This workshop is designed to cover the latest advancements in AI techniques presented at the Neural Information Processing Systems (NeurIPS) Conference. Participants will delve deep into neural architectures, large-scale AI model optimization, and interdisciplinary AI applications in healthcare, climate science, education, and ethical AI. Hands-on sessions will focus on building and fine-tuning AI models such as transformers and graph neural networks, preparing participants for cutting-edge AI research and its practical implementation.

Aim

To empower PhD scholars and academicians with advanced AI techniques used in neural information processing systems, equipping them with the knowledge of state-of-the-art neural architectures, optimization techniques, and real-world applications in AI-driven societal impact research. This workshop focuses on cutting-edge AI models, implementation strategies, and emerging trends in AI research.

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Workshop Objectives

Understand key trends and breakthroughs in AI research from NeurIPS.
Gain deep insights into neural network architectures and optimization techniques.
Learn to apply AI for societal impact in fields like healthcare, climate science, and education.
Hands-on experience in building and optimizing advanced AI models.
Explore future directions in AI research, including quantum AI and AI safety.

Workshop Structure

Day 1:
Part 1: Introduction to Neural Information Processing Systems (NeurIPS) and AI Research (15 mins)

Overview of the Neural Information Processing Systems (NeurIPS) Conference
History and significance of NeurIPS in AI research
Key themes and breakthroughs from recent conferences
Highlighting seminal papers and transformative ideas in AI
Importance of NeurIPS in Academic Research:
Impact of NeurIPS on shaping future AI research directions
How to leverage NeurIPS research for academic projects, publications, and teaching
Learning Objective:

Understand the importance of NeurIPS in shaping AI research and its implications for academic teaching and publication.
Part 2: Deep Dive into Neural Network Architectures (25 mins)

Exploring State-of-the-Art Architectures:
Transformers and Attention Mechanisms:
Introduction to transformer architectures and their role in NLP and beyond
Case study: How transformers like BERT and GPT have transformed language processing
Graph Neural Networks (GNNs):
Understanding GNNs and their applications in complex data structures (social networks, molecules, etc.)
Examples of GNNs in scientific research and data modeling
Capsule Networks (CapsNets):
Capsule networks’ promise over traditional CNNs for hierarchical data
Recent developments and research applications of CapsNets
Research Insights and Case Studies:
Papers from NeurIPS highlighting novel neural architectures
How these architectures are applied in academic research
Learning Objective:

Develop an in-depth understanding of advanced neural network architectures and their applications in cutting-edge AI research.
Day 2:
Part 3: Optimization Techniques for Large-Scale AI Models (20 mins)

Advanced Optimization Strategies:
Adaptive Gradient Methods:
Exploration of Adam, AdaGrad, and RMSProp in enhancing model performance
Layer-wise Adaptive Rate Scaling (LARS):
Optimizing training for large-scale AI models with LARS
Stochastic Gradient Descent (SGD):
Innovations in SGD and its efficiency in handling deep learning models
Regularization and Generalization:
Advanced regularization techniques (Dropout, Batch Normalization)
Ensuring generalizability of AI models in academic research
Practical Insights from NeurIPS:
Case studies on optimizing models for large datasets and computational efficiency
How these optimization methods are revolutionizing AI research at scale
Learning Objective:

Equip participants with advanced optimization techniques to enhance the performance and scalability of neural network models in academic settings.
Part 4: AI for Societal Impact and Interdisciplinary Research (20 mins)

Real-World Applications of AI in Scientific Research:
AI in Healthcare:
Predictive analytics, diagnostics, and treatment personalization using AI
Examples of AI models detecting diseases (cancer, Alzheimer’s) from NeurIPS papers
Climate Science and Environmental Research:
Use of AI in climate modeling and environmental conservation
Case study: AI in monitoring and predicting extreme weather events
AI in Education:
Enhancing learning through AI-based adaptive learning systems
AI for educational content generation and student assessment
AI Ethics and Responsible Research:
Addressing bias, fairness, and transparency in AI models
How to integrate ethical considerations into AI research
Papers and discussions on ethics from NeurIPS
Learning Objective:

