Spline Shape Processing : Representation, Learning, and Modeling

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Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

The growing global obesity issue emphasizes the importance of understanding its health implications. Previous research has identified consistent alterations in gray matter (GM) volume in connection with obesity. Given the various implications of distinct f ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional n ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

Advances in computational capabilities and large volumes of experimental data have established computer simulations of brain tissue models as an important pillar in modern neuroscience. Alongside, a variety of domain specific languages (DSLs) have been dev ...

Introduction: Brain-inspired computing has become an emerging field, where a growing number of works focus on developing algorithms that bring machine learning closer to human brains at the functional level. As one of the promising directions, Hyperdimensi ...

Car sharing systems (CSSs) are one of the environmentally beneficial solutions in urban transportation. However, the operators still struggle to make these systems profitable. One of the main contributors in operational cost is rebalancing operations. Ther ...

IntroductionThe current assessment and standardization of microsurgical skills are subjective, posing challenges in reliable skill evaluation. We aim to address these limitations by developing a quantitative and objective framework for accurately assessing ...

This paper presents a theoretical analysis of linear interpolation as a principled method for stabilizing (large-scale) neural network training. We argue that instabilities in the optimization process are often caused by the nonmonotonicity of the loss lan ...

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved mod ...

Technology supporting meditation is a multimillion-dollar market that continues to grow. There is also strong academic interest to understand and improve the impact technology can have for the user experience of practitioners. However, little work investig ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...

Spline Shape Processing : Representation, Learning, and Modeling

Chattez avec Graph Search

Enabling Uncertainty Estimation in Iterative Neural Networks

The interplay between white adipose tissue, adipokines, and structural gray matter changes

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Investigating neural resource allocation in the sensorimotor control of extra limbs

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

Efficient and insightful descriptors for representing molecular and material space

MOD2IR: High-Performance Code Generation for a Biophysically Detailed Neuronal Simulation DSL

Hyperdimensional computing with holographic and adaptive encoder

A general framework to evaluate different rebalancing operations strategies in one-way car sharing systems

An objective skill assessment framework for microsurgical anastomosis based on ALI scores

Stable Nonconvex-Nonconcave Training via Linear Interpolation

Data-driven models in fusion exhaust: AI methods and perspectives

Adapting Haptic Feedback for Guided Meditation

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

Deep Learning for 3D Surface Modelling and Reconstruction