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Found 25 papers, 11 papers with code
Challenges in anomaly and change point detection
On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced.
Is the U-Net Directional-Relationship Aware?
CNNs are often assumed to be capable of using contextual information about distinct objects (such as their directional relations) inside their receptive field.
Multi-winner Approval Voting Goes Epistemic
Epistemic voting interprets votes as noisy signals about a ground truth.
Non parametric estimation of causal populations in a counterfactual scenario
A Causal Auto-Encoder (CAE), enhanced by a prior dependent on treatment and outcome information, assimilates the latent space to the probability distribution of the target populations.
Truth-tracking via Approval Voting: Size Matters
Epistemic social choice aims at unveiling a hidden ground truth given votes, which are interpreted as noisy signals about it.
A new Sinkhorn algorithm with Deletion and Insertion operations
Conversely, the remaining elements of V2 correspond to the image of the epsilon pseudo element of V1.
Functional connectivity ensemble method to enhance BCI performance (FUCONE)
Functional connectivity is a key approach to investigate oscillatory activities of the brain that provides important insights on the underlying dynamic of neuronal interactions and that is mostly applied for brain activity analysis.
The Minimum Edit Arborescence Problem and Its Use in Compressing Graph Collections [Extended Version]
The inference of minimum spanning arborescences within a set of objects is a general problem which translates into numerous application-specific unsupervised learning tasks.
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
In this paper, we consider the task of predicting $Y$ from $X$ when we have no paired data of them, but we have two separate, independent datasets of $X$ and $Y$ each observed with some mediating variable $U$, that is, we have two datasets $S_X = \{(X_i, U_i)\}$ and $S_Y = \{(U'_j, Y'_j)\}$.
Template-Based Graph Clustering
We propose a novel graph clustering method guided by additional information on the underlying structure of the clusters (or communities).
Scaling up graph homomorphism for classification via sampling
Feature generation is an open topic of investigation in graph machine learning.
Scaling Up Graph Homomorphism Features with Efficient Data Structures
Typical datasets used in graph classification tasks only contain a few thousand graphs which rarely exceed hundreds of nodes.
On the robustness of randomized classifiers to adversarial examples
This paper investigates the theory of robustness against adversarial attacks.
Approximation of dilation-based spatial relations to add structural constraints in neural networks
Several relations can be modeled as a morphological dilation of a reference object with a structuring element representing the semantics of the relation, from which the degree of satisfaction of the relation between another object and the reference object can be derived.
RIGOLETTO -- RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge -- WCCI2020
This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020.
Estimating Individual Treatment Effects through Causal Populations Identification
Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one of both, is a challenging problems in causal learning.
A unified view on differential privacy and robustness to adversarial examples
This short note highlights some links between two lines of research within the emerging topic of trustworthy machine learning: differential privacy and robustness to adversarial examples.
Theoretical evidence for adversarial robustness through randomization
This paper investigates the theory of robustness against adversarial attacks.
Uplift Modeling from Separate Labels
Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments).
Graph-based Clustering under Differential Privacy
In this paper, we present the first differentially private clustering method for arbitrary-shaped node clusters in a graph.
Recognizing Art Style Automatically in painting with deep learning
The artistic style (or artistic movement) of a painting is a rich descriptor that captures both visual and historical information about the painting.
Ranked #1 on
Artistic style classification
on RASTA
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well.
Geometry-aware stationary subspace analysis
The classic SSA method finds a matrix that projects the data onto a stationary subspace by optimizing a cost function based on a matrix divergence.
Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices
Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification.
Challenge IEEE-ISBI/TCB : Application of Covariance matrices and wavelet marginals
This short memo aims at explaining our approach for the challenge IEEE-ISBI on Bone Texture Characterization.
