GD4 is a graph-based discrete denoising diffusion method for MIMO detection that yields higher-quality suboptimal solutions than prior diffusion detectors and classical baselines under similar compute budgets in both under- and over-determined settings.
Deep residual learning for image recognition
8 Pith papers cite this work. Polarity classification is still indexing.
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First benchmarking of ordinal adaptations of CNN and DL methods for time series shows they outperform nominal TSC techniques on ordinal metrics across 29 selected problems.
DaiMoN introduces a decentralized ledger-based network for collaborative ML model improvement with label-hidden proof-of-improvement enabled by a novel learnable Distance Embedding for Labels (DEL) function.
MDS-DETR introduces a masked duplicate suppressor in self-attention to enable one-to-many supervision inside a single decoder, yielding +2.8 mAP over Deformable-DETR on COCO with 5% more training time and outperforming MR.DETR by 0.3 mAP while training 20% faster.
DiffVC applies diffusion models for non-autoregressive video captioning, outperforming prior non-AR methods and matching AR ones in quality with faster speed on standard benchmarks.
DeCon decouples LTSSL into head-class and tail-class branches that interact and converge, delivering SOTA accuracy on mismatched-distribution benchmarks and outperforming prior methods even on matched distributions.
The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.
citing papers explorer
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GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection
GD4 is a graph-based discrete denoising diffusion method for MIMO detection that yields higher-quality suboptimal solutions than prior diffusion detectors and classical baselines under similar compute budgets in both under- and over-determined settings.
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Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
First benchmarking of ordinal adaptations of CNN and DL methods for time series shows they outperform nominal TSC techniques on ordinal metrics across 29 selected problems.
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DaiMoN: A Decentralized Artificial Intelligence Model Network
DaiMoN introduces a decentralized ledger-based network for collaborative ML model improvement with label-hidden proof-of-improvement enabled by a novel learnable Distance Embedding for Labels (DEL) function.
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MDS-DETR: DETR with Masked Duplicate Suppressor
MDS-DETR introduces a masked duplicate suppressor in self-attention to enable one-to-many supervision inside a single decoder, yielding +2.8 mAP over Deformable-DETR on COCO with 5% more training time and outperforming MR.DETR by 0.3 mAP while training 20% faster.
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DiffVC: A Non-autoregressive Framework Based on Diffusion Model for Video Captioning
DiffVC applies diffusion models for non-autoregressive video captioning, outperforming prior non-AR methods and matching AR ones in quality with faster speed on standard benchmarks.
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Decouple then Converge: Handling Unknown Unlabeled Distributions in Long-Tailed Semi-Supervised Learning
DeCon decouples LTSSL into head-class and tail-class branches that interact and converge, delivering SOTA accuracy on mismatched-distribution benchmarks and outperforming prior methods even on matched distributions.
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From System 1 to System 2: A Survey of Reasoning Large Language Models
The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.
- Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set