27 citations · 44 across the 18 of their papers we have counts for
18 papers
Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation
Yohann Benchetrit, Marlène Careil, Simon Dahan +3
Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate whether and when brain deco…
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
Hubert Banville, Stéphane d'Ascoli, Simon Dahan +12
Deep learning and large public datasets have recently catalyzed the proliferation of AI models for processing brain recordings. However, systematically evaluating these models rema…
NeuralSet: A High-Performing Python Package for Neuro-AI
Jean-Rémi King, Corentin Bel, Linnea Evanson +25
Artificial intelligence (AI) is increasingly central to understanding how the brain processes information. However, the integration of neuroscience and modern AI is bottlenecked by…
A foundation model of vision, audition, and language for in-silico neuroscience
Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit +5
Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of cognition in the human brain. He…
TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction
Stéphane d'Ascoli, Jérémy Rapin, Yohann Benchetrit +2
Historically, neuroscience has progressed by fragmenting into specialized domains, each focusing on isolated modalities, tasks, or brain regions. While fruitful, this approach hind…
Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI
Marlène Careil, Yohann Benchetrit, Jean-Rémi King
Brain-to-image decoding has been recently propelled by the progress in generative AI models and the availability of large ultra-high field functional Magnetic Resonance Imaging (fM…
Scaling laws for decoding images from brain activity
Hubert Banville, Yohann Benchetrit, Stéphane d'Ascoli +2
Generative AI has recently propelled the decoding of images from brain activity. How do these approaches scale with the amount and type of neural recordings? Here, we systematicall…
EvalGIM: A Library for Evaluating Generative Image Models
Melissa Hall, Oscar Mañas, Reyhane Askari-Hemmat +14
As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, while metrics and datasets abound…
Decoding individual words from non-invasive brain recordings across 723 participants
Stéphane d'Ascoli, Corentin Bel, Jérémy Rapin +4
Deep learning has recently enabled the decoding of language from the neural activity of a few participants with electrodes implanted inside their brain. However, reliably decoding…
Boosting Latent Diffusion with Perceptual Objectives
Tariq Berrada, Pietro Astolfi, Melissa Hall +6
Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produ…
On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
Tariq Berrada Ifriqi, Pietro Astolfi, Melissa Hall +8
Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, the key components of the best performing LDM training recipe…
Aligning brain functions boosts the decoding of visual semantics in novel subjects
Alexis Thual, Yohann Benchetrit, Felix Geilert +4
Deep learning is leading to major advances in the realm of brain decoding from functional Magnetic Resonance Imaging (fMRI). However, the large inter-subject variability in brain c…
Brain decoding: toward real-time reconstruction of visual perception
Yohann Benchetrit, Hubert Banville, Jean-Rémi King
In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded…
Characterizing Polytopes Contained in the -Cube with Bounded Chvátal-Gomory Rank
Yohann Benchetrit, Samuel Fiorini, Tony Huynh +1
Let and be any polytope contained in with . We prove that has bounded Chvátal-Gomory rank (CG-rank) provided that $S…
On 4-critical t-perfect graphs
Yohann Benchetrit
It is an open question whether the chromatic number of -perfect graphs is bounded by a constant. The largest known value for this parameter is 4, and the only example of a 4-cri…
-perfect plane triangulations
Yohann Benchetrit, Henning Bruhn
We characterise -perfect plane triangulations by forbidden induced subgraphs. As a consequence, we obtain that a plane triangulation is -perfect if and only if it is perfect.
Ear-decompositions and the complexity of the matching polytope
Yohann Benchetrit, András Sebő
The complexity of the matching polytope of graphs may be measured with the maximum length of a starting sequence of odd ears in an ear-decomposition. Indeed, a theorem of Edmon…
Integer round-up property for the chromatic number of some h-perfect graphs
Yohann Benchetrit
A graph is h-perfect if its stable set polytope can be completely described by non-negativity, clique and odd-hole constraints. It is t-perfect if it furthermore has no clique of s…