Daniel Klotz

🏷️ ml + earth @ UFZ & Google
🔬 measuring what the models can and can’t.

[Daniel Klotz @ido87]

abc: always be curious

[Andreas Auer @AndAuer]

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

[ELLIS Unit Jena @Ellis_Unit_Jena]

🌍 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
🔗 : https://t.co/Oqu2jK7tPu

[Jakub Tomczak @jmtomczak]

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

[Daniel Klotz @ido87]

Hey there 👋

Want to do a Phd with me? Apply: https://t.co/da5Wb0yagW

[Miguel Mahecha @MiguelDMahecha]

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

[Kajetan Schweighofer @kschweig_]

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 🧵👇

[Daniel Klotz @ido87]

Hey there 👋

Want to do a Phd with me? Apply: https://t.co/da5Wb0yagW

[Mohit Anand @mohitanand]

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: https://t.co/7JZ1Had0sU

[the Water Institute @water_institute]

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.

https://t.co/mSbiR0T2Su

[Lily-belle Sweet @lilybellesweet]

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: https://t.co/jMzy5tQviz

[Claus @ClausHofm]

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. 🧵

[Sepp Hochreiter @HochreiterSepp]

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. https://t.co/IH7giCe3gd

[Sepp Hochreiter @HochreiterSepp]

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
https://t.co/IH7giCe3gd

[Tim Duignan @TimothyDuignan]

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

[Günter Klambauer @gklambauer]

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: https://t.co/ajBENM2y1u

[Nilay Dogulu @DoguluNilay]

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: https://t.co/FyrpCfuFz0 Please share widely! #hydrologyML #hydrologyAI #AIMLhydrology

[nature @Nature]

Nature research paper: Global prediction of extreme floods in ungauged watersheds https://t.co/erVExqH4l8

[WaterTO @WaterT_O]

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 (https://t.co/pCWUnZwHZb). The full paper is free on ESSD (https://t.co/aM3XLFx2Q2). @Gii_Idraulica @ISPRA_Press @giornaleprociv @EGU_HS

[Daniel Klotz @ido87]

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

[HESS Journal @EGU_HESS]

Discussion open for comments: A data-centric perspective on the information needed for hydrological uncertainty predictions https://t.co/eIUnHlncdv

[Google AI @GoogleAI]

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: https://t.co/A35HyTczZr