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I’m Jyo, a PhD student at MIT with a broad interest in intelligence. I explore this topic from various perspectives, focusing on how models can continually learn through advances in architecture and optimization.
▸ Alongside writing educational posts, I’ll also share more refined brainstorming articles as part of an open-science effort to encourage collaboration. If any idea resonates with you and you’d like to explore it further, feel free to reach out!
▸ Also, check out Scale-ML a student led MIT organization focused on scaling in deep learning

Discrete Optimal Transport

An exploration of discrete optimal transport methods and their applications in machine learning...

Distances Between Subspaces

Grassman Metric

Euler-Lagrange Equation

Euler-Lagrange equation and its significance in calculus of variations...

Gumbel Softmax

An overview of the Gumbel-Softmax distribution and its utility in differentiable sampling...

Langevin Sampling

Exploring Langevin dynamics as a method for sampling from complex distributions...

Model Merging

Techniques and challenges in merging multiple machine learning models into a cohesive system...

Symmetries in Neural Networks

Understanding how symmetry in networks can improve optimization...

Centered Kernel Alignment

How can we compare representations between networks...