Search Results for author:
Found 16 papers, 13 papers with code
CARNA: Characterizing Advanced heart failure Risk and hemodyNAmic phenotypes using learned multi-valued decision diagrams
To address these limitations, this paper presents CARNA, a hemodynamic risk stratification and phenotyping framework for advanced HF that takes advantage of the explainability and expressivity of machine learned Multi-Valued Decision Diagrams (MVDDs).
Dissimilar Nodes Improve Graph Active Learning
The existing Active Graph Embedding framework proposes to use centrality score, density score, and entropy score to evaluate the value of unlabeled nodes, and it has been shown to be capable of bringing some improvement to the node classification tasks of Graph Convolutional Networks.
Rethinking "Batch" in BatchNorm
BatchNorm is a critical building block in modern convolutional neural networks.
PointRend: Image Segmentation as Rendering
We present a new method for efficient high-quality image segmentation of objects and scenes.
Ranked #3 on
Instance Segmentation
on COCO 2017 val
Momentum Contrast for Unsupervised Visual Representation Learning
This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning.
Ranked #11 on
Contrastive Learning
on imagenet-1k
Bayesian Relational Memory for Semantic Visual Navigation
We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards.
Feature Denoising for Improving Adversarial Robustness
This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks.
Ranked #1 on
Adversarial Defense
on CAAD 2018
Natural Environment Benchmarks for Reinforcement Learning
While current benchmark reinforcement learning (RL) tasks have been useful to drive progress in the field, they are in many ways poor substitutes for learning with real-world data.
Learning and Planning with a Semantic Model
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI.
Group Normalization
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Ranked #140 on
Object Detection
on COCO minival
Building Generalizable Agents with a Realistic and Rich 3D Environment
To generalize to unseen environments, an agent needs to be robust to low-level variations (e. g. color, texture, object changes), and also high-level variations (e. g. layout changes of the environment).
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment.
Effective Quantization Methods for Recurrent Neural Networks
In addition, we propose balanced quantization methods for weights to further reduce performance degradation.
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients.
Exploiting Local Structures with the Kronecker Layer in Convolutional Networks
In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks.
Multilinear Map Layer: Prediction Regularization by Structural Constraint
In this paper we propose and study a technique to impose structural constraints on the output of a neural network, which can reduce amount of computation and number of parameters besides improving prediction accuracy when the output is known to approximately conform to the low-rankness prior.
