Methods, systems and one piece of hardware. Each entry links to the paper, the code, or a demo you can run.
We reveal that transformers trained autoregressively naturally encode causal structures — gradient attributions directly recover underlying causal graphs without any explicit causal objectives.
A novel reinforcement learning framework that enhances generalization to unseen environments through language-guided compositional causal components. Accepted at ICLR 2025.
An LLM-powered autonomous agent that automates the entire causal analysis pipeline — from algorithm selection to report generation — making advanced causal methods accessible to researchers across all domains.
Learn to discover causality inside a large complex system without human prior — outperforming human-designed, domain-agnostic methods on the MOS 6502 microprocessor, the NetSim fMRI dataset and the Dream3 gene dataset.
A millisecond-level phase locked neural feedback system based on OpenBCI for real-time alpha wave regulation, integrating acquisition, phase estimation and stimulation on one chip.
A robust deep learning pipeline for segmenting neuronal cells in microscopy images — Cascade Mask R-CNN X152 with semi-supervised pseudo-labelling and cascade IoU fusion, reaching top 1% on Kaggle.
Paper
Code
Demo
Paper
Video
Paper
Write-up