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 201–225 of 4891 papers

TitleStatusHype
Energy-based Preference Optimization for Test-time Adaptation—0
Research on feature fusion and multimodal patent text based on graph attention network—0
Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation—0
One Model to Rank Them All: Unifying Online Advertising with End-to-End Learning—0
Efficient Optimization Accelerator Framework for Multistate Ising Problems—0
CaseEdit: Enhancing Localized Commonsense Reasoning via Null-Space Constrained Knowledge Editing in Small Parameter Language Models—0
Learning Optimal Multimodal Information Bottleneck Representations—0
Long-Context State-Space Video World Models—0
SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point CloudsCode1
Few-Shot Class-Incremental Learning For Efficient SAR Automatic Target Recognition—0
Route to Reason: Adaptive Routing for LLM and Reasoning Strategy SelectionCode0
Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive HypergraphsCode1
Faithful Group Shapley ValueCode0
Fast and Accurate Power Load Data Completion via Regularization-optimized Low-Rank Factorization—0
A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random—0
PosePilot: An Edge-AI Solution for Posture Correction in Physical Exercises—0
PATS: Process-Level Adaptive Thinking Mode SwitchingCode1
LIMOPro: Reasoning Refinement for Efficient and Effective Test-time ScalingCode1
ASPO: Adaptive Sentence-Level Preference Optimization for Fine-Grained Multimodal Reasoning—0
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization—0
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary DomainsCode2
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-TuningCode0
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition—0
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling—0
Partition Generative Modeling: Masked Modeling Without MasksCode4
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified