arXiv:2411.09998v2 Announce Type: replace 
Abstract: As a highly expressive generative model, diffusion models have demonstrated exceptional success across various domains, including image generation, natural language processing, and combinatorial optimization. However, as data distributions grow more complex, training these models to convergence becomes increasingly computationally intensive. While diffusion models are typically trained using uniform timestep sampling, our research shows that the variance in stochastic gradients varies significantly across timesteps, with high-variance timesteps becoming bottlenecks that hinder faster convergence. To address this issue, we introduce a non-uniform timestep sampling method that prioritizes these more critical timesteps. Our method tracks the impact of gradient updates on the objective for each timestep, adaptively selecting those most likely to minimize the objective effectively. Experimental results demonstrate that this approach not only accelerates the training process, but also leads to improved performance at convergence. Furthermore, our method shows robust performance across various datasets, scheduling strategies, and diffusion architectures, outperforming previously proposed timestep sampling and weighting heuristics that lack this degree of robustness.

تظهر دراسة حديثة حول أخذ عينات غير منتظمة للوقت التكيفي لنماذج الانتشار نهجًا واعدًا لتسريع عمليات التدريب. مع تزايد استخدام نماذج الانتشار في مجالات مثل توليد الصور ومعالجة اللغة الطبيعية، يصبح التحدي المتمثل في الكثافة الحاسوبية أثناء التدريب أكثر وضوحًا. تقدم هذه الأبحاث حلاً يمكن أن يعزز الكفاءة والفعالية، مما يسهل على المطورين والباحثين الاستفادة من هذه النماذج التوليدية القوية في تطبيقات متنوعة.

Un estudio reciente sobre el muestreo de pasos de tiempo no uniforme adaptativo para modelos de difusión revela un enfoque prometedor para acelerar los procesos de entrenamiento. A medida que los modelos de difusión ganan terreno en campos como la generación de imágenes y el procesamiento del lenguaje natural, el desafío de la intensidad computacional durante el entrenamiento se vuelve más pronunciado. Esta investigación ofrece una solución que podría mejorar la eficiencia y efectividad, facilitando a los desarrolladores e investigadores aprovechar estos poderosos modelos generativos en diversas aplicaciones.

Une étude récente sur l'échantillonnage de pas de temps non uniforme adaptatif pour les modèles de diffusion révèle une approche prometteuse pour accélérer les processus d'entraînement. Alors que les modèles de diffusion gagnent en popularité dans des domaines tels que la génération d'images et le traitement du langage naturel, le défi de l'intensité computationnelle pendant l'entraînement devient de plus en plus prononcé. Cette recherche propose une solution qui pourrait améliorer l'efficacité et l'efficacité, facilitant ainsi l'utilisation de ces puissants modèles génératifs dans diverses applications.

A recent study on adaptive non-uniform timestep sampling for diffusion models reveals a promising approach to accelerate training processes. As diffusion models gain traction in fields like image generation and natural language processing, the challenge of computational intensity during training becomes more pronounced. This research offers a solution that could enhance efficiency and effectiveness, making it easier for developers and researchers to leverage these powerful generative models in various applications.

Adaptive Non-uniform Timestep Sampling for Accelerating Diffusion Model Training

arXiv:2503.09114v2 Announce Type: replace 
Abstract: The rapid rise of Language Models (LMs) has expanded the capabilities of natural language processing, powering applications from text generation to complex decision-making. While state-of-the-art LMs often boast hundreds of billions of parameters and are primarily deployed in data centers, recent trends show a growing focus on compact models-typically under 10 billion parameters-enabled by techniques such as quantization and other model compression techniques. This shift paves the way for LMs on edge devices, offering potential benefits such as enhanced privacy, reduced latency, and improved data sovereignty. However, the inherent complexity of even these smaller models, combined with the limited computing resources of edge hardware, raises critical questions about the practical trade-offs in executing LM inference outside the cloud. To address these challenges, we present a comprehensive evaluation of generative LM inference on representative CPU-based and GPU-accelerated edge devices. Our study measures key performance indicators-including memory usage, inference speed, and energy consumption-across various device configurations. Additionally, we examine throughput-energy trade-offs, cost considerations, and usability, alongside an assessment of qualitative model performance. While quantization helps mitigate memory overhead, it does not fully eliminate resource bottlenecks, especially for larger models. Our findings quantify the memory and energy constraints that must be considered for practical real-world deployments, offering concrete insights into the trade-offs between model size, inference performance, and efficiency. The exploration of LMs at the edge is still in its early stages. We hope this study provides a foundation for future research, guiding the refinement of models, the enhancement of inference efficiency, and the advancement of edge-centric AI systems.