Learn how AI techniques can be applied to drive interdisciplinary research and solve global challenges, with an emphasis on ethical considerations.
Day 3:
Part 5: Hands-on Demonstration: Implementing Cutting-Edge AI Models (30 mins)

Step-by-Step Implementation of Transformer Models:
Building a transformer-based model for NLP tasks using Python and PyTorch/TensorFlow
Fine-tuning pre-trained transformer models (e.g., BERT, GPT-3)
Hands-on example: Sentiment analysis using a transformer model
Graph Neural Networks (GNNs) for Research Applications:
Introduction to building a GNN for graph-based data (e.g., chemical compounds, social networks)
Coding example: Node classification with GNNs using PyTorch Geometric
Model Evaluation and Hyperparameter Tuning:
Techniques for tuning hyperparameters for optimal model performance
Evaluating models in terms of accuracy, F1 score, and robustness
Learning Objective:

Equip participants with hands-on experience in implementing and fine-tuning advanced AI models for research purposes.
Part 6: Future Directions in AI Research (20 mins)

Exploring Emerging Trends in AI and Machine Learning:
AI safety and robustness: Techniques for building safe and secure AI systems
Interpretability and explainability: Understanding black-box models through interpretability techniques
Reinforcement learning and decision-making: Advancements in autonomous systems
Preparing for Quantum AI and Next-Gen Technologies:
Introduction to quantum computing and its potential in AI research
How AI researchers and professors can prepare for upcoming advancements in quantum AI
Actionable Steps for AI Researchers:
Publishing in top AI journals and conferences
How to stay updated with NeurIPS and other top AI research conferences
Networking with the AI research community through academic events and collaborative projects
Learning Objective:

Identify the next big areas of AI research and understand how to prepare for advancements in quantum AI, interpretability, and reinforcement learning.

Participant’s Eligibility

AI researchers, data scientists, academic professors, PhD scholars, and professionals in the field of neural information processing systems and AI research.

Important Dates

Registration Ends

2024-11-20
Indian Standard Timing 1:00 pm

Workshop Dates

2024-11-20 to 2024-11-22
Indian Standard Timing 5 PM

Workshop Outcomes

Mastery of neural network architectures like transformers, GNNs, and CapsNets.
Hands-on experience in optimizing large-scale AI models.
Practical application of AI in healthcare, climate science, and education.
Skills to address ethical concerns in AI models.
In-depth understanding of the future trends in AI research and quantum AI.

Mentor Profile

Name: DR. LOVLEEN GAUR
Designation: Professor
Affiliation:

Dr. Lovleen Gaur is a Professor, Amity University, Deactivatedida, India, Adjunct Professor, School of Computer Science, Taylor’s University, Malaysia and Adjunct Professor, Graduate Business School, University of the South Pacific, Fiji. She has received a Ph.D. in computer application from Jodhpur National University in 2011. Then worked as an Professor at three major National/ International Universities of repute. She is having total approx 19 Years of Experience in the field of advanced-era specialized courses, including Predictive Analytics, Data Visualization, Social Network Analytics, Deep Learning, Power BI, Digital Marketing and Digital InDeactivatedvation. Her Area of Expertise includes: Artificial Intelligence, Machine Learning, Pattern Recognition, Internet of Things, Data Analytics and Business Intelligence. She has introduced courses related to the Artificial Intelligence specialization including, Predictive Analytics, Deep and Reinforcement learning etc. To date, she is the author of 33 research articles published in peer reviewed international Journals, author of 17 Book, 28 Conference Papers, and 29 Book Chapters etc.

Fee Structure

Student

INR. 1499
USD. 40

Ph.D. Scholar / Researcher

INR. 1999
USD. 45

Academician / Faculty

INR. 2999
USD. 50

Industry Professional

INR. 4999
USD. 75

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