SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

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Showing 1271–1280 of 4891 papers

TitleStatusHype
Robust Channel Estimation for Optical Wireless Communications Using Neural NetworkCode0
Overcoming Vocabulary Constraints with Pixel-level Fallback—0
3D Gaussian Inverse Rendering with Approximated Global Illumination—0
FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning—0
CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection—0
An Explainable Reconfiguration-Based Optimization Algorithm for Industrial and Reliability-Redundancy Allocation Problems—0
LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi—0
Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates—0
ToolACE-R: Tool Learning with Adaptive Self-Refinement—0
Revisiting Funnel Transformers for Modern LLM Architectures with Comprehensive Ablations in Training and Inference Configurations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified