Machine learning Hubbard parameters with equivariant neural networks

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Secondary organic aerosol (SOA) comprises most of the submicron atmospheric particle mass, and often becomes internally mixed with other particles. When SOA mixes with transition metal (e.g., iron) containing particles, metal-organic complexes can form, en ...

Computational simulations of quantum molecular dynamics are indispensable for understanding and predicting phenomena in physical chemistry. This thesis explores molecular processes where multiple electronic states play a role. A significant part of this wo ...

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challengi ...

Recent advancements in natural language processing, driven by the success of large language models, have led to the scaling up of models to unprecedented sizes. While these models have achieved remarkable performance across various tasks, their computation ...

We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach via pre-defined prompts to generate new data using either a language model or h ...

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

Elucidating the functional effects of missense variants is crucial yet challenging. To investigate their impact, we fine-tuned protein language models, including ESM2 and ProtT5, to classify 20 protein features at amino acid resolution. In addition, we tra ...

Cell tracking is a ubiquitous image analysis task in live-cell microscopy. Unlike multiple object tracking (MOT) for natural images, cell tracking typically involves hundreds of similar-looking objects that can divide in each frame, making it a particularl ...

Adversarial robustness has been studied extensively in image classification, especially for the ℓ∞-threat model, but significantly less so for related tasks such as object detection and semantic segmentation, where attacks turn out to be a much harder opti ...

Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local st ...

Data generation remains a bottleneck in training surrogate models to predict molecular properties. We demonstrate that multitask Gaussian process regression overcomes this limitation by leveraging both expensive and cheap data sources. In particular, we co ...

Synthetic strategies to isolate molecular complexes of lanthanides, other than cerium, in the +4 oxidation state remain elusive, with only four complexes of Tb(iv) isolated so far. Herein, we present a new approach for the stabilization of Tb(iv) using a s ...

Machine learning Hubbard parameters with equivariant neural networks

Chattez avec Graph Search

Photochemistry of iron-containing secondary organic aerosol is impacted by relative humidity during formation

Applications of semiclassical and mixed quantum-classical dynamics to study molecular systems with multiple electronic states

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

Parameter-efficient Methods for Training and Inference of Transformer-based Models

Controlled Training Data Generation with Diffusion Models

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Fine-tuning protein language models to understand the functional impact of missense variants

TRACKASTRA: Transformer-Based Cell Tracking for Live-Cell Microscopy

Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models

Unravelling the components of diffuse scattering using deep learning

Multitask methods for predicting molecular properties from heterogeneous data

Siloxide tripodal ligands as a scaffold for stabilizing lanthanides in the +4 oxidation state