Search Results for author:
Found 15 papers, 8 papers with code
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking
This paper presents FlowCyt, the first comprehensive benchmark for multi-class single-cell classification in flow cytometry data.
HemaGraph: Breaking Barriers in Hematologic Single Cell Classification with Graph Attention
To the best of our knowledge, this is the first effort to use GATs, and Graph Neural Networks (GNNs) in general, to classify cell populations from single-cell flow cytometry data.
Why Attention Graphs Are All We Need: Pioneering Hierarchical Classification of Hematologic Cell Populations with LeukoGraph
LeukoGraph intricately addresses a classification paradigm where for example four different cell populations undergo flat categorization, while a fifth diverges into two distinct child branches, exemplifying the nuanced hierarchical structure inherent in complex datasets.
Supervised Auto-Encoding Twin-Bottleneck Hashing
Other existing methods construct the similarity graph and consider all points simultaneously.
Cold Start Active Learning Strategies in the Context of Imbalanced Classification
We present novel active learning strategies dedicated to providing a solution to the cold start stage, i. e. initializing the classification of a large set of data with no attached labels.
Optimality Inductive Biases and Agnostic Guidelines for Offline Reinforcement Learning
Only our framework allowed us to design a method that performed well across the spectrum while remaining modular if more information about the quality of the data ever becomes available.
Towards Efficient Cross-Modal Visual Textual Retrieval using Transformer-Encoder Deep Features
It is designed for producing fixed-size 1024-d vectors describing whole images and sentences, as well as variable-length sets of 1024-d vectors describing the various building components of the two modalities (image regions and sentence words respectively).
Computing flood probabilities using Twitter: application to the Houston urban area during Harvey
In this paper, we investigate the conversion of a Twitter corpus into geo-referenced raster cells holding the probability of the associated geographical areas of being flooded.
Fine-grained Visual Textual Alignment for Cross-Modal Retrieval using Transformer Encoders
In this work, we tackle the task of cross-modal retrieval through image-sentence matching based on word-region alignments, using supervision only at the global image-sentence level.
Ranked #6 on
Image Retrieval
on Flickr30K 1K test
Extracting localized information from a Twitter corpus for flood prevention
In this paper, we discuss the collection of a corpus associated to tropical storm Harvey, as well as its analysis from both spatial and topical perspectives.
Structured nonlinear variable selection
We investigate structured sparsity methods for variable selection in regression problems where the target depends nonlinearly on the inputs.
Large-scale Nonlinear Variable Selection via Kernel Random Features
We propose a new method for input variable selection in nonlinear regression.
Forecasting and Granger Modelling with Non-linear Dynamical Dependencies
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series.
On Hölder projective divergences
We describe a framework to build distances by measuring the tightness of inequalities, and introduce the notion of proper statistical divergences and improper pseudo-divergences.
Learning Leading Indicators for Time Series Predictions
We consider the problem of learning models for forecasting multiple time-series systems together with discovering the leading indicators that serve as good predictors for the system.
