Papers Systematic Generalization
“Systematic Generalization” 태그가 달린 논문 126편 · 필터 해제
Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey
A core aspect of compositionality, systematicity is a desirable property in ML models as it enables strong generalization to novel contexts. This has led to numerous studies proposing benchmarks to assess systematic gene…
Systematic GeneralizationSystematic Generalization in Language Models Scales with Information Entropy
Systematic generalization remains challenging for current language models, which are known to be both sensitive to semantically similar permutations of the input and to struggle with known concepts presented in novel con…
Systematic GeneralizationEnabling Systematic Generalization in Abstract Spatial Reasoning through Meta-Learning for Compositionality
Systematic generalization refers to the capacity to understand and generate novel combinations from known components. Despite recent progress by large language models (LLMs) across various domains, these models often fai…
Meta-LearningSpatial ReasoningSystematic GeneralizationTranslationFlorenz: Scaling Laws for Systematic Generalization in Vision-Language Models
Cross-lingual transfer enables vision-language models (VLMs) to perform vision tasks in various languages with training data only in one language. Current approaches rely on large pre-trained multilingual language models…
Cross-Lingual TransferImage CaptioningLarge Language ModelMachine Translation+3Enhancing NLP Robustness and Generalization through LLM-Generated Contrast Sets: A Scalable Framework for Systematic Evaluation and Adversarial Training
Standard NLP benchmarks often fail to capture vulnerabilities stemming from dataset artifacts and spurious correlations. Contrast sets address this gap by challenging models near decision boundaries but are traditionally…
DiversitySystematic GeneralizationUnveiling the Mechanisms of Explicit CoT Training: How CoT Enhances Reasoning Generalization
The integration of explicit Chain-of-Thought (CoT) reasoning into training large language models (LLMs) has advanced their reasoning capabilities, yet the mechanisms by which CoT enhances generalization remain poorly und…
Generalization BoundsSystematic GeneralizationTowards Conscious Service Robots
Deep learning's success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state space…
Systematic GeneralizationInductive Biases for Zero-shot Systematic Generalization in Language-informed Reinforcement Learning
Sample efficiency and systematic generalization are two long-standing challenges in reinforcement learning. Previous studies have shown that involving natural language along with other observation modalities can improve …
Decision MakingSystematic GeneralizationData Distributional Properties As Inductive Bias for Systematic Generalization
Deep neural networks (DNNs) struggle at systematic generalization (SG). Several studies have evaluated the possibility of promoting SG through the proposal of novel architectures, loss functions, or training methodol…
DiversityInductive BiasOut-of-Distribution GeneralizationSystematic GeneralizationCAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives
Grounding the instruction in the environment is a key step in solving language-guided goal-reaching reinforcement learning problems. In automated reinforcement learning, a key concern is to enhance the model's ability to…
reinforcement-learningReinforcement LearningRetrievalSystematic Generalization+2Is Multiple Object Tracking a Matter of Specialization?
End-to-end transformer-based trackers have achieved remarkable performance on most human-related datasets. However, training these trackers in heterogeneous scenarios poses significant challenges, including negative inte…
AttributeDomain GeneralizationMultiple Object TrackingObject+3Neural networks that overcome classic challenges through practice
Since the earliest proposals for neural network models of the mind and brain, critics have pointed out key weaknesses in these models compared to human cognitive abilities. Here we review recent work that uses metalearni…
Few-Shot LearningSystematic GeneralizationMany-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes
Graph-based machine learning models for materials properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simplest forms, graph-based models do not incl…
Systematic GeneralizationOn The Specialization of Neural Modules
A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason about new situations by combining aspects of previous experiences. These models leverage …
Systematic GeneralizationSystematic Reasoning About Relational Domains With Graph Neural Networks
Developing models that can learn to reason is a notoriously challenging problem. We focus on reasoning in relational domains, where the use of Graph Neural Networks (GNNs) seems like a natural choice. However, previous w…
Inductive BiasSystematic GeneralizationCompositional Models for Estimating Causal Effects
Many real-world systems can be usefully represented as sets of interacting components. Examples include computational systems, such as query processors and compilers, natural systems, such as cells and ecosystems, and so…
Causal InferencecounterfactualSystematic GeneralizationA Systematization of the Wagner Framework: Graph Theory Conjectures and Reinforcement Learning
In 2021, Adam Zsolt Wagner proposed an approach to disprove conjectures in graph theory using Reinforcement Learning (RL). Wagner's idea can be framed as follows: consider a conjecture, such as a certain quantity f(G) < …
Reinforcement Learning (RL)Systematic GeneralizationIdentifiable Object-Centric Representation Learning via Probabilistic Slot Attention
Learning modular object-centric representations is crucial for systematic generalization. Existing methods show promising object-binding capabilities empirically, but theoretical identifiability guarantees remain relativ…
ObjectRepresentation LearningSystematic GeneralizationDiscrete Dictionary-based Decomposition Layer for Structured Representation Learning
Neuro-symbolic neural networks have been extensively studied to integrate symbolic operations with neural networks, thereby improving systematic generalization. Specifically, Tensor Product Representation (TPR) framework…
Representation LearningSystematic GeneralizationAttention-based Iterative Decomposition for Tensor Product Representation
In recent research, Tensor Product Representation (TPR) is applied for the systematic generalization task of deep neural networks by learning the compositional structure of data. However, such prior works show limited pe…
Systematic Generalization