Harvard University

∙ Cambridge , United States ∙ 0 followers

Harvard University is a private Ivy League research university in Cambridge, Massachusetts, with about 6,700 undergraduate students and about 15,250 postgraduate students.

Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks

Dynamic computed tomography perfusion (CTP) imaging is a promising appro...
05/19/2020 ∙ by Dufan Wu, et al. ∙