أدى التقدم السريع في نماذج اللغة (LM) إلى تحول نحو نماذج مدمجة، عادةً أقل من 10 مليارات معلمة، يمكن نشرها على الأجهزة الطرفية. يقود هذا التحول تقنيات مثل التكميم وضغط النماذج، بهدف تعزيز الخصوصية وتقليل زمن الاستجابة وتحسين سيادة البيانات. ومع ذلك، فإن تعقيد هذه النماذج والموارد الحاسوبية المحدودة للأجهزة الطرفية تطرح تحديات كبيرة لتنفيذ الاستدلال بشكل فعال خارج بيئات السحابة.

El rápido avance de los Modelos de Lenguaje (LM) ha llevado a un cambio hacia modelos compactos, típicamente por debajo de los 10 mil millones de parámetros, que pueden ser desplegados en dispositivos de borde. Esta transición está impulsada por técnicas como la cuantificación y la compresión de modelos, con el objetivo de mejorar la privacidad, reducir la latencia y mejorar la soberanía de los datos. Sin embargo, la complejidad de estos modelos y los recursos computacionales limitados del hardware de borde plantean desafíos significativos para la inferencia efectiva fuera de los entornos en l…

L'avancement rapide des modèles de langage (LM) a conduit à un tournant vers des modèles compacts, généralement inférieurs à 10 milliards de paramètres, pouvant être déployés sur des appareils en périphérie. Cette transition est motivée par des techniques telles que la quantification et la compression de modèles, visant à améliorer la confidentialité, réduire la latence et renforcer la souveraineté des données. Cependant, la complexité de ces modèles et les ressources informatiques limitées du matériel en périphérie posent des défis significatifs pour une inférence efficace en dehors des envir…

The rapid advancement of Language Models (LMs) has led to a shift towards compact models, typically under 10 billion parameters, which can be deployed on edge devices. This transition is driven by techniques like quantization and model compression, aiming to enhance privacy, reduce latency, and improve data sovereignty. However, the complexity of these models and the limited computing resources of edge hardware pose significant challenges for effective inference outside cloud environments.

Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge

arXiv:2511.17184v1 Announce Type: new 
Abstract: News text classification is a crucial task in natural language processing, essential for organizing and filtering the massive volume of digital content. Traditional methods typically rely on statistical features like term frequencies or TF-IDF values, which are effective at capturing word-level importance but often fail to reflect contextual meaning. In contrast, modern deep learning approaches utilize semantic features to understand word usage within context, yet they may overlook simple, high-impact statistical indicators. This paper introduces an Attention-Guided Feature Fusion (AGFF) model that combines statistical and semantic features in a unified framework. The model applies an attention-based mechanism to dynamically determine the relative importance of each feature type, enabling more informed classification decisions. Through evaluation on benchmark news datasets, the AGFF model demonstrates superior performance compared to both traditional statistical models and purely semantic deep learning models. The results confirm that strategic integration of diverse feature types can significantly enhance classification accuracy. Additionally, ablation studies validate the contribution of each component in the fusion process. The findings highlight the model's ability to balance and exploit the complementary strengths of statistical and semantic representations, making it a practical and effective solution for real-world news classification tasks.

تم تقديم نموذج دمج الميزات الموجه بالانتباه (AGFF) لتحسين تصنيف نصوص الأخبار من خلال دمج الميزات الإحصائية والدلالية. يستخدم هذا النموذج آلية انتباه لتقييم أهمية كل نوع من الميزات، بهدف تحسين دقة التصنيف في سياق معالجة اللغة الطبيعية.

Se ha introducido el modelo de fusión de características guiado por atención (AGFF) para mejorar la clasificación de textos de noticias al integrar características estadísticas y semánticas. Este modelo emplea un mecanismo de atención para evaluar la importancia de cada tipo de característica, con el objetivo de mejorar la precisión de la clasificación en el contexto del procesamiento del lenguaje natural.

Le modèle de fusion de caractéristiques guidé par l'attention (AGFF) a été introduit pour améliorer la classification des textes d'actualités en intégrant à la fois des caractéristiques statistiques et sémantiques. Ce modèle utilise un mécanisme d'attention pour évaluer l'importance de chaque type de caractéristique, visant à améliorer la précision de la classification dans le contexte du traitement du langage naturel.

The Attention-Guided Feature Fusion (AGFF) model has been introduced to enhance news text classification by integrating both statistical and semantic features. This model employs an attention mechanism to assess the importance of each feature type, aiming to improve classification accuracy in the context of natural language processing.

Adaptive Non-uniform Timestep Sampling for Accelerating Diffusion Model Training

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