How Prankster Oobah Butler Convinced Venture Capitalists to Give Him Over $1 Million
In his latest stunt documentary, British journalist Oobah Butler exposes the ridiculousness of internet hustle culture.
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How Prankster Oobah Butler Convinced Venture Capitalists to Give Him Over $1 Million
In his latest stunt documentary, British journalist Oobah Butler exposes the ridiculousness of internet hustle culture.
How Prankster Oobah Butler Convinced Venture Capitalists to Give Him Over $1 Million
In his latest stunt documentary, British journalist Oobah Butler exposes the ridiculousness of internet hustle culture.
How Prankster Oobah Butler Convinced Venture Capitalists to Give Him Over $1 Million
In his latest stunt documentary, British journalist Oobah Butler exposes the ridiculousness of internet hustle culture.
MIRA: Medical Time Series Foundation Model for Real-World Health Data
arXiv:2506.07584v5 Announce Type: replace Abstract: A unified foundation model for medical time series — pretrained on open access and ethics board-approved medical corpora — offers the potential to reduce annotation burdens, minimize model customization, and enable robust transfer across clinical…
Don’t be lazy: CompleteP enables compute-efficient deep transformers
arXiv:2505.01618v3 Announce Type: replace Abstract: We study compute efficiency of LLM training when using different parameterizations, i.e., rules for adjusting model and optimizer hyperparameters (HPs) as model size changes. Some parameterizations fail to transfer optimal base HPs (such as learning…
Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator
arXiv:2505.16690v4 Announce Type: replace Abstract: Post-training of large language models is essential for adapting pre-trained language models (PLMs) to align with human preferences and downstream tasks. While PLMs typically exhibit well-calibrated confidence, post-trained language models (PoLMs) often suffer from over-confidence,…
A Principle of Targeted Intervention for Multi-Agent Reinforcement Learning
arXiv:2510.17697v2 Announce Type: replace-cross Abstract: Steering cooperative multi-agent reinforcement learning (MARL) towards desired outcomes is challenging, particularly when the global guidance from a human on the whole multi-agent system is impractical in a large-scale MARL. On the other hand, designing…
Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes
arXiv:2502.03393v5 Announce Type: replace Abstract: Accurate epidemic forecasting is crucial for outbreak preparedness, but existing data-driven models are often brittle. Typically trained on a single pathogen, they struggle with data scarcity during new outbreaks and fail under distribution shifts caused…
On the Robustness of Kernel Goodness-of-Fit Tests
arXiv:2408.05854v5 Announce Type: replace-cross Abstract: Goodness-of-fit testing is often criticized for its lack of practical relevance: since “all models are wrong”, the null hypothesis that the data conform to our model is ultimately always rejected as the sample size grows.…