Recursive Cortical Network
George, Lavin, et al. Cortical Microcircuits from a Generative Vision Model. CCN 2018. /// paper
Hierarchical Temporal Memory
Hawkins, Lavin, et al. Biological and Machine Intelligence. 2016. /// book
Data-efficient computer vision
Naud & Lavin (2020), Manifolds for Unsupervised Visual Anomaly Detection. /// paper
Lavin (2020), Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels. /// paper
Lavin (2020), Probabilistic Programmed Causal Inference.
Lavin & Renard. Technology Readiness Levels for Machine Learning Systems. ICML 2020 Workshop on Challenges in Deploying ML Systems. /// paper
Lippoldt & Lavin. Attention-Sampling Graph Convolutional Networks. 2020 BayLearn Symposium.
Haney & Lavin (2020). Probabilistic Domain-Expert Hyperspherical Networks.
Lavin (2020), Accelerating Gaussian Processes and Deep Kernel Networks on GPUs. /// video
Lavin (2019), Predictive Modeling Neurodegenerative Processes with a Domain-Specific Probabilistic Programming Language.
Lavin. Integration of human-like covert-overt attention with probabilistic neural networks. Neuroscience to Artificially Intelligent Systems (NAISys) 2020. /// abs
AI & innovation articles
I contribute articles to Forbes on AI & ML, discussing important intersections with these technologies and society.
This is not a time for an international arms race in AI. Rather, it is a time for collaboration across governments, corporate R&D, academia, and startups. It is a time to push the frontier of AI and space for climate science and humankind.
Machine learning systems can be massively useful, but also fail critically in unforeseen ways. We need systems engineering in synergy with experimentation and innovation.
Healthcare is ripe for the power of machine learning, but only with the scientific rigor of causal reasoning.
Black-box AI can be extremely powerful yet difficult to understand and trust. White-box AI is explainable and insightful, but sometimes at the cost of predictive power. How do we mend the gap?
Causal reasoning is a necessary ingredient to human-level artificial intelligence. We’re not there, yet.
On brains n bits, AI, and SW. For example,
- “Python for AI Research” on Talk Python to Me Podcast
- “Machine Learning in Medicine: Priorities for the Research to Product Gap” lecture at Cornell Medicine: video, slides
- “How The Brain is Inspiring AI” at Forbes 30 Under 30 summit
- “Accelerating Gaussian Processes and Deep Kernel Networks on GPUs” for NVIDIA GTC: video, slides
- Expert panel on AI ethics at the 2020 ICML Workshop on Challenges Deploying ML Systems.
- This 2016 TEDX talk at Cornell Tech on artifical and biological neurons: