Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data | Paper Citation Record

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This paper cites Learn to learn on chip: Hardware-aware meta-learning for quantized few-shot learning at the edge,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Learn to learn on chip: Hardware-aware meta-learning for quantized few-shot learning at the edge,

Reference 1

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This paper cites A tinyml platform for on-device continual learning with quantized latent replays,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data A tinyml platform for on-device continual learning with quantized latent replays,

Reference 2

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This paper cites Concrete Problems in AI Safety.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Concrete Problems in AI Safety

Reference 3

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This paper cites Latent replay for real-time continual learning,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Latent replay for real-time continual learning,

Reference 4

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This paper cites A quantization framework for neural network adaption at the edge,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data A quantization framework for neural network adaption at the edge,

Reference 5

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This paper cites Exploring quantization in few-shot learning,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Exploring quantization in few-shot learning,

Reference 6

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This paper cites An in-memory computing sram macro for memory-augmented neural network,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data An in-memory computing sram macro for memory-augmented neural network,

Reference 7

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This paper cites One-shot learning with memory- augmented neural networks using a 64-kbit, 118 gops/w rram-based non-volatile associative memory,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data One-shot learning with memory- augmented neural networks using a 64-kbit, 118 gops/w rram-based non-volatile associative memory,

Reference 8

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This paper cites Fsl-hdnn: A 5.7 tops/w end-to- end few-shot learning classifier accelerator with feature extraction and hyperdimensional computing,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Fsl-hdnn: A 5.7 tops/w end-to- end few-shot learning classifier accelerator with feature extraction and hyperdimensional computing,

Reference 9

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Observation a18edd7d-2763-44b8-b348-88110fc881ea · outbound

This paper cites V ocell: A 65-nm speech-triggered wake-up soc for 10- µ w keyword spotting and speaker verification,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data V ocell: A 65-nm speech-triggered wake-up soc for 10- µ w keyword spotting and speaker verification,

Reference 10

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This paper cites Efficient execution of temporal convolutional networks for embedded keyword spotting,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Efficient execution of temporal convolutional networks for embedded keyword spotting,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:43.401047Z

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This paper cites Tinyvers: A tiny versatile system-on-chip with state-retentive emram for ml inference at the extreme edge,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Tinyvers: A tiny versatile system-on-chip with state-retentive emram for ml inference at the extreme edge,

Reference 12

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This paper cites Ultratrail: A configurable ultralow-power tc-resnet ai accelerator for efficient keyword spotting,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Ultratrail: A configurable ultralow-power tc-resnet ai accelerator for efficient keyword spotting,

Reference 13

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This paper cites A 23-uw keyword spotting ic with ring-oscillator-based time-domain feature extraction,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data A 23-uw keyword spotting ic with ring-oscillator-based time-domain feature extraction,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:42.896022Z

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This paper cites In-memory realization of in-situ few-shot continual learning with a dynamically evolving explicit memory,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data In-memory realization of in-situ few-shot continual learning with a dynamically evolving explicit memory,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:42.775708Z

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Observation 5f2f6c15-fef4-46a0-9c87-647ad1e71223 · outbound

This paper cites Prototypical networks for few- shot learning,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Prototypical networks for few- shot learning,

Reference 16

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This paper cites Human-level concept learning through probabilistic program induction,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Human-level concept learning through probabilistic program induction,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:42.331836Z

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This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 18

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This paper cites Tcn-cutie: A 1,036-top/s/w, 2.72-µj/inference, 12.2-mw all-digital ternary accelerator in 22-nm fdx technology,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Tcn-cutie: A 1,036-top/s/w, 2.72-µj/inference, 12.2-mw all-digital ternary accelerator in 22-nm fdx technology,

Reference 19

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This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 20

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This paper cites Meta- learning in neural networks: A survey,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Meta- learning in neural networks: A survey,

Reference 21

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This paper cites Adam: A method for stochastic optimization.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Adam: A method for stochastic optimization

Reference 22

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This paper cites A Simple Neural Attentive Meta-Learner.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data A Simple Neural Attentive Meta-Learner

Reference 23

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This paper cites Meta-learning with memory-augmented neural networks,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Meta-learning with memory-augmented neural networks,

Reference 24

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This paper cites Locality-based encoder and model quantization for efficient hyper- dimensional computing,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Locality-based encoder and model quantization for efficient hyper- dimensional computing,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:40.535720Z

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This paper cites Anp-g: A 28-nm 1.04-pj/sop sub-mm2 asynchronous hybrid neural network olfactory processor enabling few-shot class-incremental on- chip learning,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Anp-g: A 28-nm 1.04-pj/sop sub-mm2 asynchronous hybrid neural network olfactory processor enabling few-shot class-incremental on- chip learning,

