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 3951–4000 of 4891 papers

TitleStatusHype
Knowledge Distillation Circumvents Nonlinearity for Optical Convolutional Neural Networks—0
Nested-block self-attention for robust radiotherapy planning segmentation—0
CausalX: Causal Explanations and Block Multilinear Factor Analysis—0
Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics—0
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach—0
FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic flowCode0
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders—0
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
Generative Archimedean CopulasCode0
Computationally Efficient Learning of Statistical ManifoldsCode0
Sequence-based deep learning antibody design for in silico antibody affinity maturation—0
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning—0
The Variational Bayesian Inference for Network Autoregression Models—0
RFI Mitigation for One-bit UWB Radar Systems—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
On the Fundamental Limits of Exact Inference in Structured Prediction—0
Structured Dropout Variational Inference for Bayesian Neural Networks—0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks—0
Learning a Product Relevance Model from Click-Through Data in E-Commerce—0
Tight lower bounds for Dynamic Time WarpingCode0
Cerebral cortical communication overshadows computational energy-use, but these combine to predict synapse number—0
Dynamic Neural Networks: A Survey—0
Fast and Accurate Amplitude Demodulation of Wideband SignalsCode0
Refining a -nearest neighbor graph for a computationally efficient spectral clusteringCode0
An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO—0
A Universal Framework for Featurization of Atomistic SystemsCode0
Learning the Update Operator for 2D/3D Image Registration—0
An efficient optimization based microstructure reconstruction approach with multiple loss functions—0
Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation—0
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark—0
A Multiscale Environment for Learning by DiffusionCode0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Recurrent Localization Networks applied to the Lippmann-Schwinger EquationCode0
Decision Machines: Congruent Decision Trees—0
Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning—0
Data-driven sparse polynomial chaos expansion for models with dependent inputs—0
ZeRO-Offload: Democratizing Billion-Scale Model TrainingCode0
Frequency-weighted H2-optimal model order reduction via oblique projection—0
Disentangled Self-Attentive Neural Networks for Click-Through Rate PredictionCode0
Resolution-Based Distillation for Efficient Histology Image Classification—0
A novel shape matching descriptor for real-time hand gesture recognition—0
Optimizing Over All Sequences of Orthogonal Polynomials—0
Statistically Consistent Saliency Estimation—0
A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation—0
Balancing training time vs. performance with Bayesian Early Pruning—0
Globally Optimal and Efficient Manhattan Frame Estimation by Delimiting Rotation Search Space—0
Rethinking Convolution: Towards an Optimal Efficiency—0
Estimation of Number of Communities in Assortative Sparse Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified