171 citations · 246 across the 15 of their papers we have counts for
17 papers
AI Consciousness and Existential Risk
Rufin VanRullen
In AI, the existential risk denotes the hypothetical threat posed by an artificial system that would possess both the capability and the objective, either directly or indirectly, t…
GASPnet: Global Agreement to Synchronize Phases
Andrea Alamia, Sabine Muzellec, Thomas Serre +1
In recent years, Transformer architectures have revolutionized most fields of artificial intelligence, relying on an attentional mechanism based on the agreement between keys and q…
Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models
Louis Béthune, David Vigouroux, Yilun Du +3
What is the shortest path between two data points lying in a high-dimensional space? While the answer is trivial in Euclidean geometry, it becomes significantly more complex when t…
Learning to Chain Operations by Routing Information Through a Global Workspace
Hugo Chateau-Laurent, Rufin VanRullen
We present a model inspired by the Global Workspace Theory that integrates specialized modules to perform a sequential reasoning task. A controller selectively routes information b…
Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning
Léopold Maytié, Roland Bertin Johannet, Rufin VanRullen
Humans leverage rich internal models of the world to reason about the future, imagine counterfactuals, and adapt flexibly to new situations. In Reinforcement Learning (RL), world m…
Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics
Sabine Muzellec, Andrea Alamia, Thomas Serre +1
Neural synchrony is hypothesized to play a crucial role in how the brain organizes visual scenes into structured representations, enabling the robust encoding of multiple objects w…
Tracking objects that change in appearance with phase synchrony
Sabine Muzellec, Drew Linsley, Alekh K. Ashok +4
Objects we encounter often change appearance as we interact with them. Changes in illumination (shadows), object pose, or the movement of non-rigid objects can drastically alter av…
Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks
Victor Boutin, Rishav Mukherji, Aditya Agrawal +4
Humans can effortlessly draw new categories from a single exemplar, a feat that has long posed a challenge for generative models. However, this gap has started to close with recent…
Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspace
Léopold Maytié, Benjamin Devillers, Alexandre Arnold +1
Humans perceive the world through multiple senses, enabling them to create a comprehensive representation of their surroundings and to generalize information across domains. For in…
Saliency strikes back: How filtering out high frequencies improves white-box explanations
Sabine Muzellec, Thomas Fel, Victor Boutin +3
Attribution methods correspond to a class of explainability methods (XAI) that aim to assess how individual inputs contribute to a model's decision-making process. We have identifi…
Semi-supervised Multimodal Representation Learning through a Global Workspace
Benjamin Devillers, Léopold Maytié, Rufin VanRullen
Recent deep learning models can efficiently combine inputs from different modalities (e.g., images and text) and learn to align their latent representations, or to translate signal…
Does language help generalization in vision models?
Benjamin Devillers, Bhavin Choksi, Romain Bielawski +1
Vision models trained on multimodal datasets can benefit from the wide availability of large image-caption datasets. A recent model (CLIP) was found to generalize well in zero-shot…
GAttANet: Global attention agreement for convolutional neural networks
Rufin VanRullen, Andrea Alamia
Transformer attention architectures, similar to those developed for natural language processing, have recently proved efficient also in vision, either in conjunction with or as a r…
Predictive coding feedback results in perceived illusory contours in a recurrent neural network
Zhaoyang Pang, Callum Biggs O'May, Bhavin Choksi +1
Modern feedforward convolutional neural networks (CNNs) can now solve some computer vision tasks at super-human levels. However, these networks only roughly mimic human visual perc…
Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN
Milad Mozafari, Leila Reddy, Rufin VanRullen
Decoding and reconstructing images from brain imaging data is a research area of high interest. Recent progress in deep generative neural networks has introduced new opportunities…
Which Neural Network Architecture matches Human Behavior in Artificial Grammar Learning?
Andrea Alamia, Victor Gauducheau, Dimitri Paisios +1
In recent years artificial neural networks achieved performance close to or better than humans in several domains: tasks that were previously human prerogatives, such as language p…
Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks
Rufin VanRullen, Leila Reddy
While objects from different categories can be reliably decoded from fMRI brain response patterns, it has proved more difficult to distinguish visually similar inputs, such as diff…