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 3851–3875 of 4891 papers

TitleStatusHype
Joint Network Topology Inference via Structured Fusion Regularization—0
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference—0
Real-World Single Image Super-Resolution: A Brief ReviewCode1
Anharmonic Raman spectra simulation of crystals from deep neural networks—0
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place RecognitionCode1
A Pose-only Solution to Visual Reconstruction and NavigationCode1
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations—0
Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech SeparationCode1
Knowledge Distillation Circumvents Nonlinearity for Optical Convolutional Neural Networks—0
Nested-block self-attention for robust radiotherapy planning segmentation—0
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach—0
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders—0
FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic flowCode0
CausalX: Causal Explanations and Block Multilinear Factor Analysis—0
Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics—0
Anytime Sampling for Autoregressive Models via Ordered AutoencodingCode1
Solving high-dimensional parabolic PDEs using the tensor train formatCode1
Computationally Efficient Learning of Statistical ManifoldsCode0
You Only Compress Once: Optimal Data Compression for Estimating Linear ModelsCode1
Generative Archimedean CopulasCode0
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferenceCode0
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval—0
Sequence-based deep learning antibody design for in silico antibody affinity maturation—0
Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTMCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified