research
∙
08/26/2023
Semi-Supervised Semantic Segmentation via Marginal Contextual Information
We present a novel confidence refinement scheme that enhances pseudo-lab...
share
research
∙
05/09/2023
StarCoder: may the source be with you!
The BigCode community, an open-scientific collaboration working on the r...
research
∙
06/13/2022
GoToNet: Fast Monocular Scene Exposure and Exploration
Autonomous scene exposure and exploration, especially in localization or...
research
∙
01/30/2022
On Recoverability of Graph Neural Network Representations
Despite their growing popularity, graph neural networks (GNNs) still hav...
research
∙
11/29/2021
End-to-End Referring Video Object Segmentation with Multimodal Transformers
The referring video object segmentation task (RVOS) involves segmentatio...
research
∙
03/25/2021
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
The success of learning with noisy labels (LNL) methods relies heavily o...
research
∙
11/06/2020
Single-Node Attack for Fooling Graph Neural Networks
Graph neural networks (GNNs) have shown broad applicability in a variety...
research
∙
08/24/2020
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
Unsupervised learning has always been appealing to machine learning rese...
research
∙
04/19/2020
HCM: Hardware-Aware Complexity Metric for Neural Network Architectures
Convolutional Neural Networks (CNNs) have become common in many fields i...
research
∙
03/04/2020
Colored Noise Injection for Training Adversarially Robust Neural Networks
Even though deep learning have shown unmatched performance on various ta...
research
∙
11/17/2019
Smoothed Inference for Adversarially-Trained Models
Deep neural networks are known to be vulnerable to inputs with malicious...
research
∙
11/17/2019
Loss Aware Post-training Quantization
Neural network quantization enables the deployment of large models on re...
research
∙
09/25/2019
CAT: Compression-Aware Training for bandwidth reduction
Convolutional neural networks (CNNs) have become the dominant neural net...
research
∙
05/26/2019
Feature Map Transform Coding for Energy-Efficient CNN Inference
Convolutional neural networks (CNNs) achieve state-of-the-art accuracy i...
research
∙
04/22/2019
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks
Recently, deep learning has become a de facto standard in machine learni...
research
∙
09/29/2018
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Convolutional Neural Networks (CNN) are very popular in many fields incl...
research
∙
04/29/2018
UNIQ: Uniform Noise Injection for non-uniform Quantization of neural networks
We present a novel method for training a neural network amenable to infe...
research
∙
04/29/2018
UNIQ: Uniform Noise Injection for the Quantization of Neural Networks
We present a novel method for training deep neural network amenable to i...
research
∙
07/31/2017