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 401–425 of 4891 papers

TitleStatusHype
CosmoPower-JAX: high-dimensional Bayesian inference with differentiable cosmological emulatorsCode1
An empirical evaluation of functional alignment using inter-subject decodingCode1
featsel: A framework for benchmarking of feature selection algorithms and cost functionsCode1
A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spacesCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
Fast Point TransformerCode1
Structured Multi-Track Accompaniment Arrangement via Style Prior ModellingCode1
DASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable LearnersCode1
FastMap: Fast Queries Initialization Based Vectorized HD Map Reconstruction FrameworkCode1
Fast Sequence Based Embedding with Diffusion GraphsCode1
AMD-Hummingbird: Towards an Efficient Text-to-Video ModelCode1
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive ImagingCode1
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference AccelerationCode1
Hyp2Nav: Hyperbolic Planning and Curiosity for Crowd NavigationCode1
FastKV: KV Cache Compression for Fast Long-Context Processing with Token-Selective PropagationCode1
Fast Sequence-Based Embedding with Diffusion GraphsCode1
Improved Techniques for Training Adaptive Deep NetworksCode1
Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorchCode1
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
Improving Facade Parsing with Vision Transformers and Line IntegrationCode1
DCT-SNN: Using DCT to Distribute Spatial Information over Time for Learning Low-Latency Spiking Neural NetworksCode1
Improving Temporal Link Prediction via Temporal Walk Matrix ProjectionCode1
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent CodingCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
Adapting LLaMA Decoder to Vision TransformerCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified