Papers Systematic Generalization
“Systematic Generalization” 태그가 달린 논문 126편 · 필터 해제
Illiterate DALL$\cdot$E Learns to Compose
DALL$\cdot$E has shown an impressive ability of composition-based systematic generalization in image generation. This is possible because it utilizes the dataset of text-image pairs where the text provides the source of …
DecoderImage GenerationSystematic GeneralizationThe Role of Learning Regime, Architecture and Dataset Structure on Systematic Generalization in Simple Neural Networks
Humans often systematically generalize in situations where standard deep neural networks do not. Empirical studies have shown that the learning procedure and network architecture can influence systematicity in deep netwo…
Systematic GeneralizationSymbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics
Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance. However, their ability to achieve stro…
Out-of-Distribution GeneralizationSystematic GeneralizationSystematic Generalization on gSCAN: What is Nearly Solved and What is Next?
We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding. First, we study which aspects of the original benchmark can be solved by…
Compositional Generalization (AVG)Systematic 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 unclear w…
Natural QuestionsOpen-Domain Question AnsweringQuestion AnsweringRetrieval+2The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers
Recently, many datasets have been proposed to test the systematic generalization ability of neural networks. The companion baseline Transformers, typically trained with default hyper-parameters from standard tasks, are s…
Systematic GeneralizationLexicon Learning for Few Shot Sequence Modeling
Sequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural network models that provide the dominan…
Instruction FollowingMachine TranslationSemantic ParsingSystematic Generalization+1Systematic human learning and generalization from a brief tutorial with explanatory feedback
Neural networks have long been used to model human intelligence, capturing elements of behavior and cognition, and their neural basis. Recent advancements in deep learning have enabled neural network models to reach and …
High School MathematicsSystematic GeneralizationvalidDiscrete-Valued Neural Communication
Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed of positional elements, modular architectures divided into slots, and graph neu…
QuantizationSystematic GeneralizationGeneralization and Robustness Implications in Object-Centric Learning
The idea behind object-centric representation learning is that natural scenes can better be modeled as compositions of objects and their relations as opposed to distributed representations. This inductive bias can be inj…
Inductive BiasObjectProperty PredictionRepresentation Learning+1How Modular Should Neural Module Networks Be for Systematic Generalization?
Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task. NMNs are a promising strategy to achieve systematic generalization, i.e., overcoming biasing factors…
Question AnsweringSystematic GeneralizationVisual Question AnsweringVisual Question Answering (VQA)Lexicon Learning for Few-Shot Neural Sequence Modeling
Sequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural network models that provide the dominan…
Instruction FollowingMachine TranslationSemantic ParsingSystematic Generalization+1Structured Reordering for Modeling Latent Alignments in Sequence Transduction
Despite success in many domains, neural models struggle in settings where train and test examples are drawn from different distributions. In particular, in contrast to humans, conventional sequence-to-sequence (seq2seq) …
Machine TranslationSemantic ParsingSystematic GeneralizationTranslationSyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics
Recently, deep neural networks (DNNs) have achieved great success in semantically challenging NLP tasks, yet it remains unclear whether DNN models can capture compositional meanings, those aspects of meaning that have be…
NegationSystematic GeneralizationModelling the development of counting with memory-augmented neural networks
Learning to count is an important example of the broader human capacity for systematic generalization, and the development of counting is often characterized by an inflection point when children rapidly acquire proficien…
Systematic GeneralizationIterated learning for emergent systematicity in VQA
Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize systematically in practice. When instead learning layouts and modules jointly, composi…
Question AnsweringSystematic GeneralizationVisual Question AnsweringVisual Question Answering (VQA)A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics
Inspired by humans' exceptional ability to master arithmetic and generalize to new problems, we present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machines' capability of learning generalizabl…
Few-Shot LearningProgram SynthesisSystematic GeneralizationSystematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models
Systematic generalization aims to evaluate reasoning about novel combinations from known components, an intrinsic property of human cognition. In this work, we study systematic generalization of NNs in forecasting future…
Inductive BiasMultivariate Time Series ForecastingOut-of-Distribution GeneralizationSystematic Generalization+3The role of Disentanglement in Generalisation
Combinatorial generalization — the ability to understand and produce novel combinations of familiar elements — is considered a core capacity of humans that modern AI systems currently struggle at. Recently, it has been s…
DisentanglementOut-of-Distribution GeneralizationSystematic GeneralizationGenerative Scene Graph Networks
Human perception excels at building compositional hierarchies of parts and objects from unlabeled scenes that help systematic generalization. Yet most work on generative scene modeling either ignores the part-whole relat…
Systematic Generalization