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

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Showing 4101–4110 of 4891 papers

TitleStatusHype
Augmented Sliced Wasserstein DistancesCode1
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence—0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial—0
AlgebraNetsCode0
Combining the band-limited parameterization and Semi-Lagrangian Runge--Kutta integration for efficient PDE-constrained LDDMM—0
Exploring Quality and Generalizability in Parameterized Neural Audio EffectsCode1
Neural Network Activation Quantization with Bitwise Information BottlenecksCode0
GAP++: Learning to generate target-conditioned adversarial examples—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified