KI-Modelle

KI-Modelle machen Vorhersagen auf Basis ihrer Trainingsdaten. Sie können in jedem Bereich eingesetzt werden, beispielsweise in Zahlen, Texten oder Multimedia-Inhalten.

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LLMs

Tabular-Modelle Benchmark: Leistung über 19 Datensätze

KI-ModelleMai 22

We benchmarked 7 widely used tabular learning models across 19 real-world datasets, covering ~260,000 samples and over 250 total features, with dataset sizes ranging from 435 to nearly 49,000 rows. Our goal was to understand top-performing model families for datasets of different sizes and structure (e.g. numeric vs.

KI-ModelleMai 15

Welt-Grundmodelle: 10 Anwendungsfälle

Training robots and autonomous vehicles (AVs) in the physical world can be costly, time-consuming and risky. World Foundation Models offer a scalable alternative by enabling realistic simulations of real-world environments. These models accelerate development and deployment in robotics, AVs, and other domains by reducing reliance on physical testing.

KI-ModelleMai 7

Vergleich großer visueller Modelle: GPT-4o vs YOLOv8n

Large vision models (LVMs) can automate and improve visual tasks such as defect detection, medical diagnosis, and environmental monitoring. We benchmarked three object detection models: YOLOv8n, DETR, and GPT-4o Vision, across 1,000 images each, measuring metrics such as mAP@0.5, inference speed, FLOPs, and parameter count.

KI-ModelleApr 24

Vision Language Models im Vergleich zur Bilderkennung

Can advanced Vision Language Models (VLMs) replace traditional image recognition models? To find out, we benchmarked 16 leading models across three paradigms: traditional CNNs (ResNet, EfficientNet), VLMs ( such as GPT-4.1, Gemini 2.5), and Cloud APIs (AWS, Google, Azure).

KI-ModelleApr 15

Vergleich relationaler Fundamentaler Modelle

We benchmarked SAP-RPT-1-OSS against gradient boosting (LightGBM, CatBoost) on 17 tabular datasets spanning the semantic-numeral spectrum, small/high-semantic tables, mixed business datasets, and large low-semantic numerical datasets. Our goal is to measure where a relational LLM’s pretrained semantic priors may provide advantages over traditional tree models and where they face challenges under scale or low-semantic structure.

KI-ModelleFeb 10

Zeitreihen-Fundamentmodelle: Anwendungsfälle & Vorteile

Time series foundation models (TSFMs) build on advances in foundation models from natural language processing and vision. Using transformer-based architectures and large-scale training data, they achieve zero-shot performance and adapt across sectors such as finance, retail, energy, and healthcare.