Automatic Parkinson's disease detection from speech: Layer selection vs adaptation of foundation models

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Elucidating the functional effects of missense variants is crucial yet challenging. To investigate their impact, we fine-tuned protein language models, including ESM2 and ProtT5, to classify 20 protein features at amino acid resolution. In addition, we tra ...

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

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Large language models (LLMs) have gained widespread interest owing to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only a limited systematic understanding of the chemical capa ...

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While writing peer reviews resembles an important task in science, education, and large organizations, providing fruitful suggestions to peers is not a straightforward task, as different user interaction designs of text suggestion interfaces can have diver ...

© The Author(s) 2024.Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, h ...

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Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep-learning-based framework (CLAIMS: Cortical Lesio ...

Active learning (AL) is a branch of machine learning that deals with problems where unlabeled data is abundant yet obtaining labels is expensive. The learning algorithm has the possibility of querying a limited number of samples to obtain the corresponding ...

As an indicator of attention, gaze is an important cue for human behavior and social interaction analysis. Recent deep learning methods for gaze estimation rely on plain regression of the gaze from images without accounting for potential mismatches in eye ...

Automatic Parkinson's disease detection from speech: Layer selection vs adaptation of foundation models

Chat with Graph Search

Fine-tuning protein language models to understand the functional impact of missense variants

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Parameter-efficient Methods for Training and Inference of Transformer-based Models

A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design Dynamics

A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting

Point-process-based Representation Learning for Electronic Health Records

Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI

Large deviations in the perceptron model and consequences for active learning

Deep Multitask Gaze Estimation with a Constrained Landmark-Gaze Model