Interpretability Tools as Feedback Loops in Machine Learning Training
Explore machine learning interpretability techniques to optimize training sequences and develop more effective models without increasing compute or training time.
Helping Canadians Live Life Well Through Data-driven Innovation
Explore how Loblaw leverages data for forecasting, personalization, and supply chain optimization to enhance Canadian retail experiences.
Machine Learning for E-commerce Product Discovery - Finding a Needle in a Haystack
Explore machine learning techniques for efficient e-commerce product discovery, addressing challenges and leveraging NLP, image recognition, and knowledge graphs.
So Moore's Law is Dead - Now What? The Future of AI Processing
Explore the future of AI processing beyond Moore's Law, focusing on innovative architectures designed for demanding ML workloads in finance and other sectors.
Machine Learning on the Edge - From Microcontrollers to Embedded Linux Devices
Explore ML deployment on edge devices, from microcontrollers to embedded Linux systems. Learn about hardware options, model adaptation, and deployment strategies for real-world applications.
The Secret Sauce to ML and AI-Powered Products
Discover the key to building successful ML and AI-powered products through customer-centric solutions and diverse talent. Learn to fuel innovation and make an impact in your ML career.
Bogged Down by Annotation - Why Subject Matter Experts Should Do the Heavy Lifting
Discover why subject matter experts should handle data annotation, improving efficiency and accuracy in machine learning projects.
Human Interpretable Machine Learning for Smart Water Management
Explore human interpretable machine learning for smart water management, addressing challenges in aging infrastructure, urbanization, and climate change through AI-driven solutions.
Capsule Network-based Framework for Identification of COVID Cases from X-ray Images
Explore a Capsule Network framework for COVID-19 detection in X-ray images, addressing small dataset challenges and outperforming CNN models.
Prompt Optimization and Parameter Efficient Fine Tuning for Large Language Models
Explore state-of-the-art prompt optimization techniques and parameter efficient fine-tuning methods for large language models to enhance performance on various tasks.
Machine Learning-Driven Phenotyping Platform for Rapid In Vivo Target Validation and Precision Medicine
Develop a zebrafish phenotyping platform using 3D-printed mounts and computer vision for drug discovery, enabling rapid gene-to-phenotype analysis and accelerating clinical translation.
ML in Production: Implementation, Tooling and Engineering - Data MLOps
Explore ML observability in e-commerce, focusing on Loblaw Digital's approach to tracking model performance, user behavior, and business impact using centralized tools and analytics.
OpenFL - A Federated Learning Project to Power and Secure Your Projects
Discover OpenFL: Secure federated learning framework for collaborative model training without sharing sensitive data. Adopt, contribute, and enhance AI projects with privacy-preserving techniques.
Demystifying Large Language Models - ChatGPT, GPT-4, and the Future of AI Communication
Explore the inner workings of ChatGPT and GPT-4, their practical applications, and societal implications. Gain insights into how these AI models are shaping the future of communication and problem-solving.
Efficient Inference of Extremely Large Transformer Models
Explore techniques for optimizing large transformer models, including compression, efficient attention, and parallelism for faster, smaller, and cost-effective inference.
Sensitivity and Interpretability of AI Models in Regulated Industries
Explore sensitivity analysis for AI model interpretability in regulated industries. Learn design and implementation approaches using case studies from healthcare and human activity data.
Managing AI and Associated Risks in a Regulatory Environment
Explore AI's evolution, regulatory trends, and risk management practices in high-stakes industries with expert insights on navigating the complex AI landscape.
Latent User Intent Modeling in Recommender Systems
Explore latent variable models for capturing user intents in recommender systems, enhancing understanding beyond item-level interactions.
Declarative Machine Learning Systems - Ludwig and Predibase
Explore declarative machine learning systems, focusing on Ludwig framework and Predibase enterprise solution for flexible, simplified ML infrastructure.
Deep Learning-Based Lung Ultrasound Video Classification to Predict COVID-19
Explore deep learning methods for COVID-19 detection using lung ultrasound videos, focusing on results, methodology, and explainability of the approach.
Federated Learning in Production: Enabling AI Training with Data Privacy
Explore federated learning's applications, benefits, and implementation requirements for AI training on edge devices with data privacy.