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Efficient Computing

1253 directly classified papers

Papers per year

Papers

Entropy-Driven Mixed-Precision Quantization for Deep Network Design NIPS 2022
An In-depth Study of Stochastic Backpropagation NIPS 2022
TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training NIPS 2022
On-Device Training Under 256KB Memory NIPS 2022
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost NIPS 2022
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction NIPS 2022
Improved Utility Analysis of Private CountSketch NIPS 2022
TREC: Transient Redundancy Elimination-based Convolution NIPS 2022
Is Integer Arithmetic Enough for Deep Learning Training? NIPS 2022
NAS-Bench-Suite-Zero: Accelerating Research on Zero Cost Proxies NIPS 2022
M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design NIPS 2022
Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models NIPS 2022
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models NIPS 2022
An empirical analysis of compute-optimal large language model training NIPS 2022
A Theoretical View on Sparsely Activated Networks NIPS 2022
Matryoshka Representation Learning NIPS 2022
Accelerating Sparse Convolution with Column Vector-Wise Sparsity NIPS 2022
DropCov: A Simple yet Effective Method for Improving Deep Architectures NIPS 2022
Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer NIPS 2022
Scalable Interpretability via Polynomials NIPS 2022
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints NIPS 2022
Latency-aware Spatial-wise Dynamic Networks NIPS 2022
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks NIPS 2022
Rethinking Resolution in the Context of Efficient Video Recognition NIPS 2022
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy NIPS 2022