Papers Systematic Generalization
“Systematic Generalization” 태그가 달린 논문 126편 · 필터 해제
Mixture of Latent Experts Using Tensor Products
In multi-task learning, the conventional approach involves training a model on multiple tasks simultaneously. However, the training signals from different tasks can interfere with one another, potentially leading to \tex…
Language ModellingMulti-Task LearningSystematic GeneralizationTransfer LearningWhat makes Models Compositional? A Theoretical View: With Supplement
Compositionality is thought to be a key component of language, and various compositional benchmarks have been developed to empirically probe the compositional generalization of existing sequence processing models. These …
Systematic GeneralizationLearning to Compose: Improving Object Centric Learning by Injecting Compositionality
Learning compositional representation is a key aspect of object-centric learning as it enables flexible systematic generalization and supports complex visual reasoning. However, most of the existing approaches rely on au…
ObjectSystematic GeneralizationvalidVisual ReasoningComposing Pre-Trained Object-Centric Representations for Robotics From "What" and "Where" Foundation Models
There have recently been large advances both in pre-training visual representations for robotic control and segmenting unknown category objects in general images. To leverage these for improved robot learning, we propose…
ObjectSystematic GeneralizationSlot Abstractors: Toward Scalable Abstract Visual Reasoning
Abstract visual reasoning is a characteristically human ability, allowing the identification of relational patterns that are abstracted away from object features, and the systematic generalization of those patterns to un…
ObjectSystematic GeneralizationVisual ReasoningCase-Based or Rule-Based: How Do Transformers Do the Math?
Despite the impressive performance in a variety of complex tasks, modern large language models (LLMs) still have trouble dealing with some math problems that are simple and intuitive for humans, such as addition. While w…
MathSystematic GeneralizationA Neural Rewriting System to Solve Algorithmic Problems
Modern neural network architectures still struggle to learn algorithmic procedures that require to systematically apply compositional rules to solve out-of-distribution problem instances. In this work, we focus on formul…
Language ModelingLanguage ModellingLarge Language ModelOut-of-Distribution Generalization+1Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies
Large Language Models (LLMs) have revolutionized the field of Natural Language Processing thanks to their ability to reuse knowledge acquired on massive text corpora on a wide variety of downstream tasks, with minimal (i…
BenchmarkingSystematic GeneralizationUnsupervised Discovery of Object-Centric Neural Fields
We study inferring 3D object-centric scene representations from a single image. While recent methods have shown potential in unsupervised 3D object discovery from simple synthetic images, they fail to generalize to real-…
ObjectObject DiscoverySemantic SegmentationSystematic Generalization+1On the generalization capacity of neural networks during generic multimodal reasoning
The advent of the Transformer has led to the development of large language models (LLM), which appear to demonstrate human-like capabilities. To assess the generality of this class of models and a variety of other base n…
Multimodal ReasoningSystematic GeneralizationInterpretability Illusions in the Generalization of Simplified Models
A common method to study deep learning systems is to use simplified model representations--for example, using singular value decomposition to visualize the model's hidden states in a lower dimensional space. This approac…
Code CompletionDimensionality ReductionSystematic GeneralizationGenerating Interpretable Networks using Hypernetworks
An essential goal in mechanistic interpretability to decode a network, i.e., to convert a neural network's raw weights to an interpretable algorithm. Given the difficulty of the decoding problem, progress has been made t…
Systematic GeneralizationImagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models
Systematic compositionality, or the ability to adapt to novel situations by creating a mental model of the world using reusable pieces of knowledge, remains a significant challenge in machine learning. While there has be…
Systematic GeneralizationSIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation
Strong inductive biases enable learning from little data and help generalization outside of the training distribution. Popular neural architectures such as Transformers lack strong structural inductive biases for seq2seq…
Few-Shot LearningInductive BiasSystematic GeneralizationCompositional Program Generation for Few-Shot Systematic Generalization
Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples. Neural machine learning models, including the now ubiquitous Transformers, struggle to generaliz…
Systematic GeneralizationD3: Data Diversity Design for Systematic Generalization in Visual Question Answering
Systematic generalization is a crucial aspect of intelligence, which refers to the ability to generalize to novel tasks by combining known subtasks and concepts. One critical factor that has been shown to influence syste…
DiversityQuestion AnsweringSystematic GeneralizationVisual Question Answering+1Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models
We investigate how to elicit compositional generalization capabilities in large language models (LLMs). Compositional generalization empowers LLMs to solve complex problems by combining foundational skills, a critical re…
In-Context LearningMathMathematical ReasoningMath Word Problem Solving+1A Hybrid System for Systematic Generalization in Simple Arithmetic Problems
Solving symbolic reasoning problems that require compositionality and systematicity is considered one of the key ingredients of human intelligence. However, symbolic reasoning is still a great challenge for deep learning…
Language ModelingLanguage ModellingLarge Language ModelSystematic GeneralizationLearning to Substitute Spans towards Improving Compositional Generalization
Despite the rising prevalence of neural sequence models, recent empirical evidences suggest their deficiency in compositional generalization. One of the current de-facto solutions to this problem is compositional data au…
Data AugmentationInductive BiasSystematic GeneralizationSystematic Visual Reasoning through Object-Centric Relational Abstraction
Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and to systematically generalize those patterns to novel inputs. This capacity depends in large pa…
ObjectSystematic GeneralizationVisual Reasoning