Signatures and Induction Principles for Higher Inductive-Inductive Types [PDF]
Logical Methods in Computer Science, 2020 Higher inductive-inductive types (HIITs) generalize inductive types of dependent type theories in two ways. On the one hand they allow the simultaneous definition of multiple sorts that can be indexed over each other.
Ambrus Kaposi, András Kovács
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Constructing quotient inductive-inductive types [PDF]
Proceedings of the ACM on Programming Languages, 2019 Quotient inductive-inductive types (QIITs) generalise inductive types in two ways: a QIIT can have more than one sort and the later sorts can be indexed over the previous ones. In addition, equality constructors are also allowed. We work in a setting with uniqueness of identity proofs, hence we use the term QIIT instead of higher inductive-inductive ...
Ambrus Kaposi +2 more
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Graph Inductive Biases in Transformers without Message Passing [PDF]
International Conference on Machine Learning, 2023 Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional ...
Liheng Ma +7 more
semanticscholar +1 more source
ConViT: improving vision transformers with soft convolutional inductive biases [PDF]
International Conference on Machine Learning, 2021 Convolutional architectures have proven to be extremely successful for vision tasks. Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially lower performance ceiling.
Stéphane d'Ascoli +5 more
semanticscholar +1 more source
ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond [PDF]
International Journal of Computer Vision, 2022 Vision transformers have shown great potential in various computer vision tasks owing to their strong capability to model long-range dependency using the self-attention mechanism.
Qiming Zhang +3 more
semanticscholar +1 more source
Hypothesis Search: Inductive Reasoning with Language Models [PDF]
International Conference on Learning Representations, 2023 Inductive reasoning is a core problem-solving capacity: humans can identify underlying principles from a few examples, which robustly generalize to novel scenarios.
Ruocheng Wang +5 more
semanticscholar +1 more source
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement [PDF]
International Conference on Learning Representations, 2023 The ability to derive underlying principles from a handful of observations and then generalize to novel situations -- known as inductive reasoning -- is central to human intelligence.
Linlu Qiu +10 more
semanticscholar +1 more source
InGram: Inductive Knowledge Graph Embedding via Relation Graphs [PDF]
International Conference on Machine Learning, 2023 Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training.
Jaejun Lee +2 more
semanticscholar +1 more source
Hybrid Magnetic–Inductive Angular Sensor with 360° Range and Stray-Field Immunity
Sensors, 2022 Magnetic and inductive sensors are the dominant technologies in angular position sensing for automotive applications. This paper introduces a new angular sensor: a hybrid concept combining the magnetic Hall and inductive principles.
Bruno Brajon, Lorenzo Lugani, Gael Close
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Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases
Journal of Engineering Education, 2006 Richard M Felder
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