AI for the Open-World: the Learning Principles | Paper Citation Record

Observation 8c55c465-1532-49cb-a2b2-9a04e344fa11 · outbound

This paper cites Systematic generalisation with group invariant predictions.

AI for the Open-World: the Learning Principles Systematic generalisation with group invariant predictions

Reference 1

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Observation 1383e2a0-775a-43d8-b367-e8425ad8fce3 · outbound

This paper cites Empirical or Invariant Risk Minimization? A Sample Complexity Perspective.

AI for the Open-World: the Learning Principles Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

Reference 2

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Observation d2af58b7-62bf-4c46-a175-b2cf1ae92b26 · outbound

This paper cites In-Context Language Learning: Architectures and Algorithms.

AI for the Open-World: the Learning Principles In-Context Language Learning: Architectures and Algorithms

Reference 3

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Observation 067a2e78-80a2-4059-bd3d-b882536bdec6 · outbound

This paper cites SGD with Large Step Sizes Learns Sparse Features.

AI for the Open-World: the Learning Principles SGD with Large Step Sizes Learns Sparse Features

Reference 4

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Observation e032eedf-fa87-4ca7-b9a5-e02d7c90bac0 · outbound

This paper cites Sgd with large step sizes learns sparse features.

AI for the Open-World: the Learning Principles Sgd with large step sizes learns sparse features

Reference 5

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Observation 7be1d68d-4016-4676-8ddd-93b5a544e236 · outbound

This paper cites Invariant Risk Minimization.

AI for the Open-World: the Learning Principles Invariant Risk Minimization

Reference 6

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Observation a2fb3ba9-99fa-4d39-bf30-792e77743133 · outbound

This paper cites Invariant risk minimization.

AI for the Open-World: the Learning Principles Invariant risk minimization

Reference 7

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Observation 397c8b26-94c3-4128-8e92-37063c5fa270 · outbound

This paper cites Ensemble of averages: Improving model selection and boosting performance in domain generalization.

AI for the Open-World: the Learning Principles Ensemble of averages: Improving model selection and boosting performance in domain generalization

Reference 8

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Observation 25b8ff43-8f56-4c36-9d7d-517d7b149e34 · outbound

This paper cites Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning.

AI for the Open-World: the Learning Principles Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Reference 9

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Observation 626759ca-900e-4d1d-bfff-91585fdfb6be · outbound

This paper cites Locally weighted learning.

AI for the Open-World: the Learning Principles Locally weighted learning

Reference 10

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Observation 123e7841-5239-4287-bd5c-5e26c271db79 · outbound

This paper cites Multiple kernel learning, conic duality, and the smo algorithm.

AI for the Open-World: the Learning Principles Multiple kernel learning, conic duality, and the smo algorithm

Reference 11

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Observation 4c13700f-3dce-4983-894a-9768cd8fee0c · outbound

This paper cites Meta-learned invariant risk minimization.

AI for the Open-World: the Learning Principles Meta-learned invariant risk minimization

Reference 12

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Observation 57d88539-c9d4-4106-a66e-3d8e813d9e4a · outbound

This paper cites Neural machine translation by jointly learning to align and translate.

AI for the Open-World: the Learning Principles Neural machine translation by jointly learning to align and translate

Reference 13

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Observation 7275b6c1-9c08-4cb9-af71-8b706897f50d · outbound

This paper cites From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge.

AI for the Open-World: the Learning Principles From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge

Reference 14

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Observation 45dc0572-0bda-4f31-93b0-9c3c2ca20e77 · outbound

This paper cites Predict then interpolate: A simple algorithm to learn stable classifiers, 2021.

AI for the Open-World: the Learning Principles Predict then interpolate: A simple algorithm to learn stable classifiers, 2021

Reference 15

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Observation 3c492b19-89c5-4947-8d39-f42c2e0927d8 · outbound

This paper cites VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning.

AI for the Open-World: the Learning Principles VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Reference 16

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Observation b67590c3-bf9c-4a39-aaf6-45001ad1a40a · outbound

This paper cites o ppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, G \.

