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 1–25 of 4891 papers

TitleStatusHype
LivePortrait: Efficient Portrait Animation with Stitching and Retargeting ControlCode11
TinyLlama: An Open-Source Small Language ModelCode11
Enhancing Fourier Neural Operators with Local Spatial FeaturesCode7
Muon is Scalable for LLM TrainingCode7
Revisiting PCA for time series reduction in temporal dimensionCode7
PromptWizard: Task-Aware Prompt Optimization FrameworkCode7
VMamba: Visual State Space ModelCode7
Mamba: Linear-Time Sequence Modeling with Selective State SpacesCode6
U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image SegmentationCode6
RWKV: Reinventing RNNs for the Transformer EraCode6
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual PerceptionCode5
Continuous Thought MachinesCode5
Comet: Fine-grained Computation-communication Overlapping for Mixture-of-ExpertsCode5
FireRedASR: Open-Source Industrial-Grade Mandarin Speech Recognition Models from Encoder-Decoder to LLM IntegrationCode5
Video Depth Anything: Consistent Depth Estimation for Super-Long VideosCode5
Exploring GLU Expansion Ratios: A Study of Structured Pruning in LLaMA-3.2 ModelsCode5
MambaIRv2: Attentive State Space RestorationCode5
CogView3: Finer and Faster Text-to-Image Generation via Relay DiffusionCode5
Partition Generative Modeling: Masked Modeling Without MasksCode4
High-performance training and inference for deep equivariant interatomic potentialsCode4
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of ExpertsCode4
On the limits of agency in agent-based modelsCode4
T-MAC: CPU Renaissance via Table Lookup for Low-Bit LLM Deployment on EdgeCode4
RaDe-GS: Rasterizing Depth in Gaussian SplattingCode4
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed TomographyCode4
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified