1 citations · 3 across the 10 of their papers we have counts for
13 papers
VoiceNet: Fine-Grained Voice Understanding Beyond Emotion at Scale
Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby +7
Expressive speech synthesis has outpaced expressive speech perception: systems now render fine-grained vocal performances that no public benchmark can score. Most benchmarks for th…
Tydra: An Efficient Hybrid Model for Tabular Data
Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus +2
Transformer-based tabular foundation models such as TabPFN achieve strong predictive performance but incur quadratic computational cost with context length. On the other hand, subq…
KletterMix: Climbing Toward High-Quality German Pretraining Data - The Full Report
Maurice Kraus, Ruben Härle, Sebastian Sztwiertnia +5
High-quality pretraining data is a central ingredient in modern language models, but German-language resources remain far less developed than their English counterparts: they are o…
A Sovereign, Open-Source Foundation Model for German and English
Soofi-Team, :, Benedikt Droste +30
We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and English. Its hybrid design activates only 3B…
Recursive Inference Machines for Neural Reasoning
Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus +2
Neural reasoners such as Tiny Recursive Models (TRMs) solve complex problems by combining neural backbones with specialized inference schemes. Such inference schemes have been a ce…
QuAnTS: Question Answering on Time Series
Felix Divo, Maurice Kraus, Anh Q. Nguyen +5
Text offers intuitive access to information. This can, in particular, complement the density of numerical time series, thereby enabling improved interactions with time series model…
EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection
Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby +6
Speech emotion recognition (SER) systems are constrained by existing datasets that typically cover only 6-10 basic emotions, lack scale and diversity, and face ethical challenges w…
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models
Mehdi Ali, Manuel Brack, Max Lübbering +15
High-quality multilingual training data is essential for effectively pretraining large language models (LLMs). Yet, the availability of suitable open-source multilingual datasets r…
EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition
Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby +7
Effective human-AI interaction relies on AI's ability to accurately perceive and interpret human emotions. Current benchmarks for vision and vision-language models are severely lim…
Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation
David Steinmann, Felix Divo, Maurice Kraus +4
Shortcuts, also described as Clever Hans behavior, spurious correlations, or confounders, present a significant challenge in machine learning and AI, critically affecting model gen…
xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
Maurice Kraus, Felix Divo, Devendra Singh Dhami +1
Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal a…
United We Pretrain, Divided We Fail! Representation Learning for Time Series by Pretraining on 75 Datasets at Once
Maurice Kraus, Felix Divo, David Steinmann +2
In natural language processing and vision, pretraining is utilized to learn effective representations. Unfortunately, the success of pretraining does not easily carry over to time…
Right on Time: Revising Time Series Models by Constraining their Explanations
Maurice Kraus, David Steinmann, Antonia Wüst +2
Deep time series models often suffer from reliability issues due to their tendency to rely on spurious correlations, leading to incorrect predictions. To mitigate such shortcuts an…