
Vivek Singhal
Co-Founder & Chief Data Scientist
CellStrat
location_on India
Member since 4 years
Vivek Singhal
Specialises In (based on submitted proposals)
Serial Entrepreneur and Artificial Intelligence / Machine Learning expert.
A leading Data Scientist and researcher with expertise in AI and Machine Learning. Also, a Startup Advisor and Industry Consultant having spent many years in USA and India in the high-tech industry. Co-Founder at CellStrat, India's leading Artificial Intelligence startup.
Serial Entrepreneurial experience having Co-Founded or advised several startups in prior years including Healthiply.in (AI-driven online health startup), LocVille.com (online furniture and decor) and SalesGlobe (sales CRM).
Long experience in telecom and digital industries in Strategy, Mobile Apps/Web, Data Analytics, Systems Integration and Enterprise Mobility, in leading MNCs like IBM, AT&T, Schlumberger, HCL-HP etc.
Thought leader within and outside the corporate environment driving innovation and consulting in emerging areas like Artificial Intelligence, Social Commerce, Enterprise Mobility and IoT. Regular participation in events and forums in the capacity of Advisor and Speaker.
Specialties: Artificial Intelligence / Machine Learning, Mobility, ECommerce, Startups, Telecom, Business Consulting, Systems Integration
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RL in Finance with ActorCritic
Shubha ManikarnikeDeep Reinforcement ResearcherCellstrat AI LabVivek SinghalCo-Founder & Chief Data ScientistCellStratschedule 2 years ago
Sold Out!20 Mins
Demonstration
Intermediate
Reinforcement Learning broadly involves Value-based methods and Policy-based Methods. Actor-based methods are suitable for discrete action spaces, whereas Policy-based methods are suitable for continuous action spaces.
A simple Policy Gradient algorithm (a policy-based method) predicts the probability of Actions of an Agent. Since, the Policy is updated using Monte Carlo approach (i.e. taking random samples), the variance of log probabilities produced by the PG algorithm is very high, causing unstable learning. This variance can be reduced by using a Baseline to remove outliers. A good choice to be used as a baseline is the Q-Value of the State, Action pair.
In this session, we will talk about Actor Critic algorithms, which uses two networks - an Actor and a Critic. The actor outputs the Policy, while the Critic gives out the Q-value which can be used to evaluate the Policy. In this zero-sum game, an equilibrium is reached when an ideal policy is discovered.
We will demonstrate with a Code demo, how this algorithm can be used to predict Actions in a Stock market scenario.
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How to start and grow an AI Lab
20 Mins
Experience Report
Beginner
I will discuss how I started CellStrat AI Lab and what goes to make it successful as one of the leading AI research groups in India.
I will cover facets such as origination, growth, researcher motivation, content portfolio, project activity, research areas, marketing techniques and talent development pipeline.
This presentation will give an idea to corporate and academic institutions as to how they can create and nurture a world-class AI Lab within their organizations.
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Scalable and Efficient Object Detection with EfficientDet
Vivek SinghalCo-Founder & Chief Data ScientistCellStratNiraj KaleAI ResearcherCellStratschedule 2 years ago
Sold Out!20 Mins
Talk
Intermediate
There has been much research in efficient and scalable model approaches required for network design and object detection. Feature Pyramid Networks (FPN) enabled feature fusion at different scales. Path Aggregation Networks (PANet) conduct bottom-up path augmentation in an FPN, which shortens information transfer from bottom layers to topmost features. A more recent scheme, NAS-FPN (Neural Architecture Search-Feature Pyramid Network), combines RL-based NAS with an FPN to enable combination of top-down and bottom-up connections to fuse features across scales.
A recent state-of-the art model EfficientDet uses a weighted bi-directional BiFPN with multi-scale feature fusion across layers to account for learnable feature importance while applying bottom-up and top-down feature fusion. EfficientDet combines BiFPN with efficient model scaling techniques proposed in EfficientNet model, such that the baseline network, the feature network and the bounding box/class prediction networks are all scaled uniformly and efficiently using compound scaling technique across resolution/width/depth dimensions. The EfficientNet-D6 achieves top of the line accuracy (mAP) on COCO dataset with 4x lesser parameters and 13x fewer FLOPS then the recent comparable NAS-FPN model.
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Training Autonomous Driving Systems to Visualize the Road ahead for Decision Control
Vivek SinghalCo-Founder & Chief Data ScientistCellStratShreyas JagannathAI researcherCellstratschedule 3 years ago
Sold Out!90 Mins
Workshop
Intermediate
We will train the audience how to develop advanced image segmentation with FCN/DeepLab algorithms which can help visualize the driving scenarios accurately, so as to allow the autonomous driving system to take appropriate action considering the obstacle views.
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Advanced AI Application Showcase
favorite_border 19 odsc-india-2019 machine-learning-&-deep-learning Workshop 480 Mins Intermediate computer-vision image-segmentation image-processing pose-estimation densepose u-net deeplab natural-language-processing bert ner neural-language-translation sequence-models time-series reinforcement-learning driverless-cars morphnet neural-networks convolutional-neural-networks recurrent-neural-networks artificial-intelligence ai-research gaming text-analytics healthcareVivek SinghalCo-Founder & Chief Data ScientistCellStratAnshumaan DashDeep Learning Researcher- Computer VisionCellStrat AI LabsAnupam RanjanAI Research ScientistCellstratShreyas JagannathAI researcherCellstratShubha ManikarnikeDeep Reinforcement ResearcherCellstrat AI Labschedule 3 years ago
Sold Out!480 Mins
Workshop
Intermediate
In this session, data scientists from CellStrat AI Lab will present demos and presentations on cutting-edge AI solutions in :-
- Computer Vision - Image Segmentation with FCN/UNets/DeepLab/ESPNet, Image Processing, Pose Estimation with DensePose
- Natural Language Processing (NLP) - Latest NLP and Text Analytics with BERT, NER, Neural Language Translation etc to solve problems such as text summarization, QnA systems, video captioning etc.
- Reinforcement Learning (RL) - Train Atari Video Games with RL, Augmented Random Search, Deep Q Learning etc. Apply RL techniques for gaming, financial portfolios, driverless cars etc. Train Robots with MuJoCo simulator.
- Driverless Cars - Demo on multi-class roads datasets, path planning and navigation control for cars etc.
- Neural Network Architectures - Faster and Smaller Neural Networks with MorphNet
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Advances in Reinforcement Learning
45 Mins
Talk
Intermediate
This session will discuss Reinforcement Learning (RL) algorithms such as Policy Gradients, TD Learning and Deep-Q Learning. We will discuss how emerging RL algorithms can be used to train games, driverless cars, financial decision models and home automation systems.
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Applications of Generative Modeling
20 Mins
Experience Report
Intermediate
Generative Models are important techniques used in computer vision. Unlike other neural networks that are used for predictions from images, generative models can generate new images for specific objectives. This session will review several applications of generative modeling such as artistic style transfer, image generation and image translation using CNNs and GANs.
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No more submissions exist.
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No more submissions exist.