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Found 12 papers, 3 papers with code
Modelling Species Distributions with Deep Learning to Predict Plant Extinction Risk and Assess Climate Change Impacts
Cross-validation yields average accuracies of 0. 61 for status classification and 0. 78 for binary classification.
AI-based Mapping of the Conservation Status of Orchid Assemblages at Global Scale
In Sumatra, we found good correspondence of protected areas with our indicators, but supplementing current IUCN assessments with status predictions results in alarming levels of species threat across the island.
The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial resolution across Europe
We designed a European scale dataset covering around ten thousand plant species to calibrate and evaluate SDM predictions of species composition in space and time at high spatial resolution (~ten meters), and their spatial transferability.
A two-head loss function for deep Average-K classification
Average-K classification is an alternative to top-K classification in which the number of labels returned varies with the ambiguity of the input image but must average to K over all the samples.
Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classification
In modern classification tasks, the number of labels is getting larger and larger, as is the size of the datasets encountered in practice.
Controversy Detection: a Text and Graph Neural Network Based Approach
Most of the existing approaches rely on the graph structure of a topic-discussion and/or the content of messages.
The GeoLifeCLEF 2020 Dataset
Understanding the geographic distribution of species is a key concern in conservation.
Evaluation of Deep Species Distribution Models using Environment and Co-occurrences
This paper presents an evaluation of several approaches of plants species distribution modeling based on spatial, environmental and co-occurrences data using machine learning methods.
Attention-based Modeling for Emotion Detection and Classification in Textual Conversations
This paper addresses the problem of modeling textual conversations and detecting emotions.
Ranked #5 on
Emotion Recognition in Conversation
on EC
Emotion Recognition in Conversation
General Classification
+1
LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification
This paper addresses the problem of modeling textual conversations and detecting emotions.
A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources
Collaborative Filtering (CF) is one of the most commonly used recommendation methods.
LIRMM@DEFT-2018 -- Mod\`ele de classification de la vectorisation des documents (LIRMM DEFT-2018 -- Document Vectorization Classification model )
Dans ce papier, nous d{\'e}crivons notre participation au d{\'e}fi d{'}analyse de texte DEFT 2018.
