Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

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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 ...

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 ...

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 ...

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 ...

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 ...

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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 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 ...

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 ...

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 ...

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Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

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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 ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

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

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

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

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

Variational Quantum Simulation: A Case Study for Understanding Warm Starts

Aggregating Spatial and Photometric Context for Photometric Stereo

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Random matrix methods for high-dimensional machine learning models

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

Understanding generalization and robustness in modern deep learning

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

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Topics in statistical physics of high-dimensional machine learning

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Robust machine learning for neuroscientific inference

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

The multimodality cell segmentation challenge: toward universal solutions

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

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

Incorporating Projective Geometry into Deep Learning