Outstanding-Sparse: Sparse Design Principles

• Outstanding-sparse is a research paradigm that treats sparsity as a design principle rather than just a compression constraint, enhancing model quality and computational efficiency.
• The approach applies to diverse domains including pre-trained language models with one-shot pruning and re-dense reconstruction, graph Fourier analysis, vision tasks, and hardware acceleration.
• Key methodologies such as sparse-dense-sparse pruning, local sparse representations, and intrinsic sparse connectivity lead to measurable improvements in metrics like perplexity, throughput, and AUC.

to=arxiv_search.search 天天中彩票nbajson {"^^^^^^^^9query9 sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^^^^^", "^^^^^^^^9max_results9 ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^, "^^^^^^^^9sort_by9 "^^^^^^^^9submittedDate9 "^^^^^^^^9sort_order9 "^^^^^^^^9descending9 to=arxiv_search.search 公众号天天中彩票json {"^^^^^^^^9query9 One-shot Pruned Pre-trained LLMs through Sparse-Dense-Sparse Mechanism\"", "^^^^^^^^9max_results9 ^^^^9sort_order9^^^^, "^^^^^^^^9sort_by9 "relevance", "^^^^^^^^9sort_order9 "^^^^^^^^9descending9 to=arxiv_search.search 】【。】【”】【json {"^^^^^^^^9query9 "^^^^^^^^9max_results9 ^^^^9sort_order9^^^^, "^^^^^^^^9sort_by9 "relevance", "^^^^^^^^9sort_order9 "^^^^^^^^9descending9 "Outstanding-sparse" (Editor's term) denotes a recurrent research pattern in which sparsity is treated not only as a compression constraint but as a design principle that can improve model quality, computational efficiency, locality of representation, or network behavior. In the cited literature, this pattern appears in one-shot pruning for pre-trained LLMs, regression-based graph Fourier analysis, homotopy methods for sparse learning, GPU SpGEMM, FPGA and NPU accelerator design, oriented object detection, human pose estimation, intrinsically sparse recurrent networks, and synchronization of semiconductor lasers (&&&^^^^9query9^^^^&&&, &&&^^^^9max_results9^^^^&&&, &&&^^^^9sort_by9^^^^&&&, &&&^^^^9submittedDate9^^^^&&&, &&&^^^^9sort_order9^^^^&&&, &&&^^^^9descending9^^^^&&&, &&&^^^^9query9^^^^&&&, &&&^^^^9ti:\9^^^^&&&, Liu et al., 2019, &&&^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^&&&).

^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^. Forms of sparsity and their operational roles

The cited works instantiate sparsity at several distinct levels. In pre-trained LLMs and LSTMs, sparsity is imposed directly on weights or connections, either through one-shot pruning followed by reconstruction or through intrinsically sparse initialization and rewiring (&&&^^^^9query9^^^^&&&, Liu et al., 2019). In sparse GFT, sparsity is imposed on analysis vectors through an PRESERVED_PLACEHOLDER_^^^^9query9^^^^ penalty, so that transform components become localized on subsets of graph vertices rather than remaining global eigenvectors (&&&^^^^9max_results9^^^^&&&). In PSM, sparse learning is cast as a family of linear programs parametrized by a regularization factor, and the algorithmic advantage comes from tracking a sparse solution path with few pivots (&&&^^^^9sort_by9^^^^&&&).

A different class of work uses sparsity as a computational substrate. OpSparse optimizes sparse general matrix multiplication on GPUs through shared-memory binning, hashing, minimized metadata, and overlap of allocation with execution (&&&^^^^9submittedDate9^^^^&&&). LogicSparse embeds unstructured sparsity into a fully-pipelined, dataflow style QNN accelerator at compile time, eliminating any separate “sparsity engine” (&&&^^^^9sort_order9^^^^&&&). SparseCol exploits training-free structured bit-level sparsity, specifically bit-column sparsity, within a dynamic dataflow processor (&&&^^^^9descending9^^^^&&&).

In vision, sparsity appears as sparse learnable proposals and sparse high-resolution regions. Sparse R-CNN OBB uses only ^^^^9sort_by9query9query9^^^^ rotated learnable proposals instead of dense anchor grids (&&&^^^^9query9^^^^&&&). SHaRPose performs a coarse pass over the whole image, then constructs sparse high-resolution representations only on regions related to the keypoints and refines them conditionally through a quality predictor (&&&^^^^9ti:\9^^^^&&&). In dynamical systems, sparsity governs network topology itself: optimal sparse coupling configurations in semiconductor laser arrays can achieve near-complete synchronization and, in some cases, outperform fully coupled networks (&&&^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^&&&).

^^^^9max_results9^^^^. Sparse–Dense–Sparse pruning in pre-trained LLMs

The SDS framework formalizes a three-phase pruning workflow for pre-trained LLMs: initial one-shot pruning, sparse-regularized re-dense reconstruction, and final one-shot re-pruning with weight adjustment (&&&^^^^9query9^^^^&&&). In the first phase, the initial sparse model is produced by conventional one-shot pruning methods. The paper explicitly considers column-wise second-order pruning (SparseGPT) and magnitudePRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^activation pruning (Wanda). SparseGPT uses the Hessian-based salience score

PRESERVED_PLACEHOLDER_^^^^9max_results9^^^^

whereas Wanda uses PRESERVED_PLACEHOLDER_^^^^9sort_by9^^^^.

The central contribution is the re-dense phase, whose goal is to “reactivate” pruned connections and construct a dense model with a more pruning-friendly weight distribution. The layer-wise reconstruction objective is

PRESERVED_PLACEHOLDER_^^^^9submittedDate9^^^^

with PRESERVED_PLACEHOLDER_^^^^9sort_order9^^^^ by default and with the constraint that PRESERVED_PLACEHOLDER_^^^^9descending9^^^^ reactivates all positions zeroed by the initial mask. The dense reconstruction uses layer-wise PRESERVED_PLACEHOLDER_^^^^9query9^^^^ knowledge alignment from the original dense model, preserves the initial sparse mask as a prior, favors hard examples with high-loss sparse activations, and adds traditional PRESERVED_PLACEHOLDER_^^^^9ti:\9^^^^ penalties. A typical setting is ^^^^9max_results9query9query9^^^^ epochs of layer-wise distillation on ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9ti:\9^^^^ C^^^^9submittedDate9^^^^ samples (&&&^^^^9query9^^^^&&&).

The final phase re-prunes the reconstructed dense model with the same one-shot criterion and then applies a tiny $L_2$ “weight adjustment” under a soft mask:

PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^

where PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^ is dynamically selected by the magnitudes PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9^^^^. The reported explanation is that the re-dense phase transforms a single-peak Gaussian into a tri-modal, sharply-peaked around zero distribution, which improves “pruning friendliness” by separating near-zero weights from those to retain.

Quantitatively, for OPT-^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9sort_order9^^^^M with ^^^^9max_results9^^^^:^^^^9submittedDate9^^^^ sparsity on Raw-WikiText^^^^9max_results9^^^^, the dense baseline has perplexity ^^^^9max_results9query9^^^^.^^^^9descending9descending9 SparseGPT ^^^^9descending9query9^^^^.^^^^9submittedDate9sort_by9 SDS (SparseGPT base) ^^^^9sort_order9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9sort_by9query9^^^^, Wanda ^^^^9ti:\9max_results9^^^^.^^^^9submittedDate9query9 and SDS (Wanda base) ^^^^9sort_order99^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^. On seven zero-shot tasks for OPT-^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9sort_order9^^^^M with ^^^^9max_results9^^^^:^^^^9submittedDate9^^^^ sparsity, SparseGPT averages ^^^^9submittedDate9query9^^^^.^^^^9sort_order9descending9 SDS (SparseGPT base) ^^^^9submittedDate99^^^^.^^^^9descending9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^%, Wanda ^^^^9submittedDate9sort_order9^^^^.^^^^9descending99 and SDS (Wanda base) ^^^^9submittedDate9query9^^^^.^^^^9query99 Across OPT-^^^^9sort_by9sort_order9query9^^^^M, OPT-^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9sort_by9^^^^B, and LLaMA-^^^^9query9^^^^B at ^^^^9sort_order9query9^^^^% and ^^^^9submittedDate9^^^^:^^^^9ti:\9^^^^ sparsity, SDS is reported to cut perplexity by ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9ti:\9^^^^– points and boost zero-shot accuracy by ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9ti:\9^^^^– over same-sparsity SparseGPT or Wanda baselines (&&&^^^^9query9^^^^&&&). These results directly challenge the common assumption that one-shot pruning must entail an indispensable performance reduction.

^^^^9sort_by9^^^^. Sparse representations and sparse solution paths

Sparse GFT replaces the classical eigen-decomposition view of graph Fourier analysis with a regression-based construction that admits regularization on the analysis components (&&&^^^^9max_results9^^^^&&&). Starting from the normalized Laplacian PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9sort_by9^^^^ and a factorization PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9submittedDate9^^^^, the sparse formulation is

PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9sort_order9^^^^

The PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9descending9^^^^ term drives many entries of each analysis vector PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^ to exactly zero. The stated effect is that sparse components identify and select correlated signal sources into sub-graphs and perform frequency analysis locally within those sub-graphs. On the Abilene backbone network traffic dataset, Sparse GFT attains average AUC values of ^^^^9ti:\9descending9^^^^.^^^^9sort_by9sort_order9 ^^^^9ti:\9max_results9^^^^.^^^^9sort_order9query9 and ^^^^9query9query9^^^^.^^^^9sort_order9ti:\9^^^^ across three time slices, compared with baseline values including PCA ^^^^9query9descending9^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs99^^^^/^^^^9descending9submittedDate9^^^^. LPP ^^^^9descending9sort_order9^^^^.^^^^9max_results9sort_by9 sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^/^^^^9submittedDate99^^^^. RPCA ^^^^9query9max_results9^^^^.^^^^9sort_by9sort_order9 sparse pruning sparse-dense-sparse one-shot pruning PLMs9ti:\9^^^^, RPCAG ^^^^9query9sort_by9^^^^.^^^^9sort_order9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^/^^^^9descending9sort_by9^^^^. sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^/^^^^9sort_order9submittedDate9^^^^. FRPCAG ^^^^9query9descending9^^^^.^^^^9sort_order9submittedDate9 and GLPCA ^^^^9query9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9descending9ti:\9^^^^/ (&&&^^^^9max_results9^^^^&&&).

PSM addresses a different sparse-learning regime: linear programs parametrized by a regularization factor (&&&^^^^9sort_by9^^^^&&&). The method is formulated for sparse learning approaches such as the Dantzig selector for sparse linear regression, LAD-Lasso for sparse robust linear regression, CLIME for sparse precision matrix estimation, sparse differential network estimation, and sparse Linear Programming Discriminant analysis. The cited advantages are that PSM naturally obtains the complete solution path for all values of the regularization parameter, provides a high precision dual certificate stopping criterion, and yields sparse solutions through very few iterations. Under restricted eigenvalue conditions, the paper states that the path remains sparse, with

PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9ti:\9^^^^

and overall complexity

PRESERVED_PLACEHOLDER_^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs99^^^^

