7 citations · 19 across the 11 of their papers we have counts for
11 papers
Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent
Björn Hoppmann, Christoph Scholz
Humans are highly effective at utilizing prior knowledge to adapt to novel tasks, a capability that standard machine learning models struggle to replicate due to their reliance on…
BAT: Better Audio Transformer Guided by Convex Gated Probing
Houtan Ghaffari, Lukas Rauch, Christoph Scholz +1
Probing is widely adopted in computer vision to faithfully evaluate self-supervised learning (SSL) embeddings, as finetuning may misrepresent their inherent quality. In contrast, a…
Unmute the Patch Tokens: Rethinking Probing in Multi-Label Audio Classification
Lukas Rauch, René Heinrich, Houtan Ghaffari +4
Although probing frozen models has become a standard evaluation paradigm, self-supervised learning in audio defaults to fine-tuning when pursuing state-of-the-art on AudioSet. A ke…
Adversarial Training Improves Generalization Under Distribution Shifts in Bird Sound Classification
René Heinrich, Lukas Rauch, Raphael Schwinger +4
Adversarial training is a promising strategy for enhancing robustness against adversarial attacks, but its impact on generalization under substantial distribution shifts in audio c…
Can Masked Autoencoders Also Listen to Birds?
Lukas Rauch, René Heinrich, Ilyass Moummad +3
Masked Autoencoders (MAEs) learn rich semantic representations in audio classification through an efficient self-supervised reconstruction task. However, general-purpose models fai…
Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach
Mohamed Hassouna, Clara Holzhüter, Malte Lehna +4
The rising proportion of renewable energy in the electricity mix introduces significant operational challenges for power grid operators. Effective power grid management demands ada…
Predictions and Decision Making for Resilient Intelligent Sustainable Energy Systems
Martin Braun, Christian Gruhl, Christian A. Hans +7
Future energy systems are subject to various uncertain influences. As resilient systems they should maintain a constantly high operational performance whatever happens. We explore…
Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
Mohamed Hassouna, Clara Holzhüter, Pawel Lytaev +3
The increasing share of renewable energy and distributed electricity generation requires the development of deep learning approaches to address the lack of flexibility inherent in…
Towards Deep Active Learning in Avian Bioacoustics
Lukas Rauch, Denis Huseljic, Moritz Wirth +3
Passive acoustic monitoring (PAM) in avian bioacoustics enables cost-effective and extensive data collection with minimal disruption to natural habitats. Despite advancements in co…
AudioProtoPNet: An interpretable deep learning model for bird sound classification
René Heinrich, Lukas Rauch, Bernhard Sick +1
Deep learning models have significantly advanced acoustic bird monitoring by being able to recognize numerous bird species based on their vocalizations. However, traditional deep l…
BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics
Lukas Rauch, Raphael Schwinger, Moritz Wirth +8
Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domai…