Reference 26

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This paper cites Attention is all you need,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Attention is all you need,

Reference 27

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This paper cites Long short-term memory,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Long short-term memory,

Reference 28

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This paper cites R-Transformer: Recurrent Neural Network Enhanced Transformer.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data R-Transformer: Recurrent Neural Network Enhanced Transformer

Reference 29

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This paper cites Deep Residual Learning for Image Recognition.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Deep Residual Learning for Image Recognition

Reference 30

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Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Convolutional Neural Networks using Logarithmic Data Representation

Reference 31

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This paper cites Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks

Reference 32

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This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 33

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This paper cites Accurate, large minibatch sgd: Training imagenet in 1 hour.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Accurate, large minibatch sgd: Training imagenet in 1 hour

Reference 34

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This paper cites Delving deep into rectifiers: Sur- passing human-level performance on imagenet classification,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Delving deep into rectifiers: Sur- passing human-level performance on imagenet classification,

Reference 35

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This paper cites Pappalardo.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Pappalardo

Reference 36

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This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 37

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This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 38

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This paper cites A White Paper on Neural Network Quantization.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data A White Paper on Neural Network Quantization

Reference 39

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This paper cites Matching networks for one shot learning,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Matching networks for one shot learning,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:39.542623Z

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This paper cites Chimera: A 0.92-tops, 2.2-tops/w edge ai accelerator with 2-mbyte on-chip foundry resistive ram for efficient training and inference,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Chimera: A 0.92-tops, 2.2-tops/w edge ai accelerator with 2-mbyte on-chip foundry resistive ram for efficient training and inference,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:39.417841Z

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This paper cites 9.3 a 40nm 4.81tflops/w 8b floating- point training processor for non-sparse neural networks using shared exponent bias and 24-way fused multiply-add tree,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data 9.3 a 40nm 4.81tflops/w 8b floating- point training processor for non-sparse neural networks using shared exponent bias and 24-way fused multiply-add tree,

Reference 42

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This paper cites Boosting keyword spotting through on-device learnable user speech characteristics.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Boosting keyword spotting through on-device learnable user speech characteristics

Reference 43

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This paper cites Few- shot class-incremental learning,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Few- shot class-incremental learning,

Reference 44

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This paper cites 12 mj per class on-device online few-shot class-incremental learning,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data 12 mj per class on-device online few-shot class-incremental learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:39.079056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f885428e-03fa-4912-ad77-59fbe17cfd88 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T12:19:37.067992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58a57763-fa72-4be4-b299-6f936abbeef3 · outbound

This paper cites Streaming keyword spotting on mobile devices.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Streaming keyword spotting on mobile devices

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T12:19:37.163917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e867b4f9-de64-4fa3-a12f-34bc17fb7020 · outbound

This paper cites Hello Edge: Keyword Spotting on Microcontrollers.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Hello Edge: Keyword Spotting on Microcontrollers

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T12:19:37.275917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adcebaeb-b794-45d8-9630-badafec98c4a · outbound

This paper cites Comparison of parametric represen- tations for monosyllabic word recognition in continuously spoken sentences,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Comparison of parametric represen- tations for monosyllabic word recognition in continuously spoken sentences,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:38.950318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b6722017-655d-4f81-a641-28fb730ae387 · outbound

This paper cites A 510-nw wake-up keyword-spotting chip using serial-fft-based mfcc and binarized depthwise separable cnn in 28-nm cmos,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data A 510-nw wake-up keyword-spotting chip using serial-fft-based mfcc and binarized depthwise separable cnn in 28-nm cmos,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:38.838997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3d4d99f8-e747-4826-b875-248a3894ed17 · outbound

This paper cites A 22nm, 10.8 uw/15.1 uw dual computing modes high power-performance-area efficiency domained background noise aware keyword- spotting processor,.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data A 22nm, 10.8 uw/15.1 uw dual computing modes high power-performance-area efficiency domained background noise aware keyword- spotting processor,

Reference 51

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T12:19:38.717614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 358736b8-8fc2-4249-b5e0-a35358e30667 · outbound

This paper cites Tan, W.-H.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Tan, W.-H

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:38.568070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f4051205-4388-4362-8713-44502289638b · outbound

This paper cites an unresolved cited work.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:19:38.156730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 437b14bb-8a1f-435e-9689-4d5ee658f9eb · outbound

This paper cites She presented several invited talks, including keynotes at the tinyML EMEA technical forum 2021 and at the Neuro-Inspired Computational Elements (NICE) neuromorphic conference.

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data She presented several invited talks, including keynotes at the tinyML EMEA technical forum 2021 and at the Neuro-Inspired Computational Elements (NICE) neuromorphic conference

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:19:38.363141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Reference resolution

Outbound references

Pith citing papers