Modelos de IA

Los modelos de IA realizan predicciones basándose en sus datos de entrenamiento. Pueden funcionar en cualquier ámbito, como números, texto o multimedia.

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Másteres en Derecho (LLM)

Comparativo de Modelos Tabulares: Rendimiento en 19 Conjuntos de Datos

Modelos de IAMay 22

We benchmarked 7 widely used tabular learning models to identify top-performing model families across 19 real-world datasets of varying sizes and structures, covering ~260,000 samples and over 250 total features, with dataset sizes ranging from 435 to nearly 49,000 rows. Tabular learning models benchmark results In the chart, the winning model receives 1 point.

Modelos de IAMay 15

Modelos Fundamentales del Mundo: 10 Casos de Uso

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.

Modelos de IAMay 7

Comparar Modelos de Visión Grandes: 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.

Modelos de IAAbr 24

Modelos de Lenguaje Visual Comparados con el Reconocimiento de Imágenes

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).

Modelos de IAAbr 15

Comparar Modelos Fundacionales Relacionales

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.

Modelos de IAFeb 10

Modelos Fundacionales de Series Temporales: Casos de Uso y Beneficios

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.