75 citations · 394 across the 149 of their papers we have counts for
52 papers · 1 filter
EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applications
Ard Kastrati, Josua Bürki, Jonas Lauer +3
We introduce a unified benchmarking framework focused on evaluating EEG-based foundation models in clinical applications. The benchmark spans 11 well-defined diagnostic tasks acros…
Adapting Neural Audio Codecs to EEG
Ard Kastrati, Luca Lanzendörfer, Riccardo Rigoni +2
EEG and audio are inherently distinct modalities, differing in sampling rate, channel structure, and scale. Yet, we show that pretrained neural audio codecs can serve as effective…
Steering Pretrained Drafters during Speculative Decoding
Frédéric Berdoz, Peer Rheinboldt, Roger Wattenhofer
Speculative decoding accelerates language model inference by separating generation into fast drafting and parallel verification. Its main limitation is drafter-verifier misalignmen…
How Many Tokens Do 3D Point Cloud Transformer Architectures Really Need?
Tuan Anh Tran, Duy M. H. Nguyen, Hoai-Chau Tran +7
Recent advances in 3D point cloud transformers have led to state-of-the-art results in tasks such as semantic segmentation and reconstruction. However, these models typically rely…
Scalable Evaluation and Neural Models for Compositional Generalization
Giacomo Camposampiero, Pietro Barbiero, Michael Hersche +2
Compositional generalization-a key open challenge in modern machine learning-requires models to predict unknown combinations of known concepts. However, assessing compositional gen…
I-RAVEN-X: Benchmarking Generalization and Robustness of Analogical and Mathematical Reasoning in Large Language and Reasoning Models
Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer +2
We introduce I-RAVEN-X, a symbolic benchmark designed to evaluate generalization and robustness in analogical and mathematical reasoning for Large Language Models (LLMs) and Large…
Inductive Transfer Learning for Graph-Based Recommenders
Florian Grötschla, Elia Trachsel, Luca A. Lanzendörfer +1
Graph-based recommender systems are commonly trained in transductive settings, which limits their applicability to new users, items, or datasets. We propose NBF-Rec, a graph-based…
SAO-Instruct: Free-form Audio Editing using Natural Language Instructions
Michael Ungersböck, Florian Grötschla, Luca A. Lanzendörfer +3
Generative models have made significant progress in synthesizing high-fidelity audio from short textual descriptions. However, editing existing audio using natural language has rem…
EuroSpeech: A Multilingual Speech Corpus
Samuel Pfisterer, Florian Grötschla, Luca A. Lanzendörfer +2
Recent progress in speech processing has highlighted that high-quality performance across languages requires substantial training data for each individual language. While existing…
A Deterministic Polylogarithmic Competitive Algorithm for Matching with Delays
Marc Dufay, Roger Wattenhofer
In the online Min-cost Perfect Matching with Delays (MPMD) problem, requests in a metric space are submitted at different times by an adversary. The goal is to match all reques…
High-Fidelity Speech Enhancement via Discrete Audio Tokens
Luca A. Lanzendörfer, Frédéric Berdoz, Antonis Asonitis +1
Recent autoregressive transformer-based speech enhancement (SE) methods have shown promising results by leveraging advanced semantic understanding and contextual modeling of speech…
Multi-bit Audio Watermarking
Luca A. Lanzendörfer, Kyle Fearne, Florian Grötschla +1
We present Timbru, a post-hoc audio watermarking model that achieves state-of-the-art robustness and imperceptibility trade-offs without training an embedder-detector model. Given…
Bias beyond Borders: Global Inequalities in AI-Generated Music
Ahmet Solak, Florian Grötschla, Luca A. Lanzendörfer +1
While recent years have seen remarkable progress in music generation models, research on their biases across countries, languages, cultures, and musical genres remains underexplore…
Efficient Bayesian Inference from Noisy Pairwise Comparisons
Till Aczel, Lucas Theis, Roger Wattenhofer
Evaluating generative models is challenging because standard metrics often fail to reflect human preferences. Human evaluations are more reliable but costly and noisy, as participa…
SALSA-V: Shortcut-Augmented Long-form Synchronized Audio from Videos
Amir Dellali, Luca A. Lanzendörfer, Florian Grötschla +1
We propose SALSA-V, a multimodal video-to-audio generation model capable of synthesizing highly synchronized, high-fidelity long-form audio from silent video content. Our approach…
Parametric Neural Amp Modeling with Active Learning
Florian Grötschla, Longxiang Jiao, Luca A. Lanzendörfer +1
We introduce Panama, an active learning framework to train parametric guitar amp models end-to-end using a combination of an LSTM model and a WaveNet-like architecture. With \model…
Benchmarking Diarization Models
Luca A. Lanzendörfer, Florian Grötschla, Cesare Blaser +1
Speaker diarization is the task of partitioning audio into segments according to speaker identity, answering the question of "who spoke when" in multi-speaker conversation recordin…
From MNIST to ImageNet: Understanding the Scalability Boundaries of Differentiable Logic Gate Networks
Sven Brändle, Till Aczel, Andreas Plesner +1
Differentiable Logic Gate Networks (DLGNs) are a very fast and energy-efficient alternative to conventional feed-forward networks. With learnable combinations of logical gates, DLG…
The Impact of Scaling Training Data on Adversarial Robustness
Marco Zimmerli, Andreas Plesner, Till Aczel +1
Deep neural networks remain vulnerable to adversarial examples despite advances in architectures and training paradigms. We investigate how training data characteristics affect adv…
Light Differentiable Logic Gate Networks
Lukas Rüttgers, Till Aczel, Andreas Plesner +1
Differentiable logic gate networks (DLGNs) exhibit extraordinary efficiency at inference while sustaining competitive accuracy. But vanishing gradients, discretization errors, and…
The Unwinnable Arms Race of AI Image Detection
Till Aczel, Lorenzo Vettor, Andreas Plesner +1
The rapid progress of image generative AI has blurred the boundary between synthetic and real images, fueling an arms race between generators and discriminators. This paper investi…
HyperCool: Reducing Encoding Cost in Overfitted Codecs with Hypernetworks
Pep Borrell-Tatché, Till Aczel, Théo Ladune +1
Overfitted image codecs like Cool-chic achieve strong compression by tailoring lightweight models to individual images, but their encoding is slow and computationally expensive. To…
Mangrove: Fast and Parallelizable State Replication for Blockchains
Anton Paramonov, Yann Vonlanthen, Quentin Kniep +2
Mangrove is a novel scaling approach to building blockchains with parallel smart contract support. Unlike in monolithic blockchains, where a single consensus mechanism determines a…
Alignment-Aware Decoding
Frédéric Berdoz, Luca A. Lanzendörfer, René Caky +1
Alignment of large language models remains a central challenge in natural language processing. Preference optimization has emerged as a popular and effective method for improving a…