generative modelling

CellStrat > Research/Blog > generative modelling
13
Aug
Generative Models for Graphs

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Recently, our AI Lab Researcher Gouthaman Asokan presented an amazing session on Generative Models for Graphs. This is part of our webinar series on Graphs and Graph Neural Networks. Graph is a nonlinear data structure consisting of nodes and edges. Widely used in applications like Physical system interactions, Knowledge graphs, Molecular interactions, Transportation […]

Posted in: Generative Modeling, Graph Neural Networks, Pharma,
Tags: bernoulli distribution, categorical distribution, deep generative model for graph, DGMG, generative model, generative modelling, GrammarVAE, graph embedding, graph grammar, graph neural network, graph propagation, molecular generation, Simplified Molecular Input Line Entry System, SMILES, synthentic graph, synthetic graph, teacher forcing,
15
May
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #WhereLearningNeverStops

Last Saturday 9th May ’20, our AI Lab researchers presented some superb sessions. Credit Risk Modelling with ML :- First, RameshBabu L. presented an excellent session on Credit Risk Modelling. Credit risk modeling refers to data driven risk models which calculates the chances of a borrower defaults on loan (or credit card). Logistic regression method […]

Posted in: Computer Vision, Deep Learning, Financial Services, Machine Learning,
Tags: Computer Vision, Credit Risk Modelling, deep learning, GANs, generative modelling, Hormonization, machine learning, Paint2Image, SINGAN, Super Resolution,
19
Feb
1
Photo-Realistic Single Image Super-Resolution using SRGAN

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars #AlwaysUpskilling Last Saturday (15th Feb 2020) our AI Lab Team Lead Abdul Azeez presented a superb hands-on workshop on Super-Resolution GANs or SRGANs as they are called. SRGANs are very useful to increase the resolution in images (or create super-resolved images). Basic GAN :- GANs or Generative Adversarial Networks are generative models […]

Posted in: Computer Vision, Deep Learning, Generative Modeling,
Tags: Adversarial Loss, Content Loss, ESPCN, GAN, generative modelling, Generator Loss, Perceptual Loss, Pixel shuffle, PSNR, Single Image Super Resolution, SISR, SRCNN, SRGAN, SSIM, Super Resolution GAN, VDSR,
19
Dec
Generative Modelling with wGAN (Wasserstein GAN)

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Last Saturday, our AI Lab team Lead Abdul Azeez presented an extensive hands-on workshop on wGANs or Wasserstein GANs. Overview of wGANs :- A GAN is a pair of neural networks – Generator and Discriminator. The G tries to produce real-like images and D tries to distinguish the generated images from real […]

Posted in: Computer Vision, Deep Learning, Generative Modeling,
Tags: 1-Lipschitz function, Earth-Mover Distance, EM Distance, GAN, generative adversarial network, generative modelling, gradient penalty, Jensen-Shannon divergence, JS Divergence, KL Divergence, Kullback-Leibler divergence, Wasserstein distance, Wasserstein GAN, wGAN, wGAN-GP, wGANs,
12
Dec
Meeting Minutes from CellStrat AI Lab meetup on Saturday 7th Dec 2019 at Bengaluru

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Minutes from Saturday 7th Dec AI Lab meetup at BLR :- Last Saturday we had fabulous presentations by AI Lab members in BLR. Recommender Systems :- In our Hebbal AI Lab, the day started with a deep session on Recommender Systems by Gurumoorthy Loganathan. Recommender system is a subclass of information filtering […]

Posted in: Computer Vision, Deep Learning, Generative Modeling, Machine Learning, Natural Language Processing, Reinforcement Learning,
Tags: BERT, CycleGAN, GANs, generative modelling, keypoint detection, openpose, optimal policy, product recommendations, Question-Answer Model, recommender systems, reinforcement learning, SQuaD, Wasserstein GAN,
24
Jul
Meeting Minutes from AI Lab session on Saturday 20th July in Bengaluru

#CellStratAILab #disrupt4.0 #WeCreateAISuperstars CellStrat AI Lab had remarkable presentations last Saturday in the AI Lab meetup. First Vivek Singhal (Co-Founder & Chief Data Scientist, CellStrat) started with a deep-dive into Convolutional Neural Networks or CNNs, specialized neural networks which are useful for image processing applications particularly. CNNs work by applying a kernel filter weight matrix […]

Posted in: Deep Learning, Machine Learning,
Tags: AI lab, artificial intelligence, AutoKeras, automl, AutoWEKA, Bayesian optimization, CNN, Decision Trees, deep learning, Disrupt4.0, generative modelling, grid search, H2O.ai, image processing, image segmentation, KNNs, machine learning, object detection, parameter search, pose extraction, random search, sequential models, TPOT,