Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

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Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

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The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

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The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

Chat with Graph Search

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

From PV to EV: Mapping the Potential for Electric Vehicle Charging with Solar Energy in Europe

Residual-based attention in physics-informed neural networks

Identification of typical district configurations: A two-step global sensitivity analysis framework

Robust NAS under adversarial training: benchmark, theory, and beyond

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life