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Found 15 papers, 4 papers with code
Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
In recent years, there has been a growing interest in using machine learning techniques for the estimation of treatment effects.
Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR Data
Airborne LiDAR systems have the capability to capture the Earth's surface by generating extensive point cloud data comprised of points mainly defined by 3D coordinates.
Time-based Self-supervised Learning for Wireless Capsule Endoscopy
State-of-the-art machine learning models, and especially deep learning ones, are significantly data-hungry; they require vast amounts of manually labeled samples to function correctly.
Deep Non-Crossing Quantiles through the Partial Derivative
Quantile Regression (QR) provides a way to approximate a single conditional quantile.
Algorithmic Causal Effect Identification with causaleffect
Our evolution as a species made a huge step forward when we understood the relationships between causes and effects.
Graph Convolutional Embeddings for Recommender Systems
In this work, we generalize the use of GCNs for N-partite graphs by considering N multiple context dimensions and propose a simple way for their seamless integration in modern deep learning RS architectures.
Causal Inference with Deep Causal Graphs
Parametric causal modelling techniques rarely provide functionality for counterfactual estimation, often at the expense of modelling complexity.
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability
Accountability of such solutions is a challenge for the auditors and the machine learning community.
WCE Polyp Detection with Triplet based Embeddings
Automatic image analysis methods can be used to reduce the time needed for physicians to evaluate a capsule endoscopy video, however these methods are still in a research phase.
Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians
In this paper, we propose a generic deep learning framework that learns an Uncountable Mixture of Asymmetric Laplacians (UMAL), which will allow us to estimate heterogeneous distributions of the output variable and shows its connections to quantile regression.
Explaining Visual Models by Causal Attribution
Model explanations based on pure observational data cannot compute the effects of features reliably, due to their inability to estimate how each factor alteration could affect the rest.
Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series
Deep Learning is a consolidated, state-of-the-art Machine Learning tool to fit a function when provided with large data sets of examples.
Uncertainty Gated Network for Land Cover Segmentation
The production of thematic maps depicting land cover is one of the most common applications of remote sensing.
Generic Feature Learning for Wireless Capsule Endoscopy Analysis
Most of the CAD systems in the capsule endoscopy share a common system design, but use very different image and video representations.
Learning to count with deep object features
Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective.
