Publications associées à A framework for evaluating the chemical knowledge and reasoning abilities of large language models against the expertise of chemists

Publications associées (16)

Adversarial Training For Defense Against Label Poisoning Attacks

As machine learning models grow in complexity and increasingly rely on publicly sourced data, such as the human-annotated labels used in training large language models, they become more vulnerable to label poisoning attacks. These attacks, in which adversa ...

2025

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

Mohammadreza Banaei

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 ...

EPFL2025

Controlled Training Data Generation with Diffusion Models

Aleksandr Alekseev, Shuqing Teresa Yeo, Andrei Atanov

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 ...

2025

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

Jacques Fellay

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 ...

2025

Automatic Parkinson's disease detection from speech: Layer selection vs adaptation of foundation models

Tilak Purohit

In this work, we investigate Speech Foundation Models (SFMs) for Parkinson's Disease (PD) detection. We explore two main approaches: (1) using SFMs as frozen feature extractors and, (2) fine-tuning/adapting SFMs for PD detection. We propose a cross-validat ...

Institute of Electrical and Electronics Engineers Inc.2025

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao

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 ...

IEEE Institute of Electrical and Electronics Engineers2025

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

Fernando Perez Cruz

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 ...

2025

Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design Dynamics

Xiaotian Su, Seyed Parsa Neshaei

While writing peer reviews resembles an important task in science, education, and large organizations, providing fruitful suggestions to peers is not a straightforward task, as different user interaction designs of text suggestion interfaces can have diver ...

Association for Computing Machinery2024

Training Visual Language Models with Object Detection: Grounded Change Descriptions in Satellite Images

Devis Tuia, Antoine Bosselut, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol, João Luís Prado Vieira

Recently, generalist Vision Language Models (VLMs) have shown exceptional progress in tasks previously dominated by specialized computer vision models. This becomes more prevalent when visual grounding capabilities, such as the ability to reason over input ...

Institute of Electrical and Electronics Engineers Inc.2024

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Christian Leinenbach, Antonios Nikolaos Baganis

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

Taylor & Francis Ltd2024

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Martin Weigert

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a communitywide challenge using the largest availab ...

Elsevier2023

Point-process-based Representation Learning for Electronic Health Records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Irregular sampling of time series in electronic health records (EHRs) presents a challenge for the development of machine learning models. Additionally, the pattern of missing data in certain clinical variables is not random, but depends on the decisions o ...

IEEE2023

Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Rahul Sharma

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system ...

MDPI2023

Enhancing argumentative writing with automated feedback and social comparison nudging

Thiemo Wambsganss

The advantages offered by natural language processing (NLP) and machine learning enable students to receive automated feedback on their argumentation skills, independent of educator, time, and location. Although there is a growing amount of literature on f ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI

Jean-Philippe Thiran, Francesco La Rosa

Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep-learning-based framework (CLAIMS: Cortical Lesio ...

WILEY2022

Deep Multitask Gaze Estimation with a Constrained Landmark-Gaze Model

Jean-Marc Odobez, Yu Yu

As an indicator of attention, gaze is an important cue for human behavior and social interaction analysis. Recent deep learning methods for gaze estimation rely on plain regression of the gaze from images without accounting for potential mismatches in eye ...

SPRINGER INTERNATIONAL PUBLISHING AG2018