Aiming beyond slight increases in accuracy

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Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drive ...

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Significant damages caused by lightning can be avoided by predicting lightning and taking precautionary measures. Here, a machine learning model is developed to nowcast lightning flashes using dew point temperature, precipitation, wind speed, wind directio ...

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer Interface (BCI) systems. The package incorporates efficient algori ...

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 ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

Buildings significantly impact urban energy consumption. Mobile passive solutions, such as manual solar protections, can mitigate heat gains, but their effectiveness depends on occupants’ decisions. Analyzing occupant adjustments of manual systems is chall ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

In today's era of rapid urbanization, smart cities are emerging as a beacon of efficiency and sustainability. At the heart of this evolution is the integration of Machine Learning (ML), which can transform vast streams of data into informed decisions. This ...

Aiming beyond slight increases in accuracy

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Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Improving the accuracy and interpretability of neural networks for wind power forecasting

Random matrix methods for high-dimensional machine learning models

Using deep reinforcement learning to investigate stretch feedback during swimming of the lamprey

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Exploring the portability of ML-based lightning nowcasting models

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)

Advances on Real Time M/EEG Neural Feature Extraction

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (4/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (5/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (6/6)

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

Towards Trustworthy Deep Learning for Image Reconstruction

Machine-learning to analyze human-building interactions: How do people use mobile solar protections?

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

From Data to Decision: Exploring Machine Learning's Impact on Shaping Smart Cities