Search Results for author:
Found 9 papers, 1 papers with code
POP: Prompt Of Prompts for Continual Learning
We show that a foundation model equipped with POP learning is able to outperform classic CL methods by a significant margin.
MobileInst: Video Instance Segmentation on the Mobile
To address those issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices.
Dense Network Expansion for Class Incremental Learning
This is accomplished by the introduction of dense connections between the intermediate layers of the task expert networks, that enable the transfer of knowledge from old to new tasks via feature sharing and reusing.
A Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting
We show an effective three-stage procedure to balance accuracy and sparsity in network training.
Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
We prove that if the STE is properly chosen, the expected coarse gradient correlates positively with the population gradient (not available for the training), and its negation is a descent direction for minimizing the population loss.
AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks
In addition, we found experimentally that the standard convex relaxation of permutation matrices into stochastic matrices leads to poor performance.
Median Binary-Connect Method and a Binary Convolutional Neural Nework for Word Recognition
We propose and study a new projection formula for training binary weight convolutional neural networks.
Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks
We introduce the notion of coarse gradient and propose the blended coarse gradient descent (BCGD) algorithm, for training fully quantized neural networks.
BinaryRelax: A Relaxation Approach For Training Deep Neural Networks With Quantized Weights
We propose BinaryRelax, a simple two-phase algorithm, for training deep neural networks with quantized weights.
