Kajetan Schweighofer

Introducing TiRex - xLSTM based time series model | NXAI TiRex model at the top 🦖 We are proud of TiRex - our first time series model based on #xLSTM technology. Key take aways: 🥇 Ranked #1 on official international leaderboards ➡️ Outperforms models ...

TiRex 🦖 time series xLSTM model ranked #1 on all leaderboards.

➡️ Outperforms models by Amazon, Google, Datadog, Salesforce, Alibaba

➡️ industrial applications

➡️ limited data

➡️ embedded AI and edge devices

➡️ Europe is leading

Code: lnkd.in/eHXb-XwZ
Paper: lnkd.in/e8e7xnri

shorturl.at/jcQeq

Happy to introduce 🔥LaM-SLidE🔥!

We show how trajectories of spatial dynamical systems can be modeled in latent space by

--> leveraging IDENTIFIERS.

📚Paper: arxiv.org/abs/2502.12128
💻Code: github.com/ml-jku/LaM-S...
📝Blog: ml-jku.github.io/LaM-SLidE/
1/n

1/11 Excited to present our latest work "Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics" at #ICLR2025 on Fri 25 Apr at 10 am!
#CombinatorialOptimization #StatisticalPhysics #DiffusionModels

⚠️ Beware: Your AI assistant could be hijacked just by encountering a malicious image online!

Our latest research exposes critical security risks in AI assistants. An attacker can hijack them by simply posting an image on social media and waiting for it to be captured. [1/6] 🧵

X-IL: Exploring the Design Space of Imitation Learning Policies Designing modern imitation learning (IL) policies requires making numerous decisions, including the selection of feature encoding, architecture, policy representation, and more. As the field rapidly a...

Exploration imitation learning architectures: Transformer, Mamba, xLSTM: arxiv.org/abs/2502.12330
*LIBERO: “xLSTM shows great potential”
*RoboCasa: “xLSTM models, we achieved success rate of 53.6%, compared to 40.0% of BC-Transformer”
*Point Clouds: “xLSTM model achieves a 60.9% success rate”

Ever wondered why presenting more facts can sometimes *worsen* disagreements, even among rational people? 🤔

It turns out, Bayesian reasoning has some surprising answers - no cognitive biases needed! Let's explore this fascinating paradox quickly ☺️

Often LLMs hallucinate because of semantic uncertainty due to missing factual training data. We propose a method to detect such uncertainties using only one generated output sequence. Super efficient method to detect hallucination in LLMs.

Rethinking Uncertainty Estimation in Natural Language Generation Large Language Models (LLMs) are increasingly employed in real-world applications, driving the need to evaluate the trustworthiness of their generated text. To this end, reliable uncertainty estimatio...

𝗡𝗲𝘄 𝗣𝗮𝗽𝗲𝗿 𝗔𝗹𝗲𝗿𝘁: Rethinking Uncertainty Estimation in Natural Language Generation 🌟

Introducing 𝗚-𝗡𝗟𝗟, a theoretically grounded and highly efficient uncertainty estimate, perfect for scalable LLM applications 🚀

Dive into the paper: arxiv.org/abs/2412.15176 👇

🔊 Super excited to announce the first ever Frontiers of Probabilistic Inference: Learning meets Sampling workshop at #ICLR2025 @iclr-conf.bsky.social!

🔗 website: sites.google.com/view/fpiwork...

🔥 Call for papers: sites.google.com/view/fpiwork...

more details in thread below👇 🧵

Just 10 days after o1's public debut, we’re thrilled to unveil the open-source version of the technique behind its success: scaling test-time compute

By giving models more "time to think," Llama 1B outperforms Llama 8B in math—beating a model 8x its size. The full recipe is open-source!

Proud to announce our NeurIPS spotlight, which was in the works for over a year now :) We dig into why decomposing aleatoric and epistemic uncertainty is hard, and what this means for the future of uncertainty quantification.

📖 arxiv.org/abs/2402.19460 🧵1/10

Cool work!

Thrilled to share our NeurIPS spotlight on uncertainty disentanglement! ✨ We study how well existing methods disentangle different sources of uncertainty, like epistemic and aleatoric. While all tested methods fail at this task, there are promising avenues ahead. 🧵 👇 1/7

📖: arxiv.org/abs/2402.19460

The Machine Learning for Molecules workshop 2024 will take place THIS FRIDAY, December 6.

Tickets for in-person participation are "SOLD" OUT.

We still have a few free tickets for online/virtual participation!

Registration link here: moleculediscovery.github.io/workshop2024/

🙌

Would love to join, working on Bayesian ML

Very cool Michael, congrats! 😄