Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

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We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. It yields dense and semantically meaningful correspondences between frames. We represent the reconstructed su ...

Dynamic hand gesture recognition (HGR) is critical for real-time prosthetics and neural robotics applications, where high-density surface electromyography (HD-sEMG) signals provide valuable insights into muscle activity. However, adapting machine learning ...

This workshop proposal aims to inform the design of teacher training on equity in the use and teaching of AI. AI is transforming both teaching practices and student learning environments and learning content. Teachers are increasingly using AI tools for co ...

This paper studies the problem of learning computable functions in the limit by extending Gold's inductive inference framework to incorporate computational observations and restricted input sources. Complimentary to the traditional Input-Output Observation ...

Lattice light-sheet microscopy provides a crucial observation window into intra- and intercellular physiology of living specimens but at the diffraction-limited resolution or anisotropic super-resolution with structured illumination. Here we present meta-l ...

Machine learning models, meticulously optimized for source data, often fail to predict target data when faced with distribution shifts (DSs). Previous benchmarking studies, though extensive, have mainly focused on simple DSs. Recognizing that DSs often occ ...

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024 Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis. Affiliation: EPFL Date: June, 2024 Link to the CVPR article: https://openaccess.thecvf.com/conte ...

This thesis focuses on the development of advanced algorithmic techniques, primarily Markov Chain Monte Carlo (MCMC) methods, and message passing algorithms, to tackle high-dimensional optimization and inference problems. The algorithms used have a probabi ...

In fully supervised learning-based medical image classification, the robustness of a trained model depends on its exposure to various disease classes. Generalized Zero Shot Learning (GZSL) aims to predict both seen and novel unseen classes. While most GZSL ...

In an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhanc ...

For Graph Neural Networks, oversmoothing denotes the homogenization of vertex embeddings as the number of layers increases. To better understand this phenomenon, we study community detection with a linearized Graph Convolutional Network on the Contextual S ...

Objective. Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local fi ...

This dataset contains images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F5 ...

Aims. As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these imag ...

However, their use in imaging applications has been limited by their relatively large pixel size (≥ 55 μm) and high-energy (>80 keV) electrons scattering over multiple pixels in the sensor layer. To realize the full potential of fast, radiation-hard HPDs a ...

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it ...

Transformer-based models have revolutionized NLP. But in general, these models are highly resource consuming. Based on this consideration, several reservoir computing approaches to NLP have shown promising results. In this context, we propose EsnTorch, a l ...

The majority of uncertainty quantification methods for deep object detectors are based on the network output, such as sampling strategies like Monte-Carlo dropout or deep ensembles with straight-forward transfers to object detection. Here, we study gradien ...

Being able to reliably assess not only the accuracy but also the uncertainty of models' predictions is an important endeavor in modern machine learning. Even if the model generating the data and labels is known, computing the intrinsic uncertainty after le ...

Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations

Chattez avec Graph Search

Temporally-Consistent Surface Reconstruction Using Metrically-Consistent Atlases

Low-Rank Adaptation for Transformers in Dynamic Hand Gesture Recognition Using High-Density Surface Electromyography

Preparing Teachers for an Equitable AI Integration in Education

Learning Algorithms in the Limit

Fast-adaptive super-resolution lattice light-sheet microscopy for rapid, long-term, near-isotropic subcellular imaging

An Analysis of Model Robustness across Concurrent Distribution Shifts

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields: Processed data and trained models

Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems

Multi-Label Generalized Zero Shot Chest X-Ray Classification by Combining Image-Text Information With Feature Disentanglement

Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations

Optimal performance of Graph Convolutional Networks on the Contextual Stochastic Block Model

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

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Ground-based image deconvolution with Swin Transformer UNet

Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

Learning sparse features can lead to overfitting in neural networks

The Esntorch Library: Efficient Implementation of Transformer-based Echo State Networks

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors

Theoretical characterization of uncertainty in high-dimensional linear classification