Publications associées à Instruction-tuned LLMs with World Knowledge are More Aligned to the Human Brain

Publications associées (16)

Towards Generalizable Trajectory Prediction using Dual-Level Representation Learning and Adaptive Prompting

Alexandre Massoud Alahi, Kaouther Messaoud Ben Amor

Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like complex architectures customized for a specific dataset and inefficient multimoda ...

Institute of Electrical Engineering2025

Enhancing Critical Thinking in Education by Means of a Socratic Chatbot

Tanja Christina Käser Jacober

While large language models (LLMs) are increasingly playing a pivotal role in education by providing instantaneous, adaptive responses, their potential to promote critical thinking remains understudied. In this paper, we fill such a gap and present an inno ...

Springer Science and Business Media Deutschland GmbH2025

A Spatiotemporal Analysis of Brain Dynamics, a New Feature for Sleep and Seizure State Discrimination

Amir Hossein Omidvarnia

The human brain is a complex and dynamic physiological system that can be considered as a network of reciprocally interconnected systems localized in different brain regions. To fully characterize the brain's complex dynamics, it is essential to investigat ...

IEEE2025

Capturing behavioral and neural variability in multi-area spiking neuronal network models

Christos Sourmpis

How does the brain generate behavioral responses from sensory stimuli? This sensor-to-motor transformation is a fundamental question in systems neuroscience. While recent experiments have highlighted the complexity of neural representations across the cere ...

EPFL2025

Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores

Rachid Guerraoui, Arsany Hany Abdelmessih Guirguis, Diana Andreea Petrescu

Storage disaggregation underlies today's cloud and is naturally complemented by pushing down some computation to storage, thus mitigating the potential network bottleneck between the storage and compute tiers. We show how ML training benefits from storage ...

Association for Computing Machinery, Inc2024

Reliable data-driven decision-making through optimal transport

Bahar Taskesen

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

EPFL2024

Neuroengineering of hallucinations through fMRI neurofeedback: Understanding and modulating neural dynamics for the control of hallucinations in health and disease

Herberto Dhanis Pedro De Barros Camacho

Recent years have seen considerable progress in understanding brain function, and in modulating it through real-time fMRI neurofeedback (NF). Inspired by these advances, paired with the unique possibility of inducing the clinically relevant Presence Halluc ...

EPFL2023

18 GHz Ultraviolet Astrocomb via Chip-Integrated Harmonic Generation

Luis Guillermo Villanueva Torrijo, Furkan Ayhan

Current machine learning models for vision are often highly specialized and limited to a single modality and task. In contrast, recent large language models exhibit a wide range of capabilities, hinting at a possibility for similarly versatile models in co ...

Optical Society of America2023

Neurotechnologies to restore hand functions

Silvestro Micera, Solaiman Shokur

Reaching and manipulating objects are crucial tasks that allow proactive interaction with our surroundings. However, these functions are lost after neurological disorders or traumatic events that cause hand paralysis. Neuroprosthetic technologies are medic ...

SPRINGERNATURE2023

Revealing the Acute Effects of Dietary Components on Mood and Cognition: The Role of Autonomic Nervous System Responses

Sélima Zahar

A growing body of literature suggests dietary components can support mood and cognitive function through the impact of their bioactive or sensorial properties on neural pathways. Of interest, objective measures of the autonomic nervous system-such as those ...

2023

2D Nanosystems: Applications of 2D Semiconductors for In-Memory Computing

Guilherme Migliato Marega

Machine learning and data processing algorithms have been thriving in finding ways of processing and classifying information by exploiting the hidden trends of large datasets. Although these emerging computational methods have become successful in today's ...

EPFL2023

PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models

Rabeeh Karimi Mahabadi

Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score. In this work, we propose PERFECT, a ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2022

Computational Concepts for Reconstructing and Simulating Brain Tissue

Felix Schürmann, Srikanth Ramaswamy, Jean-Denis Georges Emile Courcol

It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These re ...

Springer2022

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs

Bahar Radmehr, Tanja Christina Käser Jacober, Mirko Marras, Vinitra Swamy

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in human-centric fields. We im ...

2022

Discrete-Choice Mining of Social Processes

Victor Kristof

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

EPFL2021

Clinical connectome fingerprints of cognitive decline

Enrico Amico

Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain d ...

2021