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 601–610 of 4891 papers

TitleStatusHype
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)Code1
Exploring _0 Sparsification for Inference-free Sparse RetrieversCode1
ExpoMamba: Exploiting Frequency SSM Blocks for Efficient and Effective Image EnhancementCode1
EXTENDING CONDITIONAL CONVOLUTION STRUCTURES FOR ENHANCING MULTITASKING CONTINUAL LEARNINGCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
FADRM: Fast and Accurate Data Residual Matching for Dataset DistillationCode1
DeepOPF-V: Solving AC-OPF Problems EfficientlyCode1
Delving into Masked Autoencoders for Multi-Label Thorax Disease ClassificationCode1
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified