Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:02:34.906180Z
Paper Citation Record · LEDGER
As of 11 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2501.06663.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:02:34.906180Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
60 of 60 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation a528b484-22b8-41de-99d1-1cf3a3a99f5f · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization When machine learning meets privacy: A survey and outlook,
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization A neural network approach applied to multi-agent optimal control,
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Learning certified control using contraction metric,
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Learning in the wild: When, how, and what to learn for on-device dataset adaptation,
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization ETA: an efficient training accelerator for DNNs based on hardware-algorithm co-optimization,
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Source-reported events for the cited work
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Training deep neural networks in low-precision with high accuracy using FPGAs,
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Observation 1c86f4fb-efb4-441e-ba67-0f320166a3c4 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization FAST: DNN training under variable precision block floating point with stochastic rounding,
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Source-reported events for the cited work
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Observation f26ca76e-8598-4be8-ae52-2ef688a9c662 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Attention is all you need,
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Source-reported events for the cited work
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Observation 1aad8ddd-8844-4a66-9b52-e00249f87d52 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization BERT: Pre-training of deep bidirectional transformers for language understanding,
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Observation 29feb44a-97a5-4208-83e0-b98acbb960f2 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Source-reported events for the cited work
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Observation 433a0d07-9a39-4714-a2fb-66c1eaa8693d · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Segment anything,
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Observation 9eaed75d-ecba-40f5-834b-8c01c918fbe5 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization PINNsFormer: A transformer- based framework for physics-informed neural networks,
Reference 16
Source-reported events for the cited work
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Observation ae96de93-2f5c-4ad9-a2de-f2b488434dc5 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Federated learning in asr: Not as easy as you think,
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Source-reported events for the cited work
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Observation b7f34a39-681f-4b87-acea-0fce80f52970 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Federated Domain Adaptation for ASR with Full Self-Supervision
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Source-reported events for the cited work
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Observation 457e28ed-38f0-4f8e-bad8-703c93d922f1 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization BEBERT: efficient and robust binary ensemble BERT,
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Source-reported events for the cited work
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization BETA: Binarized Energy-Efficient Transformer Accelerator at the Edge
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Training deep neural networks with 8-bit floating point numbers,
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Observation 5b8278ff-8f14-4b08-bb71-37e1c3672f58 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding
Reference 22
Source-reported events for the cited work
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization LoRETTA: low-rank eco- nomic tensor-train adaptation for ultra-low-parameter fine-tuning of large language models,
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization AdaZeta: adaptive zeroth-order tensor-train adaption for memory- efficient large language models fine-tuning,
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Source-reported events for the cited work
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization An algorithm–hardware co-optimized framework for accelerating N: M sparse transformers,
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Source-reported events for the cited work
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Observation 45151565-c3d7-45dd-b07e-892a0867effa · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Efficient N: M sparse DNN training using algorithm, architecture, and dataflow co-design,
Reference 26
Source-reported events for the cited work
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization MINILM: Deep self-attention distillation for task-agnostic compression of pre- trained transformers,
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Wanda++: Pruning large language models via regional gradients,
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Compressing dma engine: Leveraging activation sparsity for training deep neural networks,
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Quantized neural networks: Training neural networks with low pre- cision weights and activations,
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Observation cb9ac2de-89c6-42cb-b596-2e677cfb0945 · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Deep learning with limited numerical precision,
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Observation 296d507f-1d22-48c6-987a-8c7719f6e57d · outbound
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Speeding-up convolutional neural networks using fine-tuned cp- decomposition,
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Observation 9602d4be-65ce-4962-bb6e-9ecfb9e2f3ef · outbound
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Observation 75f57b58-d873-4cca-bea8-9d6b4f68abd7 · outbound
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Observation ec34acf0-f02b-4dab-aa08-215738260a02 · outbound
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Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation
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Observation eb7b8697-aa6d-4699-a0bd-2b0b95f473b9 · outbound
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Observation 1ed1fc16-e507-47e4-9e6e-f0eafa3719ba · outbound
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Reference 1983
Source-reported events for the cited work
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No inbound Pith citation observations are available.