Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks | Paper Citation Record

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This paper cites A unified approach to interpreting model predictions.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks A unified approach to interpreting model predictions

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This paper cites ”why should I trust you?” explaining the predictions of any classifier.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks ”why should I trust you?” explaining the predictions of any classifier

Reference 2

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This paper cites Grad-cam: Visual ex- planations from deep networks via gradient-based localization.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Grad-cam: Visual ex- planations from deep networks via gradient-based localization

Reference 3

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This paper cites A comprehensive and reliable feature attribution method: Double-sided remove and reconstruct (dorar).

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks A comprehensive and reliable feature attribution method: Double-sided remove and reconstruct (dorar)

Reference 4

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This paper cites Towards better explanations of class activation mapping.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Towards better explanations of class activation mapping

Reference 5

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This paper cites On completeness-aware concept-based explana- tions in deep neural networks.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks On completeness-aware concept-based explana- tions in deep neural networks

Reference 6

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This paper cites Towards automatic concept-based explanations.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Towards automatic concept-based explanations

Reference 7

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This paper cites From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation

Reference 8

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This paper cites Towards robust interpretabil- ity with self-explaining neural networks.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Towards robust interpretabil- ity with self-explaining neural networks

Reference 9

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This paper cites Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero

Reference 10

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This paper cites Interpretability beyond feature attribution: Quan- titative testing with concept activation vectors (tcav).

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Interpretability beyond feature attribution: Quan- titative testing with concept activation vectors (tcav)

Reference 11

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This paper cites Network dissection: Quantifying interpretability of deep visual representations.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Network dissection: Quantifying interpretability of deep visual representations

Reference 12

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This paper cites A general reinforcement learning algo- rithm that masters chess, shogi, and go through self-play.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks A general reinforcement learning algo- rithm that masters chess, shogi, and go through self-play

Reference 13

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This paper cites Progress measures for grokking via mechanistic interpretability.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Progress measures for grokking via mechanistic interpretability

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This paper cites Explainable ar- tificial intelligence (XAI): What we know and what is left to attain trust- worthy artificial intelligence.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Explainable ar- tificial intelligence (XAI): What we know and what is left to attain trust- worthy artificial intelligence

Reference 15

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This paper cites Learning a SAT solver from single-bit su- pervision.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Learning a SAT solver from single-bit su- pervision

Reference 16

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This paper cites Concept learners for few- shot learning.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Concept learners for few- shot learning

Reference 17

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This paper cites Concept-cognitive learning sur- vey: Mining and fusing knowledge from data.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Concept-cognitive learning sur- vey: Mining and fusing knowledge from data

Reference 18

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This paper cites Efficient graph coloring with neural networks: A physics-inspired approach for large graphs.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Efficient graph coloring with neural networks: A physics-inspired approach for large graphs

Reference 19

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This paper cites Can hybrid geometric scattering networks help solve the maximum clique problem? In Sanmi Koyejo, S.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Can hybrid geometric scattering networks help solve the maximum clique problem? In Sanmi Koyejo, S

Reference 20

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This paper cites Rethinking Graph Neural Networks for the Graph Coloring Problem.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Rethinking Graph Neural Networks for the Graph Coloring Problem

Reference 21

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This paper cites A spectral technique for random satisfiable 3 cnf formulas.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks A spectral technique for random satisfiable 3 cnf formulas

Reference 22

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This paper cites Algorithms for random 3-sat.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Algorithms for random 3-sat

Reference 23

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This paper cites Algorithmic barriers from phase transitions.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Algorithmic barriers from phase transitions

Reference 24

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This paper cites Local search strategies for satisfiability testing.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Local search strategies for satisfiability testing

Reference 25

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This paper cites Instability of one-step replica-symmetry-broken phase in satisfiability problems.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Instability of one-step replica-symmetry-broken phase in satisfiability problems

Reference 26

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This paper cites Focused Local Search for Random 3-Satisfiability.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Focused Local Search for Random 3-Satisfiability

Reference 27

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This paper cites Axiomatic attribu- tion for deep networks.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Axiomatic attribu- tion for deep networks

Reference 28

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This paper cites DISSECT: Disentangled Simultaneous Explanations via Concept Traversals.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks DISSECT: Disentangled Simultaneous Explanations via Concept Traversals

Reference 29

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This paper cites Concept learning through deep reinforcement learning with memory-augmented neural networks.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Concept learning through deep reinforcement learning with memory-augmented neural networks

Reference 30

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Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks In-context Learning and Induction Heads

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This paper cites The Quantization Model of Neural Scaling.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks The Quantization Model of Neural Scaling

Reference 32

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This paper cites Interpretability in the wild: a circuit for indirect object identification in gpt-2 small.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Interpretability in the wild: a circuit for indirect object identification in gpt-2 small

Reference 33

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This paper cites Zoom in: An introduction to circuits.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Zoom in: An introduction to circuits

Reference 34

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Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Reinforced continual learning for graphs

Reference 35

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This paper cites Conlearn: Contextual-knowledge- aware concept prerequisite relation learning with graph neural network.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Conlearn: Contextual-knowledge- aware concept prerequisite relation learning with graph neural network

Reference 36

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Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Gonzalez, Lawrence B

Reference 37

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This paper cites Zcha ff2004: An ef- ficient sat solver.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Zcha ff2004: An ef- ficient sat solver

Reference 38

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Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Guiding high-performance sat solvers with unsat-core predictions

Reference 39

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Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Learning local search heuristics for boolean satisfiability

Reference 40

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This paper cites SATzilla: Portfolio-based Algorithm Selection for SAT.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks SATzilla: Portfolio-based Algorithm Selection for SAT

Reference 41

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This paper cites Combinatorial optimization and rea- soning with graph neural networks.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Combinatorial optimization and rea- soning with graph neural networks

Reference 42

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This paper cites RNNs, CNNs and Transformers in Human Action Recognition: A Survey and a Hybrid Model.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks RNNs, CNNs and Transformers in Human Action Recognition: A Survey and a Hybrid Model

Reference 43

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Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Exact combinatorial optimization with graph convolutional neural networks

Reference 44

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This paper cites Threshold val- ues of random k-sat from the cavity method.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Threshold val- ues of random k-sat from the cavity method

Reference 45

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This paper cites Gibbs states and the set of solutions of random constraint satisfaction problems.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Gibbs states and the set of solutions of random constraint satisfaction problems

Reference 46

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This paper cites Why al- most all satisfiable k-cnf formulas are easy.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Why al- most all satisfiable k-cnf formulas are easy

Reference 47

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This paper cites Satlib: An online resource for re- search on sat.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Satlib: An online resource for re- search on sat

Reference 48

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This paper cites Beyond interpretability: developing a language to shape our relationships with ai.

Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks Beyond interpretability: developing a language to shape our relationships with ai

Reference 49

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