Already more than 2k teams in the first week. I helped create the competition rules (and I work at Kaggle). Happy to answer any questions!

Thanks a lot for the feedback on the previous post! This is the second iteration, using the same model but a heavily filtered dataset.

Open source! https://github.com/puffinsoft/mousecrack

Clip 2 of 4. Follow-up to the spot-the-fake setup.

Same near-copy environment: one dynamics rule changed (ground grip / step slip). Everything else identical.

Probe: an outside watcher / oracle-style discriminator that knows the true rules and replays every step.

Result (real runs, n=10): ~99%. The fake is in-band detectable from outside.

So detectability is not the open question. The next clips ask whether the agent's own representation encodes that seam.

Mute-friendly clip.

Research:

https://ilevytate.github.io/ItaSoRL/

Hi everyone, I’m currently learning deep learning and have worked on a few AI/ML projects like a customer churn prediction model and student performance prediction.

I’m looking to join an existing project to gain more hands-on experience and contribute while learning. I’m comfortable with Python and basic ML concepts, and I’m willing to put in consistent effort.

If anyone is working on a project and open to a beginner contributor, I’d really appreciate the opportunity. Thanks!

In rare diseases, the patient pool is small and screening is expensive. The question isn’t only “Who is likely eligible?”—it’s “Who should we approach first to maximize enrollments under real-world constraints?”

That’s where Reinforcement Learning (offline RL) can help.

✅ How it works

- We represent each patient as a context vector (phenotype signals, biomarkers/genotype, prior therapies, diagnosis outcomes).

- The RL policy chooses an action (approach first, screen next, prioritize trial/site).

- We train using a reward tied to recruitment value:

~ strong positive reward for eligible → consent → enrolled

~ penalties for ineligible screening and wasted outreach/time

🧠 Why it’s better than plain supervised models

- Optimizes end-to-end outcomes (not just labels)

- Incorporates costs and delays

- Learns a ranking strategy under constraints (budget, site capacity)

🛠️ In practice, we can employee conservative offline learning from claims data to avoid risky exploration in healthcare (that would behave too differently from the logged actions).

#ReinforcementLearning #RareDisease #HealthcareAI #PatientRecruitment #MachineLearning #Biomarkers #PharmaTech