Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

High resolution multi-physics core solvers predict safety parameters on a local scale with high accuracy while simulating local heterogeneities and their consequences. To achieve this, large computational resources are needed which renders the extensive us ...

This thesis explores stochastic optimal control through three interconnected contributions: a theoretical framework, and the development of two regression methods, POLK and POLRF, tailored to high-dimensional regression challenges in this domain. In the f ...

Symbolic Regression holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for previous online search methods an ...

Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. Here, we develop ChromatinHD, a pair of scale-adaptive models that uses the raw a ...

Building energy modeling plays a vital role in optimizing the operation of building energy systems by providing accurate predictions of the building's real-world conditions. In this context, various techniques have been explored, ranging from traditional p ...

The barren plateau phenomenon, characterized by loss gradients that vanish exponentially with system size, poses a challenge to scaling variational quantum algorithms. Here we explore the potential of warm starts, whereby one initializes closer to a soluti ...

Language modeling has seen impressive progress over the last few years. The invention of the Transformer architecture sparked a revolution in many fields of machine learning, including breakthroughs in chemistry and biology. In this chapter, we explore how ...

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 ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

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 ...

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 ...

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 ...

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

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 ...

Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

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, ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

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, ...

Much of our knowledge about the brain is derived from extracellular recordings of the electric fields produced by neural activity. However, interpretation of these signals is nontrivial. In par- ticular, it is difficult to link macro-scale observations of ...

Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

Chattez avec Graph Search

High-Resolution Predictions of the Fuel and Cladding Temperatures for the 3D PWR Core with Artificial Neural Networks trained on CTF

Parsimonious Online Learning with Kernels and Random Features with Applications to Stochastic Optimal Control

Interactive symbolic regression with co-design mechanism through offline reinforcement learning

ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning

Combining physics-based and data-driven modeling for building energy systems

Variational Quantum Simulation: A Case Study for Understanding Warm Starts

Transformers and Large Language Models for Chemistry and Drug Discovery

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

Aggregating Spatial and Photometric Context for Photometric Stereo

Driving and suppressing the human language network using large language models

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Random matrix methods for high-dimensional machine learning models

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Robust machine learning for neuroscientific inference

Social-Transmotion: Promptable Human Trajectory Prediction

Robust NAS under adversarial training: benchmark, theory, and beyond

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

Understanding generalization and robustness in modern deep learning

Machine Learning for Modeling Stock Returns

Coupled Electromagnetic-Electrophysiological Modeling of Neural Circuits