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 3726–3750 of 4891 papers

TitleStatusHype
Adaptive wavelet distillation from neural networks through interpretationsCode1
Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders—0
Solving Large-Scale Multi-Objective Optimization via Probabilistic Prediction Model—0
Transformer-based Machine Learning for Fast SAT Solvers and Logic SynthesisCode1
Continuous vs. Discrete Optimization of Deep Neural NetworksCode0
Accelerating Spherical k-Means—0
On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation—0
Training Adaptive Computation for Open-Domain Question Answering with Computational ConstraintsCode1
Certifiably Robust Interpretation via Renyi Differential Privacy—0
Learned Global Optimization for Inverse Scattering Problems -- Matching Global Search with Computational Efficiency—0
Dispatchable Region for Active Distribution Networks Using Approximate Second-Order Cone Relaxation—0
Computationally efficient spatial rendering of late reverberation in virtual acoustic environments—0
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement—0
Domain Adaptation Broad Learning System Based on Locally Linear Embedding—0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
Efficient Tensor Contraction via Fast Count Sketch—0
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and RetrievalCode1
Harmonic Power-Flow Study of Polyphase Grids with Converter-Interfaced Distributed Energy Resources, Part I: Modelling Framework and Algorithm—0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
Boundary Graph Neural Networks for 3D SimulationsCode0
Algorithm Unrolling for Massive Access via Deep Neural Network with Theoretical Guarantee—0
Training or Architecture? How to Incorporate Invariance in Neural Networks—0
Precise phase retrieval for propagation-based images using discrete mathematics—0
Pre-Trained Models: Past, Present and Future—0
Quantum Speedup of Natural Gradient for Variational Bayes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified