Publication

Driving and suppressing the human language network using large language models

Related publications (72)

Learning Entailment-Based Sentence Embeddings from Natural Language Inference

James Henderson, Rabeeh Karimi Mahabadi, Jan Frederik Jonas Florian Mai

Large datasets on natural language inference are a potentially valuable resource for inducing semantic representations of natural language sentences. But in many such models the embeddings computed by the sentence encoder goes through an MLP-based interact ...

Idiap2019

Social babbling: The emergence of symbolic gestures and words

Aude Billard, Laura Bénédicte Marine Cohen

Language acquisition theories classically distinguish passive language understanding from active language production. However, recent findings show that brain areas such as Broca's region are shared in language understanding and production. Furthermore, th ...

PERGAMON-ELSEVIER SCIENCE LTD2018

Neural assemblies as core elements for modeling neural networks in the brain

Hesam Setareh

How does the brain process and memorize information? We all know that the neuron (also known as nerve cell) is the processing unit in the brain. But how do neurons work together in networks? The connectivity structure of neural networks plays an important ...

EPFL2017

Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems

Jean-Philippe Thiran, Pierre Vandergheynst, Alessandra Griffa, Kirell Maël Benzi, Patric Hagmann, Alessandro Daducci, Xavier Bresson, Benjamin Ricaud

The study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural ...

Elsevier2017

Understanding and Decoding Imagined Speech using Electrocorticographic Recordings in Humans

Stéphanie Martin

Certain brain disorders, resulting from brainstem infarcts, traumatic brain injury, stroke and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders wou ...

EPFL2017

Corpora Delicti

Alexandre Blanc, Marco Bakker

CORPORA DELICTI, The Significant Detail. The Corpus Delicti is the evidence of a crime. The term denotes the formation of a proof that allows the unmasking of a perpetrator. In the realm of interpretation and language, it could reveal matter on which an ac ...

MANSLAB2017

The Operational Semantics and Implementation of a Core Dart language

Zhivka Gucevska

In this report we present the specification of the operational semantics of Dart Kernel and a reference implementation of Dart Kernel in Dart. We design a CESK-like machine to specify the operational semantics of Dart Kernel and we implement an interpreter ...

2017

Word Sequence Modeling using Deep Learning

Joël Yvon Roland Legrand

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

EPFL2016

Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

Acoustic and Lexical Resource Constrained ASR using Language-Independent Acoustic Model and Language-Dependent Probabilistic Lexical Model

Ramya Rasipuram

One of the key challenges involved in building statistical automatic speech recognition (ASR) systems is modeling the relationship between subword units or “lexical units” and acoustic feature observations. To model this relationship two types of resources ...

2015

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.