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 2391–2400 of 4891 papers

TitleStatusHype
InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning—0
Deep Neural Network Initialization with Sparsity Inducing Activations—0
Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding BridgeCode1
LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting—0
Fast Adversarial Attacks on Language Models In One GPU MinuteCode2
CFIR: Fast and Effective Long-Text To Image Retrieval for Large CorporaCode0
Efficient State Space Model via Fast Tensor Convolution and Block DiagonalizationCode0
DiffuSolve: Diffusion-based Solver for Non-convex Trajectory Optimization—0
opp/ai: Optimistic Privacy-Preserving AI on Blockchain—0
An FPGA-Based Accelerator Enabling Efficient Support for CNNs with Arbitrary Kernel Sizes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified