Vincent Schellekens

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 19

Top Topics

Agglomerative Hierarchical Clustering

Back Projection

Compressive Sensing

Online Learning

Top Venues

Publications

Rémi Delogne, Vincent Schellekens, Laurent Daudet, Laurent Jacques
Signal Processing with Optical Quadratic Random Sketches. ICASSP (2023)
Florimond Houssiau, Vincent Schellekens, Antoine Chatalic, Shreyas Kumar Annamraju, Yves-Alexandre de Montjoye
M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch. STM (2022)
Rémi Delogne, Vincent Schellekens, Laurent Jacques
ROP inception: signal estimation with quadratic random sketching. CoRR (2022)
Rémi Delogne, Vincent Schellekens, Laurent Jacques
ROP inception: signal estimation with quadratic random sketching. ESANN (2022)
Vincent Schellekens, Laurent Jacques
Asymmetric Compressive Learning Guarantees With Applications to Quantized Sketches. IEEE Trans. Signal Process. 70 (2022)
Florimond Houssiau, Vincent Schellekens, Antoine Chatalic, Shreyas Kumar Annamraju, Yves-Alexandre de Montjoye
M: A general method to perform various data analysis tasks from a differentially private sketch. CoRR (2022)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter
Sketching Data Sets for Large-Scale Learning: Keeping only what you need. IEEE Signal Process. Mag. 38 (5) (2021)
Vincent Schellekens, Laurent Jacques
Asymmetric compressive learning guarantees with applications to quantized sketches. CoRR (2021)
Vincent Schellekens, Laurent Jacques
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines. CoRR (2020)
Vincent Schellekens, Laurent Jacques
When compressive learning fails: blame the decoder or the sketch? CoRR (2020)
Vincent Schellekens, Laurent Jacques
Compressive Learning of Generative Networks. ESANN (2020)
Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter
Sketching Datasets for Large-Scale Learning (long version). CoRR (2020)
Vincent Schellekens, Laurent Jacques
Compressive Learning of Generative Networks. CoRR (2020)
Vincent Schellekens, Antoine Chatalic, Florimond Houssiau, Yves-Alexandre de Montjoye, Laurent Jacques, Rémi Gribonval
Differentially Private Compressive K-means. ICASSP (2019)
Chunlei Xu, Vincent Schellekens, Laurent Jacques
Taking the Edge off Quantization: Projected Back Projection in Dithered Compressive Sensing. SSP (2018)
Vincent Schellekens, Laurent Jacques
Compressive Classification (Machine Learning without learning). CoRR (2018)
Vincent Schellekens, Laurent Jacques
Quantized Compressive K-Means. CoRR (2018)
Chunlei Xu, Vincent Schellekens, Laurent Jacques
Taking the edge off quantization: projected back projection in dithered compressive sensing. CoRR (2018)
Vincent Schellekens, Laurent Jacques
Quantized Compressive K-Means. IEEE Signal Process. Lett. 25 (8) (2018)