arXiv:2307.07785v2 Announce Type: replace Abstract: The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset. Classical information criteria typically consider the large-data limit, penalizing model size. However, these criteria are not appropriate in modern settings where overparameterized models tend to perform well. For any overparameterized model, we show that there exists a dual underparameterized model that possesses the same marginal likelihood, thus establishing […]

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In the high-stakes world of AI infrastructure, the industry has operated under a singular assumption: flexibility is king. We build general-purpose GPUs because AI models change every week, and we need programmable silicon that can adapt to the next research breakthrough. But Taalas, the Toronto-based startup thinks that flexibility is exactly what’s holding AI back. According to Taalas team, if we want AI to be as common and cheap as plastic, we have to stop ‘simulating’ intelligence on […]

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arXiv:2602.14286v1 Announce Type: cross Abstract: Watermarking for large language models (LLMs) has emerged as an effective tool for distinguishing AI-generated text from human-written content. Statistically, watermark schemes induce dependence between generated tokens and a pseudo-random sequence, reducing watermark detection to a hypothesis testing problem on independence. We develop a unified framework for LLM watermark detection based on e-processes, providing anytime-valid guarantees for online testing. We propose various methods to construct empirically adaptive e-processes that can enhance the detection […]

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Today, it might be easy to grow accustomed to the idea that more data equals better insights. Companies can track every click through the sales funnel, analyze product usage, and scrape public websites to gather leads or competitor insights. However, 68% of data, according to research done by IDC for Seagate, sits unused, growing stale by the day. But as any data engineer will tell you, collecting data is only the first step. Cleaning, deduplicating, structuring, and integrating […]

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arXiv:2306.10767v2 Announce Type: replace Abstract: Several recent papers have proposed increasing the expressive power of graph neural networks by exploiting subgraphs or other topological structures. In parallel, researchers have investigated higher order permutation equivariant networks. In this paper we tie these two threads together by providing a general framework for higher order permutation equivariant message passing in subgraph neural networks. In this paper we introduce a new type of mathematical object called $P$-tensors, which provide a simple way […]

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