Daniel Klotz

abc: always be curious

We’re excited to introduce TiRex — a pre-trained time series forecasting model based on an xLSTM architecture.

🌍 PhD in AI in Desert Geomorphology
🔎Join a @dfg_public funded project mapping desert pavements in Namibia using AI, remote sensing & fieldwork.
🗓️Start: 01 June Apply by: 07 Feb
📍Part-time (75%), fixed-term until 31 May 2028. @geobrenning 🔗 : jobs.uni-jena.de/jobposting/1ef…

The one and only @HochreiterSepp kicks of the 2nd day of @NeurIPSConf!

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

This was a truly inspiring meeting! Thank you @JosefineUmlauft for organising! Great presentations by @ClemensMosig @BarrassoC @Eya__Cherif @taismncarvalho @m_soechting @annavonblohn @m_de_Brito @JohannesQuaas @ido87 and others not on X, but doing excellent work in @StadtLeipzig

Deep Ensembles are widely used to improve the performance of Deep Learning models. But beware, they can have profound impact on group fairness ⚖️ We analyzed why it happens and what can be done about it 🧵👇

Thrilled to share my second PhD paper in Environmental Research Letters! While most focus on spring preconditioning, our study reveals the impact of winter conditions on summer vegetation across the entire Northern Hemisphere. Read more: twtr.to/824hX

Inside WaterResearch - Issue 19: Dive into the performance of various models on the #GreatLakes—a region with complex transboundary datasets, diverse climatic and land use conditions, and immense socio-economic importance to both the USA and Canada. bit.ly/3y3izNM

Can we learn from the recent past to predict future climate change impacts using machine learning? We have created a new benchmark dataset designed to help answer this question, and you can take part in the challenge: kaggle.com/competitions/t…

Really excited to announce Energy-based Hopfield Boosting. 🎉 Hopfield Boosting advances the state-of-the-art in OOD detection. 🚀 Our new energy function allows a model to focus on hard training examples close to the decision boundary between in-distribution and OOD samples. 🧵

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

Today we published xLSTM. It is so exciting. I am very proud of my team: @maxmbeck,@KorbiPoeppel,@AndAuer, @MarkusSpanring, O. Prudnikova,@m_k_kopp,@gklambauer,@jo_brandstetter arxiv.org/abs/2405.04517

I think neural network potentials are the most important scientific tool of the next decade. The ability to simulate systems at the molecular scale starting from nothing but quantum mechanics will be transformative for a vast range of problems throughout biology and chemistry 1/n

xLSTM: Extended Long Short-Term Memory The famous LSTM nets are improved by exponential gates and matrix-memory with covariance update. Strong results on large-scale language modeling. P: arxiv.org/abs/2405.04517

We are looking for your participation in the survey “AI/ML in the #Hydrology Community” – a joint initiative by #UNESCO #IHP & its EURO-FRIEND Project-3. Link to the survey: forms.office.com/e/5bkkBypXgP Please share widely! #hydrologyML #hydrologyAI #AIMLhydrology

Nature research paper: Global prediction of extreme floods in ungauged watersheds go.nature.com/3Vrpzxj

The Italian FlOod and Catchment Atlas (FOCA), a product of the #PNRR #Return Project, is out! Free data are on 631 basins are on Zenodo (doi.org/10.5281/zenodo…). The full paper is free on ESSD (essd.copernicus.org/articles/16/15…). @Gii_Idraulica @ISPRA_Press @giornaleprociv @EGU_HS

This is something for those of you who want to become more certain about the role of unvertraut in hyd.

Large-scale global flood forecasting has been out of reach for a long time. In our Nature paper published today we show how breakthroughs in AI can close the gap & provide reliable flood predictions even in regions that previously lacked data. Learn more: goo.gle/49bRqVG