Learning Model Successors | Paper Citation Record

Observation 6aa04dd3-1356-4934-97b8-6caaf6d5630c · outbound

This paper cites Generalization on the unseen, logic reasoning and degree curriculum.

Learning Model Successors Generalization on the unseen, logic reasoning and degree curriculum

Reference 1

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Observation 41075264-72e6-4f6b-88c6-d017d13d2f59 · outbound

This paper cites How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad.

Learning Model Successors How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad

Reference 2

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This paper cites and Mansouri, A.

Learning Model Successors and Mansouri, A

Reference 3

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This paper cites and Rodríguez, C.

Learning Model Successors and Rodríguez, C

Reference 4

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This paper cites Autoregressive transformers are zero-shot video imitators.

Learning Model Successors Autoregressive transformers are zero-shot video imitators

Reference 5

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This paper cites Invariant Risk Minimization.

Learning Model Successors Invariant Risk Minimization

Reference 6

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This paper cites Dynamic node creation in backpropagation networks.

Learning Model Successors Dynamic node creation in backpropagation networks

Reference 7

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This paper cites and Nagarajan, V.

Learning Model Successors and Nagarajan, V

Reference 8

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Observation 6b4ea1c4-9aa0-4d42-8f5d-1661b9fa117b · outbound

This paper cites P., Köster, R., Chadwick, M.

Learning Model Successors P., Köster, R., Chadwick, M

Reference 9

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This paper cites Pondernet: Learning to ponder.

Learning Model Successors Pondernet: Learning to ponder

Reference 10

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This paper cites L., Montanari, A., and Rakhlin, A.

Learning Model Successors L., Montanari, A., and Rakhlin, A

Reference 11

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This paper cites A model of inductive bias learning.

Learning Model Successors A model of inductive bias learning

Reference 12

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This paper cites and Schuller, R.

Learning Model Successors and Schuller, R

Reference 13

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Learning Model Successors Unresolved cited work

Reference 14

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This paper cites and Boult, T.

Learning Model Successors and Boult, T

Reference 15

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This paper cites Deep learning of representations for unsupervised and transfer learning.

Learning Model Successors Deep learning of representations for unsupervised and transfer learning

Reference 16

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This paper cites Curriculum learning.

Learning Model Successors Curriculum learning

Reference 17

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Observation 1561931f-2784-4b30-999d-34db768e0e58 · outbound

This paper cites On the practical ability of recurrent neural networks to recognize hierarchical languages.

Learning Model Successors On the practical ability of recurrent neural networks to recognize hierarchical languages

Reference 18

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Observation b6e6d241-e002-4673-af37-a39711694993 · outbound

This paper cites On the Ability and Limitations of Transformers to Recognize Formal Languages.

Learning Model Successors On the Ability and Limitations of Transformers to Recognize Formal Languages

Reference 19

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This paper cites Simplicity bias in transformers and their ability to learn sparse Boolean functions.

Learning Model Successors Simplicity bias in transformers and their ability to learn sparse Boolean functions

Reference 20

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This paper cites and Schulz, E.

Learning Model Successors and Schulz, E

Reference 21

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This paper cites E., Cruz, S., Dhamija, A.

Learning Model Successors E., Cruz, S., Dhamija, A

Reference 22

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Observation 731e7264-b6dd-4eeb-9f45-021db20ddef4 · outbound

This paper cites Making Neural Programming Architectures Generalize via Recursion.

Learning Model Successors Making Neural Programming Architectures Generalize via Recursion

Reference 23

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This paper cites Précis of the origin of concepts.

Learning Model Successors Précis of the origin of concepts

Reference 24

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Observation 35f20f8d-ac1f-49a0-9223-b9f0fdeae851 · outbound

This paper cites Multitask learning.

Learning Model Successors Multitask learning

Reference 25

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Observation 1765ed43-296b-4381-9305-6828726aaa28 · outbound

This paper cites Language Models Need Inductive Biases to Count Inductively.

Learning Model Successors Language Models Need Inductive Biases to Count Inductively

Reference 26

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Observation 739a501a-3bc1-4268-9043-08bc1c1aec3c · outbound

This paper cites A convex formulation for learning shared structures from multiple tasks.

Learning Model Successors A convex formulation for learning shared structures from multiple tasks

Reference 27

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Observation 6a9a48b6-57a0-49fd-ba10-7b75351bb3a7 · outbound

This paper cites Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts.

Learning Model Successors Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts

Reference 28

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This paper cites and Liu, B.

Learning Model Successors and Liu, B

Reference 29

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This paper cites Learning from multiple sources.

Learning Model Successors Learning from multiple sources

Reference 30

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Observation a6ddfcdf-fc35-40f4-8c83-aa18aafde7d1 · outbound

This paper cites Learning higher-order logic programs.

Learning Model Successors Learning higher-order logic programs

Reference 31

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Observation 7225f957-2c7b-4f9e-a455-56ca6abc7468 · outbound

This paper cites and Feys, R.

Learning Model Successors and Feys, R

Reference 32

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Observation dd7695cd-4d73-4216-930b-c7ea771bc89c · outbound

This paper cites Bayesian multitask learning with latent hierarchies.

Learning Model Successors Bayesian multitask learning with latent hierarchies

Reference 33

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Observation cb2bff39-dbd6-4f0e-9418-c315ea5b3622 · outbound

This paper cites B., Lu, T., Luu, T., and Pál, D.