AI for the Open-World: the Learning Principles o ppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, G \

Reference 17

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Observation e1ad00ba-5312-482b-b60a-9e9aabae1aa3 · outbound

This paper cites Recognition in terra incognita.

AI for the Open-World: the Learning Principles Recognition in terra incognita

Reference 18

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Observation abc388f9-d5a7-415b-aa55-e722cdd03d5c · outbound

This paper cites Longformer: The Long-Document Transformer.

AI for the Open-World: the Learning Principles Longformer: The Long-Document Transformer

Reference 19

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Observation d8885d0c-ce80-4462-a73f-e1813d49c7f5 · outbound

This paper cites Robust Optimization, volume 28 of Princeton Series in Applied Mathematics.

AI for the Open-World: the Learning Principles Robust Optimization, volume 28 of Princeton Series in Applied Mathematics

Reference 20

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Observation 7035859b-14bb-4da6-9cae-0d47083917f2 · outbound

This paper cites Deep learning of representations: Looking forward.

AI for the Open-World: the Learning Principles Deep learning of representations: Looking forward

Reference 21

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Observation b200b2b8-3080-4abc-8c12-52e6e52db6ce · outbound

This paper cites Deep learning of representations: Looking forward.

AI for the Open-World: the Learning Principles Deep learning of representations: Looking forward

Reference 22

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Observation a98954da-c9cb-40ef-8f18-134bb5117768 · outbound

This paper cites A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.

AI for the Open-World: the Learning Principles A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

Reference 24

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Observation 2791e5ba-a554-4668-b36a-04a8b63c2cdf · outbound

This paper cites Backgammon computer program beats world champion.

AI for the Open-World: the Learning Principles Backgammon computer program beats world champion

Reference 25

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Observation 1107660b-711a-41d7-9ed5-5c49d68a34bb · outbound

This paper cites Birth of a transformer: A memory viewpoint.

AI for the Open-World: the Learning Principles Birth of a transformer: A memory viewpoint

Reference 26

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Observation de84558d-0eb7-40fa-be0c-0ee4e9203522 · outbound

This paper cites Implicit regularization for deep neural networks driven by an ornstein-uhlenbeck like process.

AI for the Open-World: the Learning Principles Implicit regularization for deep neural networks driven by an ornstein-uhlenbeck like process

Reference 27

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Observation 8594e3ba-ed31-4cfb-b993-d3f94ca38ef8 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

AI for the Open-World: the Learning Principles On the Opportunities and Risks of Foundation Models

Reference 28

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Observation 1a9c7d02-6406-4071-859c-72d003a3e2a0 · outbound

This paper cites From Machine Learning to Machine Reasoning.

AI for the Open-World: the Learning Principles From Machine Learning to Machine Reasoning

Reference 29

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Observation 032dc853-2940-43bd-af1b-e025ee763c8c · outbound

This paper cites Local learning algorithms.

AI for the Open-World: the Learning Principles Local learning algorithms

Reference 30

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Observation e25a60af-e097-4d4e-82a3-67d43de351f3 · outbound

This paper cites Optimization methods for large-scale machine learning.

AI for the Open-World: the Learning Principles Optimization methods for large-scale machine learning

Reference 31

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Observation e38449c0-4cbd-4235-86df-efd031620685 · outbound

This paper cites AI for open world, https://leon.bottou.org/feuilleton/turing.

AI for the Open-World: the Learning Principles AI for open world, https://leon.bottou.org/feuilleton/turing

Reference 32

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Observation 7ab444fd-6488-4048-be1f-21981b5a16e2 · outbound

This paper cites Approximate matching: Definition and terminology.

AI for the Open-World: the Learning Principles Approximate matching: Definition and terminology

Reference 33

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Observation a539606a-4572-4c38-8a32-b9a79e0219f7 · outbound

This paper cites Language models are few-shot learners.

AI for the Open-World: the Learning Principles Language models are few-shot learners

Reference 34

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Observation 38d07d4c-f371-4826-a3b5-960bc2ded1d1 · outbound

This paper cites Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets.

