Search: “nvidia”

arXiv:2609.13551v1 Announce Type: new Abstract: Static-image benchmarks do not capture the computational and temporal requirements of practical orchard video analytics. This study presents an end-to-e

arXiv:2509.25180v3 Announce Type: replace Abstract: Existing text-to-image diffusion models excel at generating high-quality images, but face significant efficiency challenges when scaled to high reso

arXiv:2609.08368v1 Announce Type: new Abstract: We present Miles v0.1, a full-stack, production-ready system for frontier post-training. Building upon the clean design of slime, Miles designs each sta

arXiv:2608.24935v1 Announce Type: new Abstract: Accurate identification of early-stage apple fruitlet anatomical structures, including the calyx, fruitlet body, and peduncle, is essential for robotic

arXiv:2608.21764v1 Announce Type: cross Abstract: Event-based vision has emerged as a promising paradigm for energy-aware artificial intelligence (AI), offering sparse, low-latency visual signals that

arXiv:2608.21887v1 Announce Type: cross Abstract: Runtime thermal management of high-performance chips depends on fast and accurate full-chip thermal maps. Conventional simulators typically estimate p

arXiv:2608.20350v1 Announce Type: cross Abstract: Traditional industrial agents rely on modular pipelines, including Router, Retriever, Planner, Executor, Responder, Reviewer, and other components. Th

arXiv:2608.19085v1 Announce Type: cross Abstract: Anticipating how scenes evolve under ego actions is fundamental to safe autonomous driving, yet the full potential of world models for decision-making

arXiv:2608.08287v1 Announce Type: new Abstract: GPU language comparisons are almost always run on tiled dense linear algebra, where every toolchain is good and the differences are small. We implement

arXiv:2608.05033v1 Announce Type: cross Abstract: Sparse matrix kernels are fundamental to scientific computing, graph analytics, and machine learning. Their GPU performance depends strongly on the in

arXiv:2608.03938v1 Announce Type: new Abstract: Bimanual manipulation policies trained with imitation learning are typically evaluated on workstation or datacenter-class GPUs, leaving the cost of depl

arXiv:2607.27494v1 Announce Type: new Abstract: Recent advances in robotics research have created a strong demand for high-performance simulators. Surgical robotics simulation faces unique challenges

arXiv:2607.28483v1 Announce Type: new Abstract: Real-time anomaly segmentation is essential for the safety of autonomous systems. Although recent approaches offer high accuracy, their computational co

arXiv:2607.26491v1 Announce Type: cross Abstract: The energy consumption of Large Language Model (LLM) serving is becoming a major system challenge as deployment scales, driven by hardware power and t

arXiv:2304.03388v2 Announce Type: replace Abstract: Deep Neural Networks (DNNs) have become ubiquitous for their ability to solve problems across various domains, including computer vision, natural la

arXiv:2607.06922v1 Announce Type: cross Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding t

arXiv:2606.06130v1 Announce Type: new Abstract: As underwater robotics research increasingly addresses complex 3D perception and autonomous navigation, the fidelity of sonar simulation has become a ke

arXiv:2604.01802v2 Announce Type: replace Abstract: Real-time inference of inaccessible interior physical fields from sparse boundary observations is a fundamental but unresolved problem in scientific

arXiv:2605.17898v1 Announce Type: new Abstract: Gaussian process (GP) inference in Python is dominated by libraries such as GPyTorch and GPflow, which are built on deep-learning frameworks and inherit

arXiv:2501.01046v4 Announce Type: replace Abstract: Dataset deduplication is widely recognized as a crucial preprocessing step that enhances data quality and improves the performance of large language

arXiv:2605.11581v1 Announce Type: new Abstract: When large language models (LLMs) serve real-time inference in commercial online advertising systems, end-to-end latency must be strictly bounded to the

arXiv:2605.09370v1 Announce Type: cross Abstract: Large-scale AI training is now fundamentally a distributed systems problem, and hardware failures have become routine operating conditions rather than

