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Found 8 papers, 3 papers with code
Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer
These models leverage the attention mechanism to capture long- and short-range dependencies in the sequence.
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy
A fundamental challenge in Bayesian inference is efficient representation of a target distribution.
Low-Rank+Sparse Tensor Compression for Neural Networks
Low-rank tensor compression has been proposed as a promising approach to reduce the memory and compute requirements of neural networks for their deployment on edge devices.
Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation
Consistency training is a popular method to improve deep learning models in computer vision and natural language processing.
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration
This paper emphasizes the importance and efficacy of training, quantization and accelerator design, and calls for more research breakthroughs in the area for AI on the edge.
Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determination
This paper presents a novel end-to-end framework for low-rank tensorized training of neural networks.
Bayesian Tensorized Neural Networks with Automatic Rank Selection
Tensor decomposition is an effective approach to compress over-parameterized neural networks and to enable their deployment on resource-constrained hardware platforms.
Variational Bayesian Inference for Robust Streaming Tensor Factorization and Completion
Streaming tensor factorization is a powerful tool for processing high-volume and multi-way temporal data in Internet networks, recommender systems and image/video data analysis.