Learning Model Successors B., Lu, T., Luu, T., and Pál, D

Reference 34

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Observation 2cdd0f68-d1d3-4e0e-80aa-fbb474b95451 · outbound

This paper cites Positional Attention: Expressivity and Learnability of Algorithmic Computation.

Learning Model Successors Positional Attention: Expressivity and Learnability of Algorithmic Computation

Reference 35

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Observation 5c1fdd9d-740e-4e47-a20a-1b5f5c3cb499 · outbound

This paper cites Random deep neural networks are biased towards simple functions.

Learning Model Successors Random deep neural networks are biased towards simple functions

Reference 36

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Observation 48562429-90ed-4751-b471-5b428a45e473 · outbound

This paper cites H., Cowan, N.

Learning Model Successors H., Cowan, N

Reference 37

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Observation d42f2003-10e6-4a22-adfb-03ee0f211182 · outbound

This paper cites Universal transformers.

Learning Model Successors Universal transformers

Reference 38

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This paper cites K., Catt, E., Cundy, C., Hutter, M., Legg, S., Veness, J., and Ortega, P.

Learning Model Successors K., Catt, E., Cundy, C., Hutter, M., Legg, S., Veness, J., and Ortega, P

Reference 39

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This paper cites Towards a theory of out-of-distribution learning.

Learning Model Successors Towards a theory of out-of-distribution learning

Reference 40

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This paper cites A closer look at distribution shifts and out-of-distribution generalization on graphs.

Learning Model Successors A closer look at distribution shifts and out-of-distribution generalization on graphs

Reference 41

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This paper cites First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains.

Learning Model Successors First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains

Reference 42

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This paper cites Location Attention for Extrapolation to Longer Sequences.

Learning Model Successors Location Attention for Extrapolation to Longer Sequences

Reference 43

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Learning Model Successors L., Jiang, L., Lin, B

Reference 44

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This paper cites How can self-attention networks recognize Dyck-n languages? In Findings of the Association for Computational Linguistics: EMNLP 2020 , November 2020.

Learning Model Successors How can self-attention networks recognize Dyck-n languages? In Findings of the Association for Computational Linguistics: EMNLP 2020 , November 2020

Reference 45

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Learning Model Successors and Zilioli, M

Reference 46

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Learning Model Successors Unresolved cited work

Reference 47

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This paper cites Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution.

Learning Model Successors Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution

Reference 48

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Learning Model Successors H., and Hutter, F

Reference 49

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This paper cites Neural fine-tuning search for few-shot learning.

Learning Model Successors Neural fine-tuning search for few-shot learning

Reference 50

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Learning Model Successors and Lebiere, C

Reference 51

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Learning Model Successors Looped transformers for length generalization

Reference 52

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Learning Model Successors and Heit, E

Reference 53

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Learning Model Successors Unresolved cited work

Reference 54

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Learning Model Successors D., Tenenbaum, J

Reference 55

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Learning Model Successors K., Mattern, C., Aitchison, M., and Veness, J

Reference 56

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Learning Model Successors Neural Turing Machines

Reference 57

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Learning Model Successors Adaptive Computation Time for Recurrent Neural Networks

Reference 58

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Learning Model Successors and Lopez-Paz, D

Reference 59

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Learning Model Successors Characterizing implicit bias in terms of optimization geometry

Reference 60

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Learning Model Successors Theoretical limitations of self-attention in neural sequence models

Reference 61

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This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

Learning Model Successors Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 62

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This paper cites Formal language recognition by hard attention transformers: Perspectives from circuit complexity.

Learning Model Successors Formal language recognition by hard attention transformers: Perspectives from circuit complexity

Reference 63

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This paper cites The problem of induction.

Learning Model Successors The problem of induction

Reference 64

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This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

Learning Model Successors The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 65

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This paper cites Universal Length Generalization with Turing Programs.

Learning Model Successors Universal Length Generalization with Turing Programs

Reference 66

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This paper cites Llm- adapters: An adapter family for parameter-efficient fine-tuning of large language models.

Learning Model Successors Llm- adapters: An adapter family for parameter-efficient fine-tuning of large language models

Reference 67

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Observation 5bf45d6d-a55d-48c3-9818-6f660380f5ad · outbound

This paper cites Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents.

Learning Model Successors Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Reference 68

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Observation 657e4fdb-9d05-44da-a2d8-3986b8d0ba48 · outbound

This paper cites A theory of universal artificial intelligence based on algorithmic complexity.

Learning Model Successors A theory of universal artificial intelligence based on algorithmic complexity

Reference 69

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Reference 70

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Reference 73

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Learning Model Successors The impact of positional encoding on length generalization in transformers

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Reference 78

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Reference 79

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Reference 80

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Reference 82

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Learning Model Successors Continual Learning as Computationally Constrained Reinforcement Learning

Reference 83

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Reference 85

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Learning Model Successors Can RNNs learn Recursive Nested Subject-Verb Agreements?

Reference 86

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Reference 87

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Reference 88

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Reference 89

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Reference 90

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Reference 91

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Reference 92

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Reference 93

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Learning Model Successors Darts: Differentiable architecture search

Reference 94

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Learning Model Successors A Survey on Evolutionary Neural Architecture Search

Reference 95

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Learning Model Successors Hierarchical representa- tions for efficient architecture search

Reference 96

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Reference 97

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Learning Model Successors Auto-Regressive Next-Token Predictors are Universal Learners

Reference 98

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Reference 99

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Reference 100

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