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

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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 ...

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

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, ...

Now is an exciting time for the domain of Earth observation (EO), with a multitude of diverse sensors looking at the planet from satellites, airplanes or drones. The volume of imagery acquired is massive, and hold great potential for a variety of applicati ...

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hard ...

Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in ter ...

Many empirical studies have provided evidence for the emergence of algorithmic mechanisms (abilities) in the learning of language models, that lead to qualitative improvements in the model capabilities. Yet, a theoretical characterization of how such mecha ...

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

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 ...

In recent years, neural quantum states have emerged as a powerful variational approach, achieving state-of-the-art accuracy when representing the ground-state wave function of a great variety of quantum many-body systems, including spin lattices, interacti ...

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

Derived from the Karush–Kuhn–Tucker (KKT) optimality conditions of utility maximization, the multiple discrete–continuous extreme value (MDCEV) models are extensively used in the field of time use. This is because they are grounded in a framework that maxi ...

Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and req ...

The Internet of Things (IoT) consists of physical objects and devices embedded with network connectivity, software, and sensors to collect and transmit data. The development of the Internet of Things (IoT) has led to various security and privacy issues, in ...

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 ...

Deep neural networks form the backbone of artificial intelligence (AI) research, with potential to transform the human experience in areas ranging from autonomous driving to personal assistants, healthcare to education. However, their integration into the ...

Increasing the scanning speed in Atomic Force Microscopy (AFM) relies on improving the tracking performance in the vertical direction of motion. The lightly damped resonances of piezo-actuators utilized in AFM nano-positioning stages hinder the maximum ach ...

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

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

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

Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

Learning to Assemble with Alternative Plans

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

Interact with Earth Observation Images using AI: Transparent Methods and Evaluation for Visual Question Answering

Mspo: a Machine Learning Hyperparameter Optimization Method for Enhanced Breast Cancer Image Classification

Training hybrid neural networks with multimode optical nonlinearities using digital twins

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

A phase transition between positional and semantic learning in a solvable model of dot-product attention

Domain shift, domain adaptation, and generalization: A focus on MRI

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

Accurate neural quantum states for interacting lattice bosons

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

A new flexible multiple discrete–continuous extreme value (MDCEV) choice model: exploring non-monotonic preference in time use decisions

Self-supervised and few-shot learning for robust bioaerosol monitoring

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

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

A Human-Centric Approach to Explainable AI for Personalized Education

Data-driven control in atomic force microscopy using a genetic algorithm

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics