Fast Language Generation by Finetuning Pretrained Transformers
Explore efficient language generation by converting pretrained transformers to RNNs, improving speed without sacrificing accuracy through a swap-then-finetune approach.
Artificial Intelligence for Molecular Design and Self-Driving Labs
Explore AI and automation for designing functional molecular materials, focusing on recent advancements in the field.
Financial Client Churn Detection Using Computer Vision
Detect financial client churn using computer vision techniques. Convert historical client data into images and apply deep neural networks for predictive analysis.
Opportunities at the Intersection of Financial Policy and Machine Learning
Explore ML's role in finance, from COVID-19 impacts to lending applications, and its potential for promoting financial stability and sustainable business models.
Quantum-Assisted Machine Learning with Near-Term Quantum Devices
Explore quantum-assisted machine learning applications for near-term devices, focusing on generative models in unsupervised and semi-supervised learning. Discover recent experimental implementations.
How to Automate Machine Learning with GitHub Actions
Automate machine learning workflows using GitHub Actions to speed up development cycles and streamline model deployment.
Reinforcement Learning via Stochastic Control
Explore reinforcement learning in continuous time and space, examining stochastic control theory's role and recent developments in this emerging field.
Machine Learning for Trust and Safety at Pinterest
Explore Pinterest's ML solutions for Trust and Safety, including DNN, CNN, and GCN techniques. Learn about challenges, design principles, and processes for developing and deploying effective content moderation systems.
Simplifying AI DevOps with Model Registry
Discover strategies to streamline ML model deployment, ensure reproducibility, and enhance governance using Model Registry for efficient AI DevOps.
A Cookbook for Deep Continuous-Time Predictive Models
Explore deep continuous-time predictive models for irregularly-sampled time series, from feedforward approaches to latent-variable stochastic differential equations.
Privacy-Preserving Machine Learning
Explore strategic approaches to privacy-preserving machine learning, including federated learning, encryption, and differential privacy, with practical examples and risk considerations.
A.I. in the Multiverse - Measuring ROI When A/B Tests Are Not Possible
Explore deep learning techniques for assessing AI's revenue impact in e-commerce without A/B testing. Learn to measure and monitor ML pipeline performance in various business settings.
Assessing China's AI Capabilities - Overview and Deep Dive into Neural Networks Research
Explore China's AI ecosystem and delve into its neural network research capabilities, gaining insights into the country's technological advancements.
Overcoming the Cold Start Problem: How to Make New Tasks Tractable
Explore strategies to overcome the cold start problem in AI, focusing on data aggregation and leveraging pre-trained models for efficient scaling of machine learning applications.
Covid-19 Impact on AI in Banking
Explore how AI in banking adapted to the radical changes brought by the COVID-19 pandemic, including economic shifts and new work environments.
How Finnish Public Broadcaster Yle is Beating Netflix with AI-Driven Media Experiences
Explore how AI is shaping media experiences and learn strategies for implementing data-driven solutions in organizations to stay competitive in the streaming industry.
AI and Machine Learning in Healthcare During the Coronavirus Pandemic in China
Explore machine learning applications in healthcare during China's coronavirus pandemic, focusing on practical implementations and outcomes.
Fighting Pipeline Debt With Great Expectations
Learn to combat pipeline debt using Great Expectations, an open-source library for data validation, documentation, and profiling in complex data systems.
Abstraction and Analogy in Natural and Artificial Intelligence
Explore the role of analogy-making in conceptual abstraction and understanding, and its importance for developing AI systems with humanlike intelligence.
How F.A.T. is Your ML Model Quality in the Era of Software
Explore F.A.T. (Fairness, Accountability, Transparency) properties for assessing ML model quality. Learn evaluation approaches, governance benchmarks, and bias mitigation techniques through case studies.
Hyperparameter Tuning with a Focus on Weights and Biases Sweeps
Explore hyperparameter tuning strategies and visualizations for deep learning models, focusing on Weights & Biases Sweeps. Accelerate progress in semantic segmentation and language understanding projects.