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 841–850 of 4891 papers

TitleStatusHype
DISC: DISC: Dynamic Decomposition Improves LLM Inference Scaling—0
A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control—0
Pseudo-Measurement Enhancement in Power Distribution Systems—0
Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery—0
Graph Self-Supervised Learning with Learnable Structural and Positional EncodingsCode0
MOB-GCN: A Novel Multiscale Object-Based Graph Neural Network for Hyperspectral Image ClassificationCode0
Dynamic Parallel Tree Search for Efficient LLM Reasoning—0
TabMixer: advancing tabular data analysis with an enhanced MLP-mixer approachCode1
Hard constraint learning approaches with trainable influence functions for evolutionary equationsCode0
Probabilistic Formulations for System Identification of Linear Dynamics with Bilinear Observation Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified