.
• Utility – in domains where it is difficult to obtain fully representative
datasets for training.
• Retraining – could be triggered if the number of data points classified
as Unknown exceeded the number of data points classified into
known classes.
• Alerts – could be raised if the percentage of data that got classified as
an Unknown class exceeded a set threshold (say 10 %).
Editor's Notes
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This is a sample Three Pictures with Captions slide ideal for including three pictures with brief, bulleted text.
To Replace the Pictures on this Sample Slide (this applies to all slides in this template that contain replaceable pictures)
Select the sample picture and press Delete. Click the icon inside the shape to open the Insert Picture dialog box. Navigate to the location where the picture is stored, select desired picture and click on the Insert button to fit the image proportionally within the shape.
Note: Do not right-click the image to change the picture inside the picture placeholder. This will change the frame size of the picture placeholder. Instead, follow the steps outlined above.
Tip: use the Crop tool to reposition a picture within a placeholder. From the Picture Tools Format tab on the ribbon, click the Crop button. Click and drag the picture within the placeholder to reposition. To scale the picture within the placeholder (while Crop is active), grab a round corner handle and drag to resize. Hold Shift key to constrain picture aspect ratio when resizing.
This is a sample Three Pictures with Captions slide ideal for including three pictures with brief, bulleted text.
To Replace the Pictures on this Sample Slide (this applies to all slides in this template that contain replaceable pictures)
Select the sample picture and press Delete. Click the icon inside the shape to open the Insert Picture dialog box. Navigate to the location where the picture is stored, select desired picture and click on the Insert button to fit the image proportionally within the shape.
Note: Do not right-click the image to change the picture inside the picture placeholder. This will change the frame size of the picture placeholder. Instead, follow the steps outlined above.
Tip: use the Crop tool to reposition a picture within a placeholder. From the Picture Tools Format tab on the ribbon, click the Crop button. Click and drag the picture within the placeholder to reposition. To scale the picture within the placeholder (while Crop is active), grab a round corner handle and drag to resize. Hold Shift key to constrain picture aspect ratio when resizing.