HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration

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Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

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Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

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Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

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In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health status of industrial assets over time serve as valuable tools for, e.g., fault d ...

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Superconducting electronics provide us with cryogenic digital circuits that can rival established technologies in performance and energy consumption. Today, the lack of tools for the design of large-scale integrated superconducting circuits is a major obst ...

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While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration

Chattez avec Graph Search

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Topics in statistical physics of high-dimensional machine learning

Robust machine learning for neuroscientific inference

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

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Partial discharge localization in power transformer tanks using machine learning methods

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Machine learning-aided generative molecular design

Meta-learning to address diverse Earth observation problems across resolutions

Random matrix methods for high-dimensional machine learning models

Residual-based attention in physics-informed neural networks

Lotic-SIPCO2: Adaptation of an open-source CO2 sensor system and examination of associated emission uncertainties across a range of stream sizes and land uses

Incorporating Projective Geometry into Deep Learning

Machine Learning for Modeling Stock Returns

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Predicting the long-term collective behaviour of fish pairs with deep learning

Technology-Aware Logic Synthesis for Superconducting Electronics

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

On the Generalization of Stochastic Gradient Descent with Momentum