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Found 63 papers, 26 papers with code
Detecting Neighborhood Gentrification at Scale via Street-level Visual Data
Neighborhood gentrification plays a significant role in shaping the social and economic well-being of both individuals and communities at large.
Improving debris flow evacuation alerts in Taiwan using machine learning
Towards improving this system, we implemented five machine learning models that input historical rainfall data and predict whether a debris flow will occur within a selected time.
METER-ML: A Multi-Sensor Earth Observation Benchmark for Automated Methane Source Mapping
Reducing methane emissions is essential for mitigating global warming.
Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia
We develop an evaluation framework inspired by the early classification literature, in order to quantify the tradeoff between diagnostic performance and inference time for sparse analytic approaches.
Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management
Recent advances in Natural Language Processing (NLP), and specifically automated Question Answering (QA) systems, have demonstrated both impressive linguistic fluency and a pernicious tendency to reflect social biases.
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports
We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14, 579 entities and 10, 889 relations), and a test dataset, which contains two independent sets of board-certified radiologist annotations for 100 radiology reports split equally across the MIMIC-CXR and CheXpert datasets.
Learning Neighborhood Representation from Multi-Modal Multi-Graph: Image, Text, Mobility Graph and Beyond
Recent urbanization has coincided with the enrichment of geotagged data, such as street view and point-of-interest (POI).
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations
We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram.
Effect of Radiology Report Labeler Quality on Deep Learning Models for Chest X-Ray Interpretation
Next, after training image classification models using labels generated from the different radiology report labelers on one of the largest datasets of chest X-rays, we show that an image classification model trained on labels from the VisualCheXbert labeler outperforms image classification models trained on labels from the CheXpert and CheXbert labelers.
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources.
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes.
CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays
Second, we evaluate whether models trained on seen diseases can detect seen diseases when co-occurring with diseases outside the subset (unseen diseases).
VisualCheXbert: Addressing the Discrepancy Between Radiology Report Labels and Image Labels
We also find that VisualCheXbert better agrees with radiologists labeling chest X-ray images than do radiologists labeling the corresponding radiology reports by an average F1 score across several medical conditions of between 0. 12 (95% CI 0. 09, 0. 15) and 0. 21 (95% CI 0. 18, 0. 24).
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation
Our controlled experiments show that the keys to improving downstream performance on disease classification are (1) using patient metadata to appropriately create positive pairs from different images with the same underlying pathologies, and (2) maximizing the number of different images used in query pairing.
CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation
Medical image segmentation models are typically supervised by expert annotations at the pixel-level, which can be expensive to acquire.
CheXternal: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays and External Clinical Settings
Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise.
CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation
First, we find no relationship between ImageNet performance and CheXpert performance for both models without pretraining and models with pretraining.
MoCo-Pretraining Improves Representations and Transferability of Chest X-ray Models
Using 0. 1% of labeled training data, we find that a linear model trained on MoCo-pretrained representations outperforms one trained on representations without MoCo-pretraining by an AUC of 0. 096 (95% CI 0. 061, 0. 130), indicating that MoCo-pretrained representations are of higher quality.
OGNet: Towards a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery
In this work, we develop deep learning algorithms that leverage freely available high-resolution aerial imagery to automatically detect oil and gas infrastructure, one of the largest contributors to global methane emissions.
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
In this study, we measured the diagnostic performance for 8 different chest x-ray models when applied to photos of chest x-rays.
ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery
Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies.
GloFlow: Global Image Alignment for Creation of Whole Slide Images for Pathology from Video
The application of deep learning to pathology assumes the existence of digital whole slide images of pathology slides.
MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models
In this work, we propose MoCo-CXR, which is an adaptation of the contrastive learning method Momentum Contrast (MoCo), to produce models with better representations and initializations for the detection of pathologies in chest X-rays.
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid.
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set
We used a deep learning model to segment all tumor nuclei in the ROIs, and computed several geometric features for each segmented nucleus.
CheXphoto: 10,000+ Photos and Transformations of Chest X-rays for Benchmarking Deep Learning Robustness
We introduce CheXphoto, a dataset of smartphone photos and synthetic photographic transformations of chest x-rays sampled from the CheXpert dataset.
