Effective Interplay Between Sparsity and Quantization: from Theory to Practice

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While convolution and self-attention mechanisms have dominated architectural design in deep learning, this survey examines a fundamental yet understudied primitive: the Hadamard product. Despite its widespread implementation across various applications, th ...

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The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens ...

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In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pr ...

Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic spaces and high-order interactions, tensors have a long history of ...

Effective Interplay Between Sparsity and Quantization: from Theory to Practice

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Hadamard product in deep learning: Introduction, Advances and Challenges

Semantic Segmentation of Benthic Classes in Reef Environments Using a Large Vision Transformer

Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City

LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

Assessment of fine-tuned large language models for real-world chemistry and material science applications

Enhancing Explainability and Performance in AI-Driven Face Recognition and Deepfake Detection

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

Positive-Unlabeled Constraint Learning for Inferring Nonlinear Continuous Constraints Functions From Expert Demonstrations

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance

Combining general and personal models for epilepsy detection with hyperdimensional computing

AMAE: Adaptation of Pre-trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays

ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields

From tensor-network quantum states to tensorial recurrent neural networks

Fast dynamic 1D simulation of divertor plasmas with neural PDE surrogates

A Compressor Off-Line Washing Schedule Optimization Method With a LSTM Deep Learning Model Predicting the Fouling Trend

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

Tensor Methods in Computer Vision and Deep Learning