StreamKL is the first fused GPU primitive for attention KL divergence that reduces memory from O(N_Q N_K) to O(1) via an online one-pass formulation and tile-wise recomputation.
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PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transforma- tion and Graph Compilation
Mixed citation behavior. Most common role is background (57%).
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representative citing papers
First empirical study of correctness bugs in torch.compile characterizes their patterns and proposes AlignGuard, which found 23 confirmed new bugs via LLM-guided test mutation.
A VGG10 predictive coding network is trained on ImageNet via equilibrium propagation to 13.23% top-5 error, close to the 12.2% backpropagation baseline, marking the first such demonstration at this scale.
Reformulates RkNN queries as graphics ray casting to leverage GPU ray-tracing cores, claiming better performance than prior methods in challenging spatial database scenarios.
AIGaitor is the first claimed end-to-end on-device monocular motion-capture and deep-learning gait analysis pipeline demonstrated on consumer smartphones.
DLR-Lock locks open-weight LLMs against unauthorized fine-tuning by swapping MLPs for deep low-rank residual networks that inflate backprop memory and complicate optimization, yet preserve original capabilities via module-wise distillation.
An FPGA implementation of a neuromorphic auditory sensor plus graph neural network achieves 87.43% accuracy on Google Speech Commands v2 with sub-35 µs latency and 1.12 W power.
A text-supervised global layout embedding augments local patch representations in late-interaction VDR, yielding +2.4 nDCG@5 and +2.3 MAP@5 gains over ColPali/ColQwen baselines on ViDoRe-v2.
VNN-LIB 2.0 defines a network theory abstraction, formal query syntax, type system over numeric domains, and Agda-mechanized semantics to provide rigorous foundations for neural network verification independent of evolving model formats.
Sarus Suite shows HPC can match production container performance using an unmodified Podman engine plus explicit system layers for scheduling, scalable images, and host integration.
Latent Grammar Flow discovers ODEs by placing grammar-based equation representations in a discrete latent space, using a behavioral loss to cluster similar equations, and sampling via a discrete flow model guided by data fit and constraints.
A large benchmark finds traditional imputation methods for scRNA-seq data generally outperform deep learning ones, but numerical recovery does not reliably improve biological downstream analyses and no method wins across all settings.
Sketch-based regularization allows in situ training of implicit neural compressors to approximately match offline performance on 2D/3D simulation data at high compression rates.
MALOQ introduces a scalable SO(2)-equivariant ML framework with custom kernels and edge-wise graph distribution for predicting large-scale quantum transport operators.
XCheck extracts cross-layer constraints to generate test models and monitor behaviors, revealing 2,034 compiler-platform interaction bugs in three DL compilers.
GF-DiT introduces elastic GPU parallelism scheduling for DiT serving via asynchronous trajectory tasks and group-free collectives, reporting up to 6.01x throughput gains over static configurations.
The paper constructs an SCPI dataset via LLM-based annotation and trains classifiers to detect sensitive personal information in Japanese pre-training corpora, claiming this is the first such exploration.
WHET applies fine-grained coefficient-to-slot transforms, plaintext compression, and modulus raising plus lightweight hardware tweaks to FHE accelerators, delivering 1.38-8.74x per-area gains and sub-millisecond CKKS bootstrapping.
PiSO computes exact optimal channel-wise quantization scales for PTQ by partitioning the scale search space into intervals admitting closed-form minimizers, with extensions to group-wise quantization and error correction.
ANNS-AMP adapts distance-computation precision to vector-space regions via a lightweight cluster-level predictor and a bit-serial accelerator, delivering 163.76x/10.57x/2.06x average speedups and 1100x/39.41x/6.66x energy reductions versus CPU/GPU/custom baselines with <2.7% accuracy loss.
KForge uses dual LLM agents for cross-platform kernel generation, reporting 2.12% throughput gain on NVIDIA B200 vs TensorRT-LLM and 5.13x geometric mean speedup on Intel Arc B580 vs PyTorch on 37 workloads.
PINN failure modes are overfitting to collocation points; regularization and double backpropagation over full residuals fix them, achieving SOTA with up to 23x fewer points on standard benchmarks.
Introduces specular gradients and three convergent subgradient selection methods for nonsmooth convex optimization in Euclidean spaces.
Courant is a state-adaptive Perceiver encoder-processor-decoder surrogate trained with L2 loss that yields interpretable, multiscale, locally supported latent features acting as time-evolving spatial basis functions.
citing papers explorer
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The Role and Relationship of Initialization and Densification in 3D Gaussian Splatting
Current densification methods in 3D Gaussian Splatting do not significantly benefit from dense initializations and perform similarly to sparse SfM-based ones.
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Hyperdimensional Decoding of Spiking Neural Networks
SNN-HDC decoding delivers better accuracy, lower latency, and 1.24x-3.67x lower estimated energy than standard methods on DvsGesture and SL-Animals-DVS while detecting 100% of samples from an untrained class.
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A Study of Parallel Continuous Local Search
Empirical study of parallel continuous local search for SAT finds redundant constraints can slow convergence, CLS works as a hybrid sub-solver, and search stabilizes quickly due to saddle-dense objectives.
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Viability of Tensor Train Methods for Geophysical Fluid Dynamics
Tensor train methods compress and accelerate simple GFD flows but struggle to represent complex realistic states in shallow water equation tests.
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Can Muon Fine-tune Adam-Pretrained Models?
Constraining fine-tuning updates with LoRA mitigates performance degradation when switching from Adam to Muon on pretrained models.
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Evaluating Artificial Intelligence Algorithms for the Standardization of Transtibial Prosthetic Socket Shape Design
Random forest predicting prosthetist adaptations from limb scans achieves median surface-to-surface error of 1.24 mm, outperforming direct socket shape prediction and other models.
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An Engineering Journey Training Large Language Models at Scale on Alps: The Apertus Experience
Apertus, a 70B open multilingual foundation model, was pre-trained on the Alps supercomputer, with details on adapting HPC infrastructure into a resilient ML platform.
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Benchmarking Quantum Red TEA on CPUs, GPUs, and TPUs
Benchmarking of variational tensor network ground-state searches reports 34x CPU speedup via parameter tuning and an additional 2.76x gain when moving to GPUs.
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Quantum-inspired tensor networks in machine learning models
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
- GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2
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