Search Results for author:
Found 15 papers, 6 papers with code
PFStorer: Personalized Face Restoration and Super-Resolution
In our approach a restoration model is personalized using a few images of the identity, leading to tailored restoration with respect to the identity while retaining fine-grained details.
On the Potential of Network-Based Features for Fraud Detection
Online transaction fraud presents substantial challenges to businesses and consumers, risking significant financial losses.
CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning
Causal discovery is the challenging task of inferring causal structure from data.
Emergent Cooperation under Uncertain Incentive Alignment
Understanding the emergence of cooperation in systems of computational agents is crucial for the development of effective cooperative AI.
Explainable Fraud Detection with Deep Symbolic Classification
There is a growing demand for explainable, transparent, and data-driven models within the domain of fraud detection.
AI4GCC - Team: Below Sea Level: Critiques and Improvements
We present a critical analysis of the simulation framework RICE-N, an integrated assessment model (IAM) for evaluating the impacts of climate change on the economy.
AI4GCC-Team -- Below Sea Level: Score and Real World Relevance
As our submission for track three of the AI for Global Climate Cooperation (AI4GCC) competition, we propose a negotiation protocol for use in the RICE-N climate-economic simulation.
A Meta-Learning Algorithm for Interrogative Agendas
These algorithms use a single concept lattice for such a task, meaning that the set of features used for the categorization is fixed.
A Meta-Reinforcement Learning Algorithm for Causal Discovery
Causal discovery is a major task with the utmost importance for machine learning since causal structures can enable models to go beyond pure correlation-based inference and significantly boost their performance.
Hyperbolic Image Segmentation
For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes.
Reasoning with Contextual Knowledge and Influence Diagrams
Influence diagrams (IDs) are well-known formalisms extending Bayesian networks to model decision situations under uncertainty.
Analyzing Differentiable Fuzzy Implications
In this paper, we investigate how implications from the fuzzy logic literature behave in a differentiable setting.
On Sufficient and Necessary Conditions in Bounded CTL: A Forgetting Approach
To address such a scenario in a principled way, we introduce a forgetting-based approach in CTL and show that it can be used to compute SNC and WSC of a property under a given model and over a given signature.
Analyzing Differentiable Fuzzy Logic Operators
Finally, we empirically show that it is possible to use Differentiable Fuzzy Logics for semi-supervised learning, and compare how different operators behave in practice.
Semi-Supervised Learning using Differentiable Reasoning
We introduce Differentiable Reasoning (DR), a novel semi-supervised learning technique which uses relational background knowledge to benefit from unlabeled data.
