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Efficient Computing

596 directly classified papers

Papers per year

Papers

TFG: Unified Training-Free Guidance for Diffusion Models NIPS 2024

SnapKV: LLM Knows What You are Looking for Before Generation NIPS 2024

SIRIUS : Contexual Sparisty with Correction for Efficient LLMs NIPS 2024

Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators NIPS 2024

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML NIPS 2024

Provable Tempered Overfitting of Minimal Nets and Typical Nets NIPS 2024

FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification NIPS 2024

QTIP: Quantization with Trellises and Incoherence Processing NIPS 2024

BOLD: Boolean Logic Deep Learning NIPS 2024

SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximation NIPS 2024

Efficient Large Multi-modal Models via Visual Context Compression NIPS 2024

Efficient Lifelong Model Evaluation in an Era of Rapid Progress NIPS 2024

Structured Unrestricted-Rank Matrices for Parameter Efficient Finetuning NIPS 2024

Mixture of In-Context Experts Enhance LLMs' Long Context Awareness NIPS 2024

Mesa-Extrapolation: A Weave Position Encoding Method for Enhanced Extrapolation in LLMs NIPS 2024

Towards training digitally-tied analog blocks via hybrid gradient computation NIPS 2024

NN4SysBench: Characterizing Neural Network Verification for Computer Systems NIPS 2024

Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers NIPS 2024

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent NIPS 2024

Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models NIPS 2024

Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation AAAI 2024

LION: Implicit Vision Prompt Tuning AAAI 2024

Preparing Lessons for Progressive Training on Language Models AAAI 2024

The Inhibitor: ReLU and Addition-Based Attention for Efficient Transformers (Student Abstract) AAAI 2024

An Empirical Study of Distributed Deep Learning Training on Edge (Student Abstract) AAAI 2024