Systematic Generalization
62 papers with code • 0 benchmarks • 7 datasets
Benchmarks
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Libraries
Use these libraries to find Systematic Generalization models and implementations
SCAN
GSCAN
Mathematics Dataset
PCFG SET
S2B
Latest papers
Neural Compositional Rule Learning for Knowledge Graph Reasoning
vivian1993/ncrl
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NCRL detects the best compositional structure of a rule body, and breaks it into small compositions in order to infer the rule head.
What Makes a Language Easy to Deep-Learn?
Deep neural networks drive the success of natural language processing.
Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality
Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models.
VIMA: General Robot Manipulation with Multimodal Prompts
vimalabs/VIMA
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We show that a wide spectrum of robot manipulation tasks can be expressed with multimodal prompts, interleaving textual and visual tokens.
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic Approach
gtziafas/hots
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Finally, we integrate our method with a robot framework and demonstrate how it can serve as an interpretable solution for an interactive object-picking task, both in simulation and with a real robot.
On a Built-in Conflict between Deep Learning and Systematic Generalization
In this paper, we hypothesize that internal function sharing is one of the reasons to weaken o. o. d.
Meta-Referential Games to Learn Compositional Learning Behaviours
Near32/Regym
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Human beings use compositionality to generalise from past experiences to novel experiences.
Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos
Unsupervised object-centric learning aims to represent the modular, compositional, and causal structure of a scene as a set of object representations and thereby promises to resolve many critical limitations of traditional single-vector representations such as poor systematic generalization.
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing
In this work, we show that better systematic generalization can be achieved by producing the meaning representation directly as a graph and not as a sequence.
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning
This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) identifying object entities and their properties, 2) inferring semantic relations between pairs of entities, and 3) generalizing to novel object-relation combinations, i. e., systematic generalization.