arXiv:2605.07662v1 Announce Type: new Abstract: Low-precision number formats are widely used in modern machine learning systems due to their efficiency. Accurate direction representation is key to the

arXiv:2509.05276v4 Announce Type: replace-cross Abstract: Mainstream Transformer-based large language models face major efficiency bottlenecks: training computation scales quadratically with sequence

arXiv:2605.00146v1 Announce Type: new Abstract: Real-time object detection on energy-constrained platforms is critical for applications such as UAV-based inspection, autonomous navigation, and mobile

arXiv:2604.27476v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong applicability in edge industrial applications, yet their deployment remains severely constrained

arXiv:2603.01999v2 Announce Type: replace-cross Abstract: Reliable obstacle avoidance in industrial settings demands 3D scene understanding, but widely used 2D LiDAR sensors perceive only a single hor

arXiv:2603.28342v2 Announce Type: replace Abstract: We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary a

arXiv:2604.19856v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for generating Register-Transfer Level (RTL) code from natural language specifications, but single-shot gene

arXiv:2510.08726v2 Announce Type: replace-cross Abstract: Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and red

arXiv:2510.04008v5 Announce Type: replace Abstract: Softmax Attention has a quadratic time complexity in sequence length, which becomes prohibitive to run at long contexts, even with highly optimized

arXiv:2604.14552v1 Announce Type: cross Abstract: Modern datacenters increasingly rely on low-power, single-slot inference accelerators to balance performance, energy efficiency, and rack density cons

arXiv:2610.16985v1 Announce Type: new Abstract: Vision-language-action (VLA) models show strong potential for end-to-end autonomous driving by leveraging world knowledge and understanding to excel in

arXiv:2610.16147v1 Announce Type: new Abstract: Dynamic sparse training (DST) aims to reduce training costs while maintaining accuracy. Existing DST methods mask out parameters but keep matrix dimensi

arXiv:2610.14856v1 Announce Type: new Abstract: This paper studies the problem of recovering motion from monocular video, even when the target skeleton topology differs from the subject observed in th

arXiv:2610.14106v1 Announce Type: new Abstract: Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically grounded

arXiv:2511.01061v2 Announce Type: replace Abstract: Backpropagation (BP) powers modern deep learning, but it must hold every activation in memory until a global backward sweep consumes it, and that sw

arXiv:2610.18160v1 Announce Type: new Abstract: Robotic harvesting of cherry tomatoes in greenhouses remains challenging due to dense fruit clusters, delicate skin, and the need for gentle yet effecti

arXiv:2610.15781v1 Announce Type: new Abstract: Training-free pruning removes weights from pretrained large language models to reduce their deployment cost. However, existing approaches typically cons

arXiv:2511.14208v3 Announce Type: replace Abstract: Video inverse problems are fundamental to streaming, telepresence, and AR/VR, where high perceptual quality must coexist with tight latency constrai

arXiv:2610.14726v1 Announce Type: new Abstract: Latent diffusion models enable high-quality video generation, but video VAE encoding and decoding remain computationally expensive. Existing methods use

arXiv:2610.18393v1 Announce Type: cross Abstract: Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calcula

arXiv:2610.14326v1 Announce Type: new Abstract: Traffic density estimation from camera feeds requires connecting visual perception with short-horizon forecasting. We present a two-stage pipeline that

arXiv:2610.18417v1 Announce Type: new Abstract: End-to-end deep learning pipelines for camera-radar 3D object detection are well established in driver assistance systems and autonomous vehicles; howev

arXiv:2606.16953v2 Announce Type: replace Abstract: Urban sidewalk navigation presents significant challenges due to complex structural layouts, dynamic pedestrian behaviors, and long distances. While

arXiv:2601.15431v2 Announce Type: replace-cross Abstract: Radiance field-based rendering methods have attracted significant interest from the computer vision and computer graphics communities. They en