AI for the Open-World: the Learning Principles Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets

Reference 35

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Observation 075072f8-8464-4b5e-87ca-d72fd9d5cbb2 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

AI for the Open-World: the Learning Principles Unsupervised learning of visual features by contrasting cluster assignments

Reference 36

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This paper cites Efficient Intent Detection with Dual Sentence Encoders.

AI for the Open-World: the Learning Principles Efficient Intent Detection with Dual Sentence Encoders

Reference 37

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Observation 7a44622a-500d-4c39-85a8-57d4835a5eca · outbound

This paper cites Transformer flops.

AI for the Open-World: the Learning Principles Transformer flops

Reference 38

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Observation befff649-ef90-465c-a1ba-2b931832a97b · outbound

This paper cites Swad: Domain generalization by seeking flat minima.

AI for the Open-World: the Learning Principles Swad: Domain generalization by seeking flat minima

Reference 39

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This paper cites Extending Context Window of Large Language Models via Positional Interpolation.

AI for the Open-World: the Learning Principles Extending Context Window of Large Language Models via Positional Interpolation

Reference 40

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This paper cites A simple framework for contrastive learning of visual representations.

AI for the Open-World: the Learning Principles A simple framework for contrastive learning of visual representations

Reference 41

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Observation c1ba12e0-3961-4a2c-8a60-1e7a85078c1c · outbound

This paper cites A Closer Look at Few-shot Classification.

AI for the Open-World: the Learning Principles A Closer Look at Few-shot Classification

Reference 42

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This paper cites Exploring Simple Siamese Representation Learning.

AI for the Open-World: the Learning Principles Exploring Simple Siamese Representation Learning

Reference 43

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This paper cites Understanding and Improving Feature Learning for Out-of-Distribution Generalization.

AI for the Open-World: the Learning Principles Understanding and Improving Feature Learning for Out-of-Distribution Generalization

Reference 44

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Observation e8e3e687-fda5-43c4-95a2-23da63f35733 · outbound

This paper cites MagicPIG: LSH Sampling for Efficient LLM Generation.

AI for the Open-World: the Learning Principles MagicPIG: LSH Sampling for Efficient LLM Generation

Reference 45

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This paper cites Natural language processing (almost) from scratch.

AI for the Open-World: the Learning Principles Natural language processing (almost) from scratch

Reference 46

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This paper cites Independent Component Analysis, a new concept? Signal Processing , 36: 0 287--314, Apr 1994.

AI for the Open-World: the Learning Principles Independent Component Analysis, a new concept? Signal Processing , 36: 0 287--314, Apr 1994

Reference 47

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AI for the Open-World: the Learning Principles Environment inference for invariant learning

Reference 48

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This paper cites GoEmotions: A Dataset of Fine-Grained Emotions.

AI for the Open-World: the Learning Principles GoEmotions: A Dataset of Fine-Grained Emotions

Reference 49

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AI for the Open-World: the Learning Principles Conditional meta-learning of linear representations

Reference 50

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AI for the Open-World: the Learning Principles Unresolved cited work

Reference 51

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AI for the Open-World: the Learning Principles Ensemble methods in machine learning

Reference 52

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

AI for the Open-World: the Learning Principles An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 53

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AI for the Open-World: the Learning Principles TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

Reference 54

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This paper cites ELIZA, https://en.wikipedia.org/wiki/ELIZA1972.

AI for the Open-World: the Learning Principles ELIZA, https://en.wikipedia.org/wiki/ELIZA1972

Reference 55

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AI for the Open-World: the Learning Principles Head2toe: Utilizing intermediate representations for better transfer learning

Reference 56

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This paper cites Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias.

AI for the Open-World: the Learning Principles Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias

Reference 57

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AI for the Open-World: the Learning Principles Model-agnostic meta-learning for fast adaptation of deep networks

Reference 58

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AI for the Open-World: the Learning Principles Ensemble deep learning: A review

Reference 59

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This paper cites Ustinova, Hana Ajakan, Pascal Germain, H.

AI for the Open-World: the Learning Principles Ustinova, Hana Ajakan, Pascal Germain, H

Reference 60

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This paper cites Garc\'ia-Portugu\'es.

