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Found 10 papers, 5 papers with code
A Sparsity Principle for Partially Observable Causal Representation Learning
Causal representation learning aims at identifying high-level causal variables from perceptual data.
Capture the Flag: Uncovering Data Insights with Large Language Models
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making.
Multi-View Causal Representation Learning with Partial Observability
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities.
OC-NMN: Object-centric Compositional Neural Module Network for Generative Visual Analogical Reasoning
A key aspect of human intelligence is the ability to imagine -- composing learned concepts in novel ways -- to make sense of new scenarios.
Using Graph Algorithms to Pretrain Graph Completion Transformers
While using our new path-finding algorithm as a pretraining signal provides 2-3% MRR improvements, we show that pretraining on all signals together gives the best knowledge graph completion results.
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class.
Knowledge Hypergraph Embedding Meets Relational Algebra
Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation.
Knowledge Hypergraphs: Prediction Beyond Binary Relations
Knowledge graphs store facts using relations between two entities.
Context-Aware Visual Compatibility Prediction
How do we determine whether two or more clothing items are compatible or visually appealing?
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Efficient Multi-Robot Coverage of a Known Environment
In this paper we present two approximation heuristics for solving the multi-robot coverage problem.
