Search Results for author:
Found 13 papers, 7 papers with code
Pruning by Active Attention Manipulation
On CIFAR-10 dataset, without requiring a pre-trained baseline network, we obtain 1. 02% and 1. 19% accuracy gain and 52. 3% and 54% parameters reduction, on ResNet56 and ResNet110, respectively.
End-to-End Sensitivity-Based Filter Pruning
Moreover, by training the pruning scores of all layers simultaneously our method can account for layer interdependencies, which is essential to find a performant sparse sub-network.
Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition
We present a novel global compression framework for deep neural networks that automatically analyzes each layer to identify the optimal per-layer compression ratio, while simultaneously achieving the desired overall compression.
Closed-form Continuous-time Neural Models
To this end, we compute a tightly-bounded approximation of the solution of an integral appearing in LTCs' dynamics, that has had no known closed-form solution so far.
Ranked #36 on
Sentiment Analysis
on IMDb
Sparse Flows: Pruning Continuous-depth Models
Our empirical results suggest that pruning improves generalization for neural ODEs in generative modeling.
Low-Regret Active learning
We develop an online learning algorithm for identifying unlabeled data points that are most informative for training (i. e., active learning).
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks.
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space
We demonstrate the effectiveness of our algorithm in learning competitive behaviors on a novel multi-agent racing benchmark that requires planning from image observations.
Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts
LiDAR-based solutions, used in US forests, are accurate, but cost-prohibitive, and hardly-accessible in the Amazon rainforest.
Provable Filter Pruning for Efficient Neural Networks
We present a provable, sampling-based approach for generating compact Convolutional Neural Networks (CNNs) by identifying and removing redundant filters from an over-parameterized network.
SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks
We introduce a pruning algorithm that provably sparsifies the parameters of a trained model in a way that approximately preserves the model's predictive accuracy.
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
We present an efficient coresets-based neural network compression algorithm that sparsifies the parameters of a trained fully-connected neural network in a manner that provably approximates the network's output.
Training Support Vector Machines using Coresets
We present a novel coreset construction algorithm for solving classification tasks using Support Vector Machines (SVMs) in a computationally efficient manner.
