A summary of my personal opinions on world models in 2025, covering their current state, future prospects, and implications for embodied AI. Discusses 3D modeling approaches, data challenges, research directions, and the role of JEPA-style architectures in the evolution of world models.
Lecture notes and Cheat Sheet for Course: Algebra and Computation, which is a course offered to Freshman students at Yao Class.
This article introduces Titans, a novel architecture that as a meta in-context learner, learns to memorize at test time. Through designing a long-term memory module, and proposing three variants of Titans (MAC, MAG, MAL), the model achieves superior performance compared to Transformers and other baselines, especially in long-context tasks.
This is the first article in the Life Hacks Series. It covers how to manage your Python environment. Basically, it covers how to install packages, how to create a new environment, how to clone an environment, and how to pack an environment. A special mention is Conda-Pack, which really made my life a lot easier.
This is the fifth article in the Machine Learning Series. It covers classic approaches to Hyperparameter Selection, including Bayesian Optimization, Gradient Optimization, Random Search, Multi-Arm Bandits and Neural Architecture Search.
This is the fourth article in the Machine Learning Series. It covers classic approaches to Robust Machine Learning, including Adversial Attacks, Adversial Training, Robust Features, Obfuscated Gradients and Provable Robust Certificates.
This is the third article in the Machine Learning Series. It covers the second part of unsupervised learning, including topics like Clustering, Spectral Graph Clustering, SimCLR, SNE and t-SNE.
This is the second article in the Machine Learning Series. It covers the first part of unsupervised learning, including topics like Dimension Reduction, PCA, k-NN, LSH and Metric Learning.
This is the second part of the Natural Language Processing Series. It covers modern approaches in natural language processing, including RNNs, VAE-LMs, Transformer, BERT, GPT, GAN-LMs, In-Context Learning, CoT, RLHF, DPO, etc.
This is the first article in the Machine Learning Series. It covers the basics of optimization(GD,SGD,SVRG,Mirror Descent,Linear Coupling), generalization(No Free Lunch, PAC Learning, VC Dimension), and supervised learning(Linear Regression, Logistic Regression, Compressed Sensing).