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 3471–3480 of 4891 papers

TitleStatusHype
Neural network-based CUSUM for online change-point detection—0
QuaLA-MiniLM: a Quantized Length Adaptive MiniLM—0
Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning—0
Collaborative Honeypot Defense in UAV Networks: A Learning-Based Game Approach—0
BEBERT: Efficient and Robust Binary Ensemble BERTCode0
Joint Semantic Transfer Network for IoT Intrusion Detection—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
Analyzing Deep Learning Representations of Point Clouds for Real-Time In-Vehicle LiDAR Perception—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified