🚀 Excited to share our latest research on quantifying the predictive uncertainty of machine learning models. QUAM searches for adversarial models (not adversarial examples!) to better estimate the epistemic uncertainty, the uncertainty about chosen model parameters. 1/5

Super paper on reliable prediction for time series at NeurIPS. Conformal Predictions adapted to time series yields excellent results.

Time series come with tradition at Sepp Hochreiter's institute - Andreas Auer puts a new perspective on conformal predictions for time series data - great work to appear NeurIPS Conference !

Interested in a semantic memory for reinforcement learning? I was recently invited to a podcast talking about our #NeurIPS2023 paper: Semantic HELM (arxiv.org/abs/2306.09312). In case you are interested, you can stream the episode here: open.spotify.com/episode/4n2lmC…

xLSTM is out -- putting LSTM networks on steroids to become a more than serious LLM competitor. How? Via exponential gating and enhanced (cell state) memory capacities. Does it work? Oh, yeah 🚀🚀 arxiv.org/abs/2405.04517

I am so excited that xLSTM is out. LSTM is close to my heart - for more than 30 years now. With xLSTM we close the gap to existing state-of-the-art LLMs. With NXAI we have started to build our own European LLMs. I am very proud of my team. arxiv.org/abs/2405.04517

The #xLSTM is finally live! What an exciting day! How far do we get in language modeling with the LSTM compared to State-of-the-Art LLMs? I would say pretty, pretty far! How? We extend the LSTM with Exponential Gating and parallelizable Matrix Memory! arxiv.org/pdf/2405.04517

Introducing Vision-LSTM - making xLSTM read images 🧠It works ... pretty, pretty well 🚀🚀 But convince yourself :) We are happy to share code already! 📜: arxiv.org/abs/2406.04303 🖥️: nx-ai.github.io/vision-lstm/ All credits to my stellar PhD Benedikt Alkin

Hey there 👋 Want to do a Phd with me? Apply: it-u.at/en/study-progr…