AI for the Open-World: the Learning Principles Garc\'ia-Portugu\'es

Reference 61

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This paper cites Better & Faster Large Language Models via Multi-token Prediction.

AI for the Open-World: the Learning Principles Better & Faster Large Language Models via Multi-token Prediction

Reference 62

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This paper cites Understanding the difficulty of training deep feedforward neural networks.

AI for the Open-World: the Learning Principles Understanding the difficulty of training deep feedforward neural networks

Reference 63

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This paper cites No one representation to rule them all: Overlapping features of training methods.

AI for the Open-World: the Learning Principles No one representation to rule them all: Overlapping features of training methods

Reference 64

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This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

AI for the Open-World: the Learning Principles Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 65

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AI for the Open-World: the Learning Principles Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision

Reference 66

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AI for the Open-World: the Learning Principles The fast Gauss transform

Reference 67

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Reference 69

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AI for the Open-World: the Learning Principles In Search of Lost Domain Generalization

Reference 70

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AI for the Open-World: the Learning Principles In search of lost domain generalization

Reference 71

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Reference 72

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AI for the Open-World: the Learning Principles Changing Answer Order Can Decrease MMLU Accuracy

Reference 73

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AI for the Open-World: the Learning Principles Structural risk minimization for character recognition

Reference 74

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AI for the Open-World: the Learning Principles Mathematical Structures of Language

Reference 75

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AI for the Open-World: the Learning Principles The elements of statistical learning, 2009

Reference 76

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AI for the Open-World: the Learning Principles Deep residual learning for image recognition

Reference 77

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AI for the Open-World: the Learning Principles Deep residual learning for image recognition

Reference 78

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AI for the Open-World: the Learning Principles Multicalibration: Calibration for the ( C omputationally-identifiable) masses

Reference 79

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AI for the Open-World: the Learning Principles Towards a Definition of Disentangled Representations

Reference 80

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AI for the Open-World: the Learning Principles Distilling the Knowledge in a Neural Network

Reference 81

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AI for the Open-World: the Learning Principles RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 82

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AI for the Open-World: the Learning Principles Densely connected convolutional networks

Reference 83

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AI for the Open-World: the Learning Principles Xing, and Dong Huang

Reference 84

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Reference 85

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AI for the Open-World: the Learning Principles Unresolved cited work

Reference 86

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AI for the Open-World: the Learning Principles Adaptive mixtures of local experts

Reference 87

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AI for the Open-World: the Learning Principles Neural tangent kernel: Convergence and generalization in neural networks

Reference 88

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AI for the Open-World: the Learning Principles Unresolved cited work

Reference 89

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AI for the Open-World: the Learning Principles Understanding Dimensional Collapse in Contrastive Self-supervised Learning

Reference 90

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AI for the Open-World: the Learning Principles Inferring algorithmic patterns with stack-augmented recurrent nets

Reference 91

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AI for the Open-World: the Learning Principles Sutherland, and Nathan Srebro

Reference 92

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AI for the Open-World: the Learning Principles How to use dropout correctly on residual networks with batch normalization

Reference 93

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AI for the Open-World: the Learning Principles Last layer re-training is sufficient for robustness to spurious correlations

Reference 94

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AI for the Open-World: the Learning Principles Overcoming catastrophic forgetting in neural networks

Reference 95

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AI for the Open-World: the Learning Principles Earnshaw, Imran S

Reference 96

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AI for the Open-World: the Learning Principles Wilds: A benchmark of in-the-wild distribution shifts

Reference 97

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AI for the Open-World: the Learning Principles Out-of-distribution generalization with maximal invariant predictor

Reference 98

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AI for the Open-World: the Learning Principles Learning multiple layers of features from tiny images

Reference 99

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AI for the Open-World: the Learning Principles A new frontier for hopfield networks

Reference 100

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AI for the Open-World: the Learning Principles Out-of-distribution generalization via risk extrapolation (rex)

Reference 101

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AI for the Open-World: the Learning Principles Out-of-distribution generalization via risk extrapolation (rex)

Reference 102

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