How Does Pre-Trained Wav2Vec 2.0 Perform On Domain-Shifted Asr? An Extensive Benchmark On Air Traffic Control Communications

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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 ...

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 ...

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 ...

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 ...

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 ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data's inherent structure or context by solving a pretext task. With ...

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 ...

We report the development of a continuous-wave and pulsed X-band electron spin resonance (ESR) spectrometer for the study of spins on ordered surfaces down to cryogenic temperatures. The spectrometer operates in ultra-high vacuum and utilizes a half-wavele ...

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 ...

River networks play a crucial role in the global carbon cycle, as relevant sources of carbon dioxide (CO2) to the atmosphere. Advancements in high-frequency monitoring in aquatic environments have enabled measurement of dissolved CO2 concentration at tempo ...

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

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 ...

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 ...

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

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 ...

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 ...

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have vanishingly low power dissipation and hence are a prime candidate for green, always-on computers. However, their u ...

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 ...

How Does Pre-Trained Wav2Vec 2.0 Perform On Domain-Shifted Asr? An Extensive Benchmark On Air Traffic Control Communications

Chattez avec Graph Search

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

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

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

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

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

Robust machine learning for neuroscientific inference

A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification

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

A continuous-wave and pulsed X-band electron spin resonance spectrometer operating in ultra-high vacuum for the study of low dimensional spin ensembles

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

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

Probing the latent hierarchical structure of data via diffusion models

Technology-Aware Logic Synthesis for Superconducting Electronics

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

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

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

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Context selectivity with dynamic availability enables lifelong continual learning

In-Sensor Passive Speech Classification with Phononic Metamaterials

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