In numerical experiments with Gaussian design, PRESERVED_PLACEHOLDER_^^^^9max_results9query9^^^^, PRESERVED_PLACEHOLDER_^^^^9max_results9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^, true PRESERVED_PLACEHOLDER_^^^^9max_results9max_results9^^^^, and noise PRESERVED_PLACEHOLDER_^^^^9max_results9sort_by9^^^^, PSM reports runtime PRESERVED_PLACEHOLDER_^^^^9max_results9submittedDate9^^^^ sec versus PRESERVED_PLACEHOLDER_^^^^9max_results9sort_order9^^^^ for Dantzig-PDIP, PRESERVED_PLACEHOLDER_^^^^9max_results9descending9^^^^ for LAD-Lasso, PRESERVED_PLACEHOLDER_^^^^9max_results9query9^^^^ for CLIME, PRESERVED_PLACEHOLDER_^^^^9max_results9ti:\9^^^^ for sparse differential network, and PRESERVED_PLACEHOLDER_^^^^9max_results99^^^^ for sparse LPD; its estimation PRESERVED_PLACEHOLDER_^^^^9sort_by9query9^^^^-error is PRESERVED_PLACEHOLDER_^^^^9sort_by9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^, compared with PRESERVED_PLACEHOLDER_^^^^9sort_by9max_results9^^^^ for Dantzig and PRESERVED_PLACEHOLDER_^^^^9sort_by9sort_by9^^^^ for LAD-Lasso (&&&^^^^9sort_by9^^^^&&&).

Taken together, these papers show two distinct but compatible interpretations of “outstanding-sparse.” One relies on sparse loadings to produce local, interpretable analysis vectors on graphs; the other relies on sparse active sets so that the optimization path itself becomes computationally light. A plausible implication is that sparsity can improve both the semantics of representation and the mechanics of solving the associated estimation problem.

^^^^9submittedDate9^^^^. Sparse computation as software–hardware co-design

OpSparse addresses the irregularity of SpGEMM on GPUs by targeting seven categories of inefficient implementation in cuSPARSE, nsparse, and spECK (&&&^^^^9submittedDate9^^^^&&&). Its optimizations include shared-memory–based two-pass binning, single-load atomicCAS hashing, experimentally tuned binning ranges, minimized global metadata memory, overlapping cudaMalloc with kernel execution, SM launch-order and cudaFree avoidance, and occupancy tuning. The reported result on ^^^^9max_results9descending9^^^^ matrices on an Nvidia Tesla V^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9query9^^^^ GPU is up to PRESERVED_PLACEHOLDER_^^^^9sort_by9submittedDate9^^^^, PRESERVED_PLACEHOLDER_^^^^9sort_by9sort_order9^^^^, and PRESERVED_PLACEHOLDER_^^^^9sort_by9descending9^^^^ speedup over cuSPARSE, nsparse, and spECK, respectively, with average speedups of PRESERVED_PLACEHOLDER_^^^^9sort_by9query9^^^^, PRESERVED_PLACEHOLDER_^^^^9sort_by9ti:\9^^^^, and PRESERVED_PLACEHOLDER_^^^^9sort_by99^^^^. For the two binning steps alone, OpSparse is on average PRESERVED_PLACEHOLDER_^^^^9submittedDate9query9^^^^ faster than nsparse and PRESERVED_PLACEHOLDER_^^^^9submittedDate9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^ faster than spECK (&&&^^^^9submittedDate9^^^^&&&).

LogicSparse transfers the sparse-computation problem to compile time in a fully-pipelined, dataflow style QNN accelerator (&&&^^^^9sort_order9^^^^&&&). Each processing element contains a small LUT-based mask decoder and a SIMD multiplier–accumulator array; weights and masks are stored together, and at runtime the mask decoder gates off multipliers for zero weights and dynamically shifts the remaining weight–activation pairs into the SIMD array. For a single output channel PRESERVED_PLACEHOLDER_^^^^9submittedDate9max_results9^^^^, the sparse inner product is

PRESERVED_PLACEHOLDER_^^^^9submittedDate9sort_by9^^^^

The pruning workflow combines global magnitude-based pre-pruning with iterative, layer-wise hardware-aware pruning during design space exploration, under the constrained objective

