Exploiting foreign resources for DNN-based ASR

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Accurately measuring behavior is critical to understanding brain function. While traditional behavioral analysis has relied on human observation and manual processing, advances in machine learning and computer vision have begun to automate the field. This ...

Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfun ...

Upcoming wide-field surveys, such as the Rubin Observatory's Legacy Survey of Space and Time (LSST), will monitor thousands of strongly lensed quasars over a 10 yr period. Many of these monitored quasars will undergo high-magnification events (HMEs) throug ...

Automatically tracking and segmenting every video entity remains a significant challenge. Despite rapid advancements in video segmentation, even state-of-the-art models like SAM 2 struggle to consistently track all entities across a video—a task we refer t ...

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

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 ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Exploiting foreign resources for DNN-based ASR

Chat with Graph Search

From Pose to Behavior: Advancing Multi-Individual Pose Estimation and Hierarchical Action Segmentation with Transformers

Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers

Predicting High-magnification Events in Microlensed Quasars in the Era of LSST Using Recurrent Neural Networks

EntitySAM: Segment Everything in Video

Driving and suppressing the human language network using large language models

Safe Deep Neural Networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Topics in statistical physics of high-dimensional machine learning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Machine Learning for Modeling Stock Returns

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Random matrix methods for high-dimensional machine learning models

Optimizing Dynamic Aperture Studies with Active Learning

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging