Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning | Paper Citation Record

Observation d7c89886-c043-452d-9b29-556bc19bad96 · outbound

This paper cites A survey on the convergence of edge computing and ai for uavs: Opportunities and challenges,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A survey on the convergence of edge computing and ai for uavs: Opportunities and challenges,

Reference 1

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

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning V ocell: A 65-nm speech-triggered wake-up soc for 10- µ w keyword spotting and speaker verification,

Reference 2

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

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A 23-uw keyword spotting ic with ring-oscillator-based time-domain feature extraction,

Reference 3

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Observation 2665fb5d-920e-4501-b15c-4e78d5f99665 · outbound

This paper cites Tan, W.-H.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Tan, W.-H

Reference 4

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This paper cites A 5.6µw 10-keyword end-to-end keyword spotting system using passive-averaging sar adc and sign-exponent-only layer fusion with 92.7% accuracy,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A 5.6µw 10-keyword end-to-end keyword spotting system using passive-averaging sar adc and sign-exponent-only layer fusion with 92.7% accuracy,

Reference 5

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This paper cites An ultra-low power reconfigurable biomedical ai processor with adaptive learning for versatile wearable intelligent health monitoring,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning An ultra-low power reconfigurable biomedical ai processor with adaptive learning for versatile wearable intelligent health monitoring,

Reference 6

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This paper cites A 3.9 mw 25- electrode reconfigured sensor for wearable cardiac monitoring system,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A 3.9 mw 25- electrode reconfigured sensor for wearable cardiac monitoring system,

Reference 7

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This paper cites A 1.06- µ w smart ecg processor in 65-nm cmos for real-time biometric authentication and personal cardiac monitoring,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A 1.06- µ w smart ecg processor in 65-nm cmos for real-time biometric authentication and personal cardiac monitoring,

Reference 8

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This paper cites A 2.89 µ w dry-electrode enabled clockless wireless ecg soc for wearable applications,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A 2.89 µ w dry-electrode enabled clockless wireless ecg soc for wearable applications,

Reference 9

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Observation 68f0f806-48e7-4368-af94-c34f9117eb1d · outbound

This paper cites An Ultra- Low Power Reconfigurable Biomedical AI Processor With Adaptive Learning for Versatile Wearable Intelligent Health Monitoring,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning An Ultra- Low Power Reconfigurable Biomedical AI Processor With Adaptive Learning for Versatile Wearable Intelligent Health Monitoring,

Reference 10

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This paper cites Reckon: A 28nm sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Reckon: A 28nm sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales,

Reference 11

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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,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Tinyvers: A tiny versatile system-on-chip with state-retentive emram for ml inference at the extreme edge,

Reference 12

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Observation 2f32478a-c71e-4414-92a4-f27eac3c7a31 · outbound

This paper cites Embedded deep neural network processing: Algorithmic and processor techniques bring deep learning to iot and edge devices,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Embedded deep neural network processing: Algorithmic and processor techniques bring deep learning to iot and edge devices,

Reference 13

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This paper cites Efficient processing of deep neural networks: A tutorial and survey,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Efficient processing of deep neural networks: A tutorial and survey,

Reference 14

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This paper cites A 640m pixel/s 3.65 mw sparse event-driven neuromorphic object recognition processor with on-chip learning,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A 640m pixel/s 3.65 mw sparse event-driven neuromorphic object recognition processor with on-chip learning,

Reference 15

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This paper cites A 42pj/decision 3.12 tops/w robust in-memory machine learning classifier with on-chip training,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A 42pj/decision 3.12 tops/w robust in-memory machine learning classifier with on-chip training,

Reference 16

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Observation 5bc34053-9955-43fd-9c84-a14b5969342c · outbound

This paper cites A 55nm time-domain mixed-signal neuromorphic accelerator with stochastic synapses and embedded reinforcement learning for autonomous micro-robots,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A 55nm time-domain mixed-signal neuromorphic accelerator with stochastic synapses and embedded reinforcement learning for autonomous micro-robots,

Reference 17

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Observation 98bd8fe6-c2a5-4786-b351-b6af40f19c1e · outbound

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

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Fsl-hdnn: A 5.7 tops/w end-to- end few-shot learning classifier accelerator with feature extraction and hyperdimensional computing,

Reference 18

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Observation c62d43f1-837f-45fe-bb8b-e0a5f281865c · outbound

This paper cites Clo-hdnn: A 4.66 tflops/w and 3.78 tops/w continual on-device learning accelerator with energy-efficient hyperdimensional computing via progressive search,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Clo-hdnn: A 4.66 tflops/w and 3.78 tops/w continual on-device learning accelerator with energy-efficient hyperdimensional computing via progressive search,

Reference 19

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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,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning One-shot learning with memory- augmented neural networks using a 64-kbit, 118 gops/w rram-based non-volatile associative memory,

Reference 20

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning An in-memory computing sram macro for memory-augmented neural network,

Reference 21

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Observation 0a9ea6a2-2606-4c3f-b153-6c20387c3ab6 · outbound

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A 4096-neuron 1m-synapse 3.8-pj/sop spiking neural network with on-chip stdp learning and sparse weights in 10-nm finfet cmos,

Reference 22

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Tess: A scalable temporally and spatially local learning rule for spiking neural networks,

Reference 23

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Online spatio-temporal learning in deep neural networks,

Reference 24

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Chameleon: A multiplier-free temporal convolutional network accelerator for end-to-end few-shot and continual learning from sequential data,

Reference 25

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Observation b052a892-4762-4c1f-808e-c6da4c183ae5 · outbound

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences,

Reference 26

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Human-level concept learning through probabilistic program induction,

Reference 27

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Observation b5e4ac00-9ab1-4833-ac38-2f024c7265a8 · outbound

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Prototypical networks for few-shot learning,

Reference 28

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Observation 41a7dfba-2b89-404b-9115-c1c6eacaa1f0 · outbound

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Learning to compare: Relation network for few-shot learning,

Reference 29

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Observation 246026c2-7841-4f21-9e01-e2c5b085c8d3 · outbound

This paper cites Few-Shot Keyword Spotting in Any Language.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Few-Shot Keyword Spotting in Any Language

Reference 30

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Observation 6abf80e5-2d50-449a-880c-ac551045c7d0 · outbound

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Matching networks for one shot learning,

Reference 31

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Domain-adaptive discriminative one-shot learning of gestures,

Reference 32

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 33

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning One shot learning of simple visual concepts,

Reference 34

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Observation 38e0dc74-0918-41fc-bf2f-edbcb1ce65e7 · outbound

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Siamese neural networks for one-shot image recognition,

Reference 35

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This paper cites One-shot learning of object categories,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning One-shot learning of object categories,

Reference 36

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Observation f6a3ac3b-5649-4fa7-89ea-dae6a32e47c9 · outbound

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

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Few- shot class-incremental learning,

Reference 37

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Observation 73f9ef02-46c8-4582-aafc-b962d5325011 · outbound

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning The neurobench framework for benchmarking neuromorphic computing algorithms and systems,

Reference 38

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Observation be94ca2e-d410-4739-8ef0-e1dea6c3a4f8 · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Overcoming catastrophic forgetting in neural networks,

Reference 39

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Observation 4b7468f2-d62a-4d5a-b116-5cc817c7b728 · outbound

This paper cites Attribute-based classification for zero-shot visual object categorization,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Attribute-based classification for zero-shot visual object categorization,

Reference 40

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Observation b258a1ad-f87c-4eb4-a5a5-46e846293cbc · outbound

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Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Trained transformers learn linear models in-context,

Reference 41

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Observation 3c0482b2-bd36-4464-9644-96280eaa4456 · outbound

This paper cites What can transformers learn in-context? a case study of simple function classes,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning What can transformers learn in-context? a case study of simple function classes,

Reference 42

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Observation a40cf486-73a7-4784-b14d-aeb146924518 · outbound

This paper cites Language models are few-shot learners,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Language models are few-shot learners,

Reference 43

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Observation 4e11ce52-274b-4e9f-8c36-54df5bec101e · outbound

This paper cites Attention is all you need,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Attention is all you need,

Reference 44

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Observation 3b43ba4e-1276-4c2a-9087-9b88a4948a54 · outbound

This paper cites MLPs Learn In-Context on Regression and Classification Tasks.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning MLPs Learn In-Context on Regression and Classification Tasks

Reference 45

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Observation c5f3c437-970c-41ae-a235-16460a289f6a · outbound

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

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 46

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Observation 8f92447e-0273-4ca3-91e1-7e932248b633 · outbound

This paper cites Hyperdimensional computing: An introduction to comput- ing in distributed representation with high-dimensional random vectors,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Hyperdimensional computing: An introduction to comput- ing in distributed representation with high-dimensional random vectors,

Reference 47

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Observation 7316977f-f8ef-4660-bb3d-6922c9fe6b9b · outbound

This paper cites Very deep convolutional neural networks for raw waveforms,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Very deep convolutional neural networks for raw waveforms,

Reference 48

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Observation f248c76e-2494-4794-a3f7-03a9e0cb401f · outbound

This paper cites Speech Model Pre-training for End-to-End Spoken Language Understanding.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Speech Model Pre-training for End-to-End Spoken Language Understanding

Reference 49

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Observation 546ca876-5cf6-4e60-9290-c4a99b4a3d81 · outbound

This paper cites A high accuracy and ultra-energy-efficient zero- shot-retraining seizure detection processor,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning A high accuracy and ultra-energy-efficient zero- shot-retraining seizure detection processor,

Reference 50

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Observation ef486c4e-ca27-45d3-a0dc-7a42fe14ada8 · outbound

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

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning In-Context Language Learning: Architectures and Algorithms

Reference 51

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Observation 74e6cc55-efb9-43df-84b0-cec8de714b0c · outbound

This paper cites Efficient Lifelong Learning with A-GEM.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Efficient Lifelong Learning with A-GEM

Reference 52

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Observation df28430a-33f9-4fce-bd6c-1a380c77a482 · outbound

This paper cites Normalization matters in zero-shot learning,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Normalization matters in zero-shot learning,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T08:50:38.315179Z

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Observation 212bbff8-62f3-4088-af40-24d12ebf78e3 · outbound

This paper cites Tf- gczsl: Task-free generalized continual zero-shot learning,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Tf- gczsl: Task-free generalized continual zero-shot learning,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T08:50:38.425114Z

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Observation 3e908950-7b97-4577-8b60-9c2ecd375d41 · outbound

This paper cites Lifelong zero-shot learning.,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Lifelong zero-shot learning.,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-03T08:50:38.589316Z

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Observation c2c02d54-0503-43a7-819c-851022915b64 · outbound

This paper cites TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T08:50:38.755927Z

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Observation ad432ecf-b8b6-4226-8246-ff23f9128f4b · outbound

This paper cites Decision transformer: Reinforcement learning via sequence modeling,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Decision transformer: Reinforcement learning via sequence modeling,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T08:50:38.923956Z

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Observation 563ef21f-18d5-49d8-b666-74ecd93d82e1 · outbound

This paper cites Offline reinforcement learning as one big sequence modeling problem,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Offline reinforcement learning as one big sequence modeling problem,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T08:50:39.045787Z

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Observation d5c44b6d-9163-4979-aa85-e43b93e1ef3b · outbound

This paper cites Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks,.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T08:50:39.202344Z

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Observation d24b62a8-aa0e-4035-870c-de597c0a4ead · outbound

This paper cites The Forward-Forward Algorithm: Some Preliminary Investigations.

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning The Forward-Forward Algorithm: Some Preliminary Investigations

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T08:50:39.370097Z

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