Evaluating the Disentanglement of Deep Generative Models through Manifold Topology
Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models.
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
The extraction of labels from radiology text reports enables large-scale training of medical imaging models.
CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
First, we find that the top 10 chest x-ray models on the CheXpert competition achieve an average AUC of 0. 851 on the task of detecting TB on two public TB datasets without fine-tuning or including the TB labels in training data.
Data augmentation with Mobius transformations
Data augmentation has led to substantial improvements in the performance and generalization of deep models, and remain a highly adaptable method to evolving model architectures and varying amounts of data---in particular, extremely scarce amounts of available training data.
NGBoost: Natural Gradient Boosting for Probabilistic Prediction
NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies.
Ranked #93 on
Multi-Label Classification
on CheXpert
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes.
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries.
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies.
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.
Ranked #3 on
Pneumonia Detection
on ChestX-ray14
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor.
Data Noising as Smoothing in Neural Network Language Models
Data noising is an effective technique for regularizing neural network models.
Neural Language Correction with Character-Based Attention
Motivated by these issues, we present a neural network-based approach to language correction.
An Empirical Evaluation of Deep Learning on Highway Driving
We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection.
Ranked #2 on
Lane Detection
on Caltech Lanes Cordova
Deep Speech: Scaling up end-to-end speech recognition
We present a state-of-the-art speech recognition system developed using end-to-end deep learning.
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs
This approach to decoding enables first-pass speech recognition with a language model, completely unaided by the cumbersome infrastructure of HMM-based systems.
Building DNN Acoustic Models for Large Vocabulary Speech Recognition
We compare standard DNNs to convolutional networks, and present the first experiments using locally-connected, untied neural networks for acoustic modeling.
Ranked #11 on
Speech Recognition
on swb_hub_500 WER fullSWBCH
Grounded Compositional Semantics for Finding and Describing Images with Sentences
Previous work on Recursive Neural Networks (RNNs) shows that these models can produce compositional feature vectors for accurately representing and classifying sentences or images.
Zero-Shot Learning Through Cross-Modal Transfer
This work introduces a model that can recognize objects in images even if no training data is available for the objects.
Large Scale Distributed Deep Networks
Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance.
Emergence of Object-Selective Features in Unsupervised Feature Learning
Recent work in unsupervised feature learning has focused on the goal of discovering high-level features from unlabeled images.
Building high-level features using large scale unsupervised learning
For example, is it possible to learn a face detector using only unlabeled images?
Sparse Filtering
Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, video and audio classification.
Selecting Receptive Fields in Deep Networks
Recent deep learning and unsupervised feature learning systems that learn from unlabeled data have achieved high performance in benchmarks by using extremely large architectures with many features (hidden units) at each layer.
Unsupervised learning models of primary cortical receptive fields and receptive field plasticity
In this work we focus on that component of adaptation which occurs during an organism's lifetime, and show that a number of unsupervised feature learning algorithms can account for features of normal receptive field properties across multiple primary sensory cortices.
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning
We show that the soft reconstruction cost can also be used to prevent replicated features in tiled convolutional neural networks.
Ranked #116 on
Image Classification
on STL-10
Learning Word Vectors for Sentiment Analysis
We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term–document information as well as rich sentiment content.
Energy Disaggregation via Discriminative Sparse Coding
Energy disaggregation is the task of taking a whole-home energy signal and separating it into its component appliances.
Tiled convolutional neural networks
Using convolutional (tied) weights significantly reduces the number of parameters that have to be learned, and also allows translational invariance to be hard-coded into the architecture.
Unsupervised feature learning for audio classification using convolutional deep belief networks
In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks.
Measuring Invariances in Deep Networks
Our evaluation metrics can also be used to evaluate future work in unsupervised deep learning, and thus help the development of future algorithms.
Efficient multiple hyperparameter learning for log-linear models
Using multiple regularization hyperparameters is an effective method for managing model complexity in problems where input features have varying amounts of noise.
Sparse deep belief net model for visual area V2
This suggests that our sparse variant of deep belief networks holds promise for modeling more higher-order features.
Latent Dirichlet Allocation
Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities.
