Papers Systematic Generalization
“Systematic Generalization” 태그가 달린 논문 126편 · 필터 해제
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 tradition…
DecoderObjectSystematic GeneralizationLAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing
Semantic parsing is the task of producing structured meaning representations for natural language sentences. Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggle…
Semantic ParsingSystematic GeneralizationRelViT: Concept-guided Vision Transformer for Visual Relational Reasoning
Reasoning about visual relationships is central to how humans interpret the visual world. This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems joint…
Human-Object Interaction DetectionObjectRetrievalSystematic Generalization+3Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference
We introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision. The model samples and rewards specific reasoning paths through policy gradient, in which the introspectiv…
Natural Language Inferencereinforcement-learningReinforcement Learning (RL)Systematic GeneralizationImproving Systematic Generalization Through Modularity and Augmentation
Systematic generalization is the ability to combine known parts into novel meaning; an important aspect of efficient human learning, but a weakness of neural network learning. In this work, we investigate how two well-kn…
Data AugmentationGrounded language learningSystematic GeneralizationProvably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization
In the sequential decision making setting, an agent aims to achieve systematic generalization over a large, possibly infinite, set of environments. Such environments are modeled as discrete Markov decision processes with…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+3Transformer Module Networks for Systematic Generalization in Visual Question Answering
Transformers achieve great performance on Visual Question Answering (VQA). However, their systematic generalization capabilities, i.e., handling novel combinations of known concepts, is unclear. We reveal that Neural Mod…
Question AnsweringSystematic GeneralizationVisual Question AnsweringVisual Question Answering (VQA)Systematic Generalization with Edge Transformers
Recent research suggests that systematic generalization in natural language understanding remains a challenge for state-of-the-art neural models such as Transformers and Graph Neural Networks. To tackle this challenge, w…
Dependency ParsingNatural Language UnderstandingRelational ReasoningSemantic Parsing+1ORCHARD: A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical Reasoning
The ability to reason with multiple hierarchical structures is an attractive and desirable property of sequential inductive biases for natural language processing. Do the state-of-the-art Transformers and LSTM architectu…
DiagnosticListOpsRelational ReasoningSystematic GeneralizationLearning Algebraic Representation for Systematic Generalization in Abstract Reasoning
Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in…
Abstract AlgebraSystematic GeneralizationConditional Object-Centric Learning from Video
Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built. Recent work on simple 2D and 3D datasets ha…
3D geometryInstance SegmentationObjectOptical Flow Estimation+2LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing
Semantic parsing is the task of producing structured meaning representations for natural language sentences. Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggl…
Semantic ParsingSystematic GeneralizationIlliterate DALL-E Learns to Compose
Although DALL-E has shown an impressive ability of composition-based systematic generalization in image generation, it requires the dataset of text-image pairs and the compositionality is provided by the text. In contras…
DecoderImage GenerationObjectSystematic GeneralizationChallenges in Generalization in Open Domain Question Answering
Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions. However, it is as of yet…
Natural QuestionsOpen-Domain Question AnsweringQuestion AnsweringRetrieval+2LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing
Semantic parsing is the task of producing a structured meaning representation for natural language utterances or questions. Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic p…
Semantic ParsingSystematic GeneralizationThe Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization
Despite progress across a broad range of applications, Transformers have limited success in systematic generalization. The situation is especially frustrating in the case of algorithmic tasks, where they often fail to fi…
ListOpsSystematic GeneralizationDynamic Inference with Neural Interpreters
Modern neural network architectures can leverage large amounts of data to generalize well within the training distribution. However, they are less capable of systematic generalization to data drawn from unseen but relate…
image-classificationImage ClassificationSystematic GeneralizationDistinguishing rule- and exemplar-based generalization in learning systems
Machine learning systems often do not share the same inductive biases as humans and, as a result, extrapolate or generalize in ways that are inconsistent with our expectations. The trade-off between exemplar- and rule-ba…
BIG-bench Machine LearningData AugmentationFairnessSystematic GeneralizationLearning Adaptive Control Flow in Transformers for Improved Systematic Generalization
Despite successes across a broad range of applications, Transformers have limited capability in systematic generalization. The situation is especially frustrating for algorithmic tasks, where they often fail to find intu…
Systematic GeneralizationAdaptive Control Flow in Transformers Improves Systematic Generalization
Despite successes across a broad range of applications, Transformers have limited capability in systematic generalization. The situation is especially frustrating in the case of algorithmic tasks, where they often fail t…
ListOpsSystematic Generalization