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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3251–3260 of 4891 papers

TitleStatusHype
AI Safety in Practice: Enhancing Adversarial Robustness in Multimodal Image Captioning—0
SHA-CNN: Scalable Hierarchical Aware Convolutional Neural Network for Edge AI—0
High Performance Im2win and Direct Convolutions using Three Tensor Layouts on SIMD Architectures—0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification—0
Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent—0
NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equities—0
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics—0
Real-time Hybrid System Identification with Online Deterministic Annealing—0
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified