Papers GPU
“GPU” 태그가 달린 논문 5,629편 · 필터 해제
CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning
The exponential growth in demand for GPU computing resources, driven by the rapid advancement of Large Language Models, has created an urgent need for automated CUDA optimization strategies. While recent advances in LLMs…
Code GenerationGPUreinforcement-learningReinforcement LearningDINO-VO: A Feature-based Visual Odometry Leveraging a Visual Foundation Model
Learning-based monocular visual odometry (VO) poses robustness, generalization, and efficiency challenges in robotics. Recent advances in visual foundation models, such as DINOv2, have improved robustness and generalizat…
GPUMonocular Visual OdometryMotion EstimationPose Estimation+1Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models
This paper addresses the challenge of high-fidelity view synthesis of humans with sparse-view videos as input. Previous methods solve the issue of insufficient observation by leveraging 4D diffusion models to generate vi…
DenoisingGPUArctic Inference with Shift Parallelism: Fast and Efficient Open Source Inference System for Enterprise AI
Inference is now the dominant AI workload, yet existing systems force trade-offs between latency, throughput, and cost. Arctic Inference, an open-source vLLM plugin from Snowflake AI Research, introduces Shift Parallelis…
GPUFourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed to respect spherical geometry and to acc…
Computational EfficiencyGPUWeather ForecastingKevin: Multi-Turn RL for Generating CUDA Kernels
Writing GPU kernels is a challenging task and critical for AI systems' efficiency. It is also highly iterative: domain experts write code and improve performance through execution feedback. Moreover, it presents verifiab…
GPUReinforcement Learning (RL)Relative Entropy Pathwise Policy Optimization
Score-function policy gradients have delivered strong results in game-playing, robotics and language-model fine-tuning. Yet its high-variance often undermines training stability. On the other hand, pathwise policy gradie…
GPUDEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation
Pixel-level annotation is expensive and time-consuming. Semi-supervised segmentation methods address this challenge by learning models on few labeled images alongside a large corpus of unlabeled images. Although foundati…
DecoderGPUPanoptic SegmentationSemantic Segmentation+3Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning
Poultry farming is a vital component of the global food supply chain, yet it remains highly vulnerable to infectious diseases such as coccidiosis, salmonellosis, and Newcastle disease. This study proposes a lightweight m…
Computational EfficiencyDimensionality Reductionfeature selectionGPUScaling Attention to Very Long Sequences in Linear Time with Wavelet-Enhanced Random Spectral Attention (WERSA)
Transformer models are computationally costly on long sequences since regular attention has quadratic $O(n^2)$ time complexity. We introduce Wavelet-Enhanced Random Spectral Attention (WERSA), a novel mechanism of linear…
GPUHNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
In medical image segmentation, convolutional neural networks (CNNs) and transformers are dominant. For CNNs, given the local receptive fields of convolutional layers, long-range spatial correlations are captured through …
GPUImage SegmentationMedical Image SegmentationSemantic Segmentation+1From large-eddy simulations to deep learning: A U-net model for fast urban canopy flow predictions
Accurate prediction of wind flow fields in urban canopies is crucial for ensuring pedestrian comfort, safety, and sustainable urban design. Traditional methods using wind tunnels and Computational Fluid Dynamics, such as…
GPUL2 RegularizationArtificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning
We introduce a real-time strategy game environment built on Generals.io, a game that hosts thousands of active players each week across multiple game formats. Our environment is fully compatible with Gymnasium and Pettin…
GPUMulti-agent Reinforcement Learningreinforcement-learningReinforcement LearningDiffusion Dataset Condensation: Training Your Diffusion Model Faster with Less Data
Diffusion models have achieved remarkable success in various generative tasks, but training them remains highly resource-intensive, often requiring millions of images and many days of GPU computation. From a data-centric…
Dataset CondensationGPUAutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations through extensive empirical tuning. Although…
GPUreinforcement-learningReinforcement LearningReinforcement Learning (RL)any4: Learned 4-bit Numeric Representation for LLMs
We present any4, a learned 4-bit weight quantization solution for large language models (LLMs) providing arbitrary numeric representations without requiring pre-processing of weights or activations. any4 yields higher ac…
GPUGSM8KHumanEvalmbpp+2Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining
Graph-based Point Cloud Networks (PCNs) are powerful tools for processing sparse sensor data with irregular geometries, as found in high-energy physics detectors. However, deploying models in such environments remains ch…
GPULLMThinkBench: Towards Basic Math Reasoning and Overthinking in Large Language Models
Large Language Models (LLMs) have achieved remarkable performance on complex mathematical benchmarks, yet often struggle with simple arithmetic tasks and exhibit a tendency toward over-explaining or "overthinking" answer…
BenchmarkingGPUMathMathOptAI.jl: Embed trained machine learning predictors into JuMP models
We present \texttt{MathOptAI.jl}, an open-source Julia library for embedding trained machine learning predictors into a JuMP model. \texttt{MathOptAI.jl} can embed a wide variety of neural networks, decision trees, and G…
CPUGaussian ProcessesGPULoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs
Low-Rank Adapters (LoRAs) have transformed the fine-tuning of Large Language Models (LLMs) by enabling parameter-efficient updates. However, their widespread adoption remains limited by the reliance on GPU-based training…
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