Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 11 of 11 for “"Compositional generalization"”.
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COMPOSITIONAL GENERALIZATION IN INSTRUCTION FOLLOWING TASKS
… use while giving and following instructions is compositionality: the capacity to understand and produce a potentially infinite number of novel combinations from familiar components. This ability is instrumental in being able to learn from limited data and is crucial for instruction following …
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Enabling Compositional Generalization of AI Systems
… thesis aims to bridge this gap by incorporating compositionality into deep neural networks, thereby enhancing their ability to generalize and solve novel and complex tasks, such as generating 2D images and 3D assets based on complicated specifications, or enabling humanoid agents to perform a …
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Factorization and Compositional Generalization in Diffusion Models
… the defining features of human intelligence is compositionality—the ability to generate an infinite array of complex ideas from a limited set of components. This capacity allows for the creation of novel and intricate combinations of arbitrary concepts, enabling potentially infinite expressive …
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Baba is AI: A Grounded Benchmark for Compositional Generalization in Dynamic Rule Systems
People leverage the compositional nature of their environment to generalize to new scenarios. For example, if you understand the meaning of the verb "to sing" and the adverb "loudly," then you can determine the meaning of the novel phrase "to sing loudly" from these known components. This process …
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Explainable Multi-Step Reasoning Over Natural Language
… Third, neural language models suffer from compositional generalization issues when solving multi-step reasoning problems, meaning that when the models are trained on simple tasks but tested on the hard tasks that require more reasoning steps than in training, they tend to fail. In this …
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Compositional Robot Learning for Generalizable Interactions
… many deep-learning based methods fail at compositional generalization, i.e., an ability to generalize to novel combinations of concepts that have not been seen before in training. This thesis presents several learning-based approaches that leverage compositionally to enable generalization …
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Compositional Models for Few Shot Sequence Learning
… models perform poorly in settings requiring compositional generalization beyond the training data—particularly to rare or unseen subsequences. Past work has found symbolic scaffolding (e.g. grammars or automata) essential in these settings. We describe two simpler and more general modeling …
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Video as the Language of Embodied Intelligence
… sample fidelity, temporal consistency, and compositional generalization. Together, these methods enable robust modeling of visual dynamics across extended timeframes. Finally, we present a preliminary yet promising video foundation model for zero-shot robot motion planning, highlighting the …
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Inference-Time Learning Algorithms of Language Models
… LMs. I demonstrate that LMs can achieve strong compositional generalization when provided with few-shot examples. In a separate analysis, I show that their performance deteriorates significantly when faced with counterfactual variants of tasks they normally performed well on. Later, I develop …
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Adversarial Déjà Vu: Jailbreak Dictionary Learning for Stronger Generalization to Unseen Attacks
… by rethinking jailbreak robustness through a compositional and data-centric lens. We introduce the Adversarial Déjà Vu hypothesis, which posits that ostensibly novel jailbreak attacks are rarely new in kind, but instead arise from recombinations of a finite set of recurring adversarial skills. …