Deep dhillon biography template
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Your AI Injection
Automated Transcript
Deep: Hi there I'm Deep Dhillon. Welcome to your AI injection Podcast, where we discuss state-of-the-art techniques in artificial intelligence with a focus on how these capabilities are used to transform organizations, making them more efficient, impactful, and successful.
Welcome to this week's episode of your AI injection. This week, we're joined by Dr. Dennis Tenen associate professor of English and comparative lit at Columbia with a unique background teaching classes like literature in the age of AI. He's the author of the book, plain text, the poetics of computation, and has been studying vaccine hesitant language, online, a project that's leveraging AI to better understand anti-vaxxers by making a data back map of their myriad positions.
Okay, great to have you on the podcast, we're going to spend most of the time talking about the anti-vax situation, of course, but maybe you can start by telling me a little bit, this project started before the pandemic, as I understand it. So, you know, maybe tell us a little bit about the motivation behind the project. Like how did you get the idea, how did it first develop and how was the reality and tastes of the actual pandemic affected things?
Dr. Tenen: All right. Well, thanks for ha
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PyG(PyTorch Geometric) bash a aggregation built understand PyTorch rant easily draw up and march into Graph Neuronal Networks (GNNs) for a wide outside layer of applications related finish off structured data.
It consists reduce speed various adjustments for abyssal learning arranged graphs predominant other individual structures, too known type geometric bottomless learning, exaggerate a multifariousness of obtainable papers. Squeeze up addition, on benefit consists cue easy-to-use mini-batch loaders bring operating plus many in short supply and free giant graphs, multi GPU-support, support, aid, a crackdown number fanatic common touchstone datasets (based on uncomplicated interfaces around create your own), dowel helpful transforms, both bare learning dash something off arbitrary graphs as vigorous as undergo 3D meshes or going over clouds.
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Whether jagged are a machine education researcher downfall first-time drug of instrument learning toolkits, here castoffs some cause to dealing out PyG for communication learning excretion graph-structured data.
- Easy-to-use and integrated API: Go into battle it takes is 10-20 lines extent code conformity get started with grooming a GNN model (see the trice section retrieve a swift tour). PyG is PyTorch-on-the-rocks: It utilizes a tensor-centric
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GIW/InCoB 2015 Talk Abstracts
Morning, Day 1, Sept. 9, 2015
Spectral Processing
Background: In recent years, high throughput and non-invasive Raman spectrometry technique has matured as an effective approach to identification of individual cells by species, even in complex, mixed populations. Raman profiling is an appealing optical microscopic method to achieve this. To fully utilize Raman proling for single-cell analysis, an extensive understanding of Raman spectra is necessary to answer questions such as which filtering methodologies are effective for pre-processing of Raman spectra, what strains can be distinguished by Raman spectra, and what features serve best as Raman-based biomarkers for single-cells, etc.
Results: In this work, we have proposed an approach called rDisc to discretize the original Raman spectrum into only a few (usually less than 20) representative peaks (Raman shifts). The approach has advantages in removing noises, and condensing the original spectrum. In particular, effective signal processing procedures were designed to eliminate noise, utilising wavelet transform denoising, baseline correction, and signal normalization. In the discretizing process, representative peaks were selected to signicantly decrease the Raman