PRESERVED_PLACEHOLDER_^^^^9submittedDate9submittedDate9^^^^

On LeNet-^^^^9sort_order9^^^^, LogicSparse attains ^^^^9sort_order9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9descending9^^^^ x compression and ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9max_results9sort_by9^^^^ x throughput improvement using only ^^^^9sort_order9^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9^^^^% of LUTs. In the detailed comparison, the dense Unfold baseline has ^^^^9max_results9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9submittedDate9^^^^,9^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs99^^^^ FPS and 9^^^^9max_results9^^^^.^^^^9max_results9 LUT use, Unfold + Prune has ^^^^9max_results9sort_order9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^,^^^^9max_results9descending9sort_order9^^^^ FPS and ^^^^9max_results9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9submittedDate9^^^^% LUT use, and LogicSparse reaches ^^^^9max_results9descending9sort_order9^^^^,^^^^9submittedDate9max_results99^^^^ FPS with ^^^^9sort_order9^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9^^^^% LUT use (&&&^^^^9sort_order9^^^^&&&).

SparseCol addresses bit-level sparsity in bit-serial computation through training-free structured sparsity, specifically bit-column sparsity, and a runtime-selectable dynamic dataflow (&&&^^^^9descending9^^^^&&&). The processor groups weights in the channel dimension and defines a bit-column mask PRESERVED_PLACEHOLDER_^^^^9submittedDate9sort_order9^^^^ so that full-zero bit-columns can be skipped wholesale. It further uses Sign-Magnitude representation and an optional Bit-Flip enhancement for layers with low original SignM sparsity. Fabricated in TSMC ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9descending9^^^^nm FinFET, with die size ^^^^9descending9^^^^.^^^^9sort_order9^^^^ mm² and on-chip memory of L^^^^9max_results9^^^^ = ^^^^9max_results9^^^^ MB and L^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^ = ^^^^9sort_order9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9^^^^ kB, SparseCol reports ^^^^9sort_order9sort_by9ti:\9^^^^.^^^^9submittedDate9^^^^ BTOPS/W at ^^^^9query9^^^^ zero-columns and ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9sort_by9max_results9query9^^^^.^^^^9ti:\9^^^^ BTOPS/W at ^^^^9descending9^^^^ zero-columns, both at ^^^^9query9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^ MHz, and ^^^^9submittedDate9^^^^.^^^^9ti:\9descending9^^^^ BTOPS and ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9descending9descending9^^^^ BTOPS, respectively, at ^^^^9max_results9ti:\9query9^^^^ MHz. Full-network evaluations give ^^^^9query9submittedDate9sort_order9^^^^.^^^^9query9max_results9^^^^ BTOPS/W for ResNet^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9ti:\9^^^^ on ImageNet, ^^^^9ti:\9sort_order9query9^^^^.^^^^9sort_order9^^^^ BTOPS/W for BERT-Base on MRPC, and ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9query9^^^^.^^^^9max_results9^^^^ BTOPS/W for CNN-LSTM on CRUSE. The paper states that peak S-BTOPS/W of ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9sort_by9max_results9query9^^^^.^^^^9ti:\9^^^^ is ^^^^9descending9^^^^.^^^^9ti:\9 higher than the nearest taped-out digital sparse accelerator, Onyx at ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs99sort_by9^^^^.^^^^9sort_order9^^^^ BTOPS/W (&&&^^^^9descending9^^^^&&&).

These three systems exemplify a consistent shift in sparse computing. Rather than treating irregularity as a secondary implementation nuisance, they redesign scheduling, metadata, memory allocation, masking, and dataflow so that sparse structure is directly aligned with the execution substrate.

^^^^9sort_order9^^^^. Sparse proposals and sparse high-resolution regions in vision

Sparse R-CNN OBB adapts sparse learnable proposals to oriented object detection in SAR imagery (&&&^^^^9query9^^^^&&&). Its backbone is ResNet-^^^^9sort_order9query9^^^^ with FPN, and it uses only ^^^^9sort_by9query9query9^^^^ rotated learnable proposals rather than dense anchor grids. Each proposal contains a ^^^^9max_results9sort_order9descending9^^^^-D feature vector and five box parameters PRESERVED_PLACEHOLDER_^^^^9submittedDate9descending9^^^^, initialized identically as PRESERVED_PLACEHOLDER_^^^^9submittedDate9query9^^^^, PRESERVED_PLACEHOLDER_^^^^9submittedDate9ti:\9^^^^, PRESERVED_PLACEHOLDER_^^^^9submittedDate99^^^^, PRESERVED_PLACEHOLDER_^^^^9sort_order9query9^^^^, and PRESERVED_PLACEHOLDER_^^^^9sort_order9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^. Six identical dynamic heads iteratively refine proposals using Rotated RoIAlign and updated object features. The box update equations are

PRESERVED_PLACEHOLDER_^^^^9sort_order9max_results9^^^^

PRESERVED_PLACEHOLDER_^^^^9sort_order9sort_by9^^^^

On the RSDD-SAR dataset, Sparse R-CNN OBB reports APPRESERVED_PLACEHOLDER_^^^^9sort_order9submittedDate9^^^^ of 9^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9query9ti:\9^^^^, APPRESERVED_PLACEHOLDER_^^^^9sort_order9sort_order9^^^^ Inshore of ^^^^9descending9descending9^^^^.^^^^9max_results9sort_order9 and APPRESERVED_PLACEHOLDER_^^^^9sort_order9descending9^^^^ Offshore of 9^^^^9descending9^^^^.^^^^9max_results9max_results9 compared with CFA at ^^^^9ti:\99^^^^.^^^^9sort_by9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^/^^^^9descending9descending9^^^^. and Oriented R-CNN at ^^^^9ti:\9ti:\9^^^^.^^^^9ti:\9submittedDate9 sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^. In the ablation on proposal count, ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9query9^^^^, ^^^^9max_results9query9query9^^^^, and ^^^^9sort_by9query9query9^^^^ proposals yield APPRESERVED_PLACEHOLDER_^^^^9sort_order9query9^^^^ values of 9^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9query9max_results9^^^^, 9^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9sort_by9max_results9^^^^, and 9^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9query9ti:\9^^^^, respectively, with model sizes ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9descending9^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9sort_by9^^^^M, ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9descending9^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9sort_order9^^^^M, and ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9descending9^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9ti:\9^^^^M, training times ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9descending9sort_by9^^^^ h, ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9sort_by9^^^^.^^^^9sort_by9submittedDate9^^^^ h, and ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9submittedDate9^^^^.^^^^9descending9query9^^^^ h, and FPS ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9submittedDate9^^^^, ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9^^^^, and ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^ (&&&^^^^9query9^^^^&&&).

SHaRPose applies sparsity to high-resolution representation for human pose estimation (&&&^^^^9ti:\9^^^^&&&). The model has a coarse stage and a fine stage. In the coarse stage, image regions and keypoints are dynamically mined while a coarse estimation is generated; a quality predictor then decides whether refinement is necessary. The relevance scores for coarse visual tokens are aggregated from region-to-keypoint attention and used to select the top PRESERVED_PLACEHOLDER_^^^^9sort_order9ti:\9^^^^ patches. The quality predictor outputs

PRESERVED_PLACEHOLDER_^^^^9sort_order99^^^^

and if PRESERVED_PLACEHOLDER_^^^^9descending9query9^^^^ exceeds the threshold PRESERVED_PLACEHOLDER_^^^^9descending9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^, the model accepts the coarse result; otherwise it proceeds to sparse fine refinement. The total loss is

PRESERVED_PLACEHOLDER_^^^^9descending9max_results9^^^^

with PRESERVED_PLACEHOLDER_^^^^9descending9sort_by9^^^^ in the main experiments and PRESERVED_PLACEHOLDER_^^^^9descending9submittedDate9^^^^ for the first ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9ti:\9query9^^^^ epochs, then PRESERVED_PLACEHOLDER_^^^^9descending9sort_order9^^^^.

