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 3051–3075 of 4891 papers

TitleStatusHype
Towards Human-Like Driving: Active Inference in Autonomous Vehicle Control—0
Towards human-level performance on automatic pose estimation of infant spontaneous movements—0
Towards Lightweight and Stable Zero-shot TTS with Self-distilled Representation Disentanglement—0
Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents—0
Towards quantum enhanced adversarial robustness in machine learning—0
Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data—0
Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation—0
TPDiff: Temporal Pyramid Video Diffusion Model—0
TpopT: Efficient Trainable Template Optimization on Low-Dimensional Manifolds—0
TQ-DiT: Efficient Time-Aware Quantization for Diffusion Transformers—0
Tracking objects using 3D object proposals—0
Tractable Identification of Electric Distribution Networks—0
TRADI: Tracking deep neural network weight distributions for uncertainty estimation—0
TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training—0
Training and inference of large language models using 8-bit floating point—0
Training Binary Weight Networks via Semi-Binary Decomposition—0
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding—0
Neural network-based CUSUM for online change-point detection—0
Towards Reusable Network Components by Learning Compatible Representations—0
Training or Architecture? How to Incorporate Invariance in Neural Networks—0
Training Sparse Neural Network by Constraining Synaptic Weight on Unit Lp Sphere—0
Training Structured Efficient Convolutional Layers—0
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability—0
Constrained Trajectory Optimization on Matrix Lie Groups via Lie-Algebraic Differential Dynamic Programming—0
Trajectory PHD and CPHD Filters with Unknown Detection Profile—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified