most citedFirst steps towards quantum machine learning applied to the classification of event-related potentials

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

cs.CL2026

Measuring Optimal Transport in Transformer Depth

Alexandre Quemy

A transformer carries each token's state from layer to layer, and the whole vocabulary carried together forms a cloud that moves with depth. We ask whether a trained network moves…

cs.CL2026

The Depth Flow of Token Representations Is Nonlinear and Does Not Descend Its Own Density

Alexandre Quemy

A token's representation is carried through the network layer by layer. The whole vocabulary carried together forms a flow. We fit this flow's equation of motion as a discrete Lang…

cs.CL2026

A Hub of Short Rows Inflates Intrinsic Dimension Estimation of Token Embeddings

Alexandre Quemy

A token-embedding table holds a hub of short rows near its origin, and we show that this cluster biases what nearest-neighbor intrinsic-dimension (ID) estimators report. Because of…

cs.AI2023

MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with Known Pareto Front

Alexandre Quemy, Marc Schoenauer, Johann Dreo

Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts. In this work, we propose a tunable benchmark generator, together with a dedicated solv…

cs.HC20231 cited

First steps towards quantum machine learning applied to the classification of event-related potentials

Grégoire Cattan, Alexandre Quemy, Anton Andreev

Low information transfer rate is a major bottleneck for brain-computer interfaces based on non-invasive electroencephalography (EEG) for clinical applications. This led to the deve…