On COCO validation at ^^^^9sort_by9ti:\9submittedDate9^^^^×^^^^9max_results9ti:\9ti:\9 ViTPose-Base obtains ^^^^9query9descending9^^^^.9 AP, ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9submittedDate9sort_by9^^^^ FPS, and ^^^^9submittedDate9submittedDate9^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^ G FLOPs, whereas SHaRPose-Base obtains ^^^^9query9query9^^^^.^^^^9submittedDate9^^^^ AP, ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs99query9^^^^ FPS, and ^^^^9sort_by9max_results9^^^^.9 G FLOPs. On COCO test-dev at ^^^^9sort_by9ti:\9submittedDate9^^^^×^^^^9max_results9ti:\9ti:\9 ViTPose-Base has ^^^^9query9descending9^^^^.^^^^9max_results9^^^^ AP and ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9submittedDate9sort_by9^^^^ FPS, whereas SHaRPose-Base has ^^^^9query9descending9^^^^.^^^^9query9^^^^ AP and ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs99query9^^^^ FPS. The paper also reports that PRESERVED_PLACEHOLDER_^^^^9descending9descending9^^^^ achieves ^^^^9query9sort_order9^^^^.^^^^9sort_order9^^^^ AP with ^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^ G FLOPs, whereas full high-res with PRESERVED_PLACEHOLDER_^^^^9descending9query9^^^^ costs ^^^^9max_results9submittedDate9^^^^.9 G FLOPs for only +^^^^9query9^^^^.^^^^9max_results9^^^^ AP. Its comparison to DynamicViT and EViT argues that discarding tokens globally harms localization (&&&^^^^9ti:\9^^^^&&&).

Both systems contradict the assumption that dense candidate generation or uniformly dense high-resolution processing is inherently necessary for strong detection or pose estimation. Here, sparsity is used not to weaken coverage, but to concentrate iterative refinement on a deliberately small set of proposals or regions.

^^^^9descending9^^^^. Intrinsic sparse connectivity and sparse dynamical coupling

SET-LSTM makes sparsity intrinsic to recurrent modeling rather than applying compression after dense pre-training (Liu et al., 2019). Each bipartite layer is initialized with Erdős–Rényi connectivity

PRESERVED_PLACEHOLDER_^^^^9descending9ti:\9^^^^

so that the expected number of connections is PRESERVED_PLACEHOLDER_^^^^9descending99^^^^. After each epoch, a fraction PRESERVED_PLACEHOLDER_^^^^9query9query9^^^^ of the smallest positive weights and a fraction PRESERVED_PLACEHOLDER_^^^^9query9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^ of the largest negative weights are removed, and—except in the final epoch—the same number of new edges are sampled uniformly at random among zero-weights. The procedure is applied both to the LSTM gates and to the embedding layer. With PRESERVED_PLACEHOLDER_^^^^9query9max_results9^^^^, the paper reports approximately 9^^^^9sort_order9^^^^.^^^^9query9 sparsity. On IMDB, the dense LSTM obtains ^^^^9ti:\9sort_order9^^^^.^^^^9max_results9descending9 with ^^^^9sort_order9^^^^,^^^^9descending9submittedDate9sort_order9 sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9^^^^ parameters, whereas SET-LSTM obtains ^^^^9ti:\9descending9^^^^.^^^^9query9submittedDate9 with ^^^^9max_results9submittedDate9sort_by9^^^^,^^^^9submittedDate9submittedDate9max_results9^^^^ parameters and 9^^^^9sort_order9^^^^.^^^^9descending99 sparsity. On Twitter, dense LSTM attains ^^^^9query9query9^^^^.^^^^9query99 and SET-LSTM ^^^^9query99^^^^.^^^^9max_results9max_results9 on Yelp ^^^^9max_results9query9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9ti:\9^^^^, dense LSTM attains ^^^^9descending9sort_by9^^^^.^^^^9sort_by9descending9 and SET-LSTM ^^^^9descending9ti:\9^^^^.^^^^9query9query9 sparse pruning sparse-dense-sparse one-shot pruning PLMs9ti:\9^^^^%; on Amazon Fine Food Reviews, dense LSTM attains ^^^^9ti:\9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^.^^^^9ti:\9ti:\9^^^^% and SET-LSTM ^^^^9ti:\9query9^^^^.^^^^9sort_order9max_results9 sparse pruning sparse-dense-sparse one-shot pruning PLMs9sort_order9^^^^%. Even at 99.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^% sparsity with PRESERVED_PLACEHOLDER_^^^^9query9sort_by9^^^^, SET-LSTM remains competitive: IMDB ^^^^9ti:\9sort_order9^^^^.^^^^9query9sort_order9 versus ^^^^9ti:\9sort_order9^^^^.^^^^9max_results9descending9 dense, Twitter ^^^^9query9ti:\9^^^^.^^^^9ti:\9sort_order9 versus ^^^^9query9query9^^^^.^^^^9query99 and Yelp ^^^^9descending9query9^^^^.^^^^9ti:\9max_results9 versus ^^^^9descending9sort_by9^^^^.^^^^9sort_by9descending9 (Liu et al., 2019).

In semiconductor laser arrays, sparsity governs the adjacency of the coupled physical system rather than the internal parameters of a learned model (&&&^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^&&&). The order parameter is

PRESERVED_PLACEHOLDER_^^^^9query9submittedDate9^^^^

and the optimization problem seeks a sparse binary coupling matrix maximizing PRESERVED_PLACEHOLDER_^^^^9query9sort_order9^^^^ under a hard PRESERVED_PLACEHOLDER_^^^^9query9descending9^^^^ connectivity constraint. For PRESERVED_PLACEHOLDER_^^^^9query9query9^^^^ lasers, detuning width PRESERVED_PLACEHOLDER_^^^^9query9ti:\9^^^^ rad/ns, and coupling strength PRESERVED_PLACEHOLDER_^^^^9query99^^^^ nsPRESERVED_PLACEHOLDER_^^^^9ti:\9query9^^^^, the homogeneous all-to-all network exhibits a coupling resonance with PRESERVED_PLACEHOLDER_^^^^9ti:\9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9^^^^, random sparse configurations of the same total coupling cost yield average PRESERVED_PLACEHOLDER_^^^^9ti:\9max_results9^^^^ with spread PRESERVED_PLACEHOLDER_^^^^9ti:\9sort_by9^^^^, and the optimized sparse network at PRESERVED_PLACEHOLDER_^^^^9ti:\9submittedDate9^^^^ achieves PRESERVED_PLACEHOLDER_^^^^9ti:\9sort_order9^^^^. The reported physical mechanism is that optimal sparse networks place coupling dominantly on laser pairs with large frequency differences, creating “large–detuning hubs” (&&&^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^&&&).

Across these results, a recurring misconception is that sparse systems necessarily underperform dense, fully connected, or all-to-all alternatives. The reported evidence does not support such a generalization. SDS improves same-sparsity PLM pruning baselines, Sparse GFT improves anomaly-detection AUC over listed alternatives, LogicSparse exceeds the throughput of a dense fully-unfolded accelerator while using ^^^^9sort_order9^^^^.^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9max_results9^^^^% of LUTs, SHaRPose surpasses ViTPose-Base at ^^^^9sort_by9ti:\9submittedDate9^^^^×^^^^9max_results9ti:\9ti:\9 SET-LSTM often surpasses dense LSTM with less than ^^^^9submittedDate9^^^^% of its parameters, and optimized sparse laser networks can outperform fully coupled networks (&&&^^^^9query9^^^^&&&, &&&^^^^9max_results9^^^^&&&, &&&^^^^9sort_order9^^^^&&&, &&&^^^^9ti:\9^^^^&&&, Liu et al., 2019, &&&^^^^9all:outstanding sparse pruning sparse-dense-sparse one-shot pruning PLMs9query9^^^^&&&). The literature therefore supports a narrower but technically significant conclusion: when sparsity is aligned with weight distribution, locality, hardware schedule, proposal refinement, or coupling topology, it can function as a constructive inductive bias rather than merely as a budget constraint.