8 citations · 13 across the 13 of their papers we have counts for
16 papers
STISA: A 0.16 GOPS/W/PE Single-Shot Inference FPGA-based SNN Accelerator with Algorithm and Hardware Co-Design
Kainan Wang, Chengyi Yang, Lei Liu +4
Spiking neural networks (SNNs) offer great potential for low-power intelligent computing owing to their event-driven nature, yet their deployment is limited by high inference laten…
Benchmarking 3D Reconstruction for Under-Ice Robotic Perception in Arctic Environments
Klára Orbán, Ashwin Nedungadi, Ana-Ştefania Kogălniceanu +5
As the Arctic transitions toward a seasonally ice-free state, capturing the complex morphology of submerged sea ice is crucial for advancing models of ocean-ice-atmosphere heat exc…
Autoregressive Mosaics: Probing 2D Spatial Reasoning in Text-Only Language Models
Ashwin Nedungadi, Stefan Oehmcke, Stefan Lüdtke
Large language models (LLMs) trained only on text and code can sometimes generate programs that draw recognizable images. However, it is unclear whether this reflects an internal r…
Lost in Aggregation: How Benchmarks Overlook Irreplaceable Model Strengths
Andrej Tschalzev, Stefan Lüdtke, Heiner Stuckenschmidt +1
Tabular machine learning benchmarks typically summarize performance by averaging scores, ranks, or pairwise wins across datasets. Such aggregates are useful for selecting robust de…
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Andrej Tschalzev, Nick Erickson, Yuyang Wang +4
Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remains a critical yet underexplor…
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods
Andrej Tschalzev, Paul Nitschke, Lukas Kirchdorfer +3
Neural networks often assume independence among input data samples, disregarding correlations arising from inherent clustering patterns in real-world datasets (e.g., due to differe…
On the Aggregation of Rules for Knowledge Graph Completion
Patrick Betz, Stefan Lüdtke, Christian Meilicke +1
Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding…
Towards Machine Learning-based Fish Stock Assessment
Stefan Lüdtke, Maria E. Pierce
The accurate assessment of fish stocks is crucial for sustainable fisheries management. However, existing statistical stock assessment models can have low forecast performance of r…
Leveraging Planning Landmarks for Hybrid Online Goal Recognition
Nils Wilken, Lea Cohausz, Johannes Schaum +3
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios it is imp…
Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition
Maximilian Popko, Sebastian Bader, Stefan Lüdtke +1
The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behavior…
Outlier Explanation via Sum-Product Networks
Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
Outlier explanation is the task of identifying a set of features that distinguish a sample from normal data, which is important for downstream (human) decision-making. Existing met…
Human Activity Recognition using Attribute-Based Neural Networks and Context Information
Stefan Lüdtke, Fernando Moya Rueda, Waqas Ahmed +2
We consider human activity recognition (HAR) from wearable sensor data in manual-work processes, like warehouse order-picking. Such structured domains can often be partitioned into…
Exchangeability-Aware Sum-Product Networks
Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
Sum-Product Networks (SPNs) are expressive probabilistic models that provide exact, tractable inference. They achieve this efficiency by making use of local independence. On the ot…
State-Space Abstractions for Probabilistic Inference: A Systematic Review
Stefan Lüdtke, Max Schröder, Frank Krüger +2
Tasks such as social network analysis, human behavior recognition, or modeling biochemical reactions, can be solved elegantly by using the probabilistic inference framework. Howeve…
Lifted Filtering via Exchangeable Decomposition
Stefan Lüdtke, Max Schröder, Sebastian Bader +2
We present a model for exact recursive Bayesian filtering based on lifted multiset states. Combining multisets with lifting makes it possible to simultaneously exploit multiple str…
Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems
Max Schröder, Stefan Lüdtke, Sebastian Bader +2
Bayesian Filtering for plan and activity recognition is challenging for scenarios that contain many observation equivalent entities (i.e. entities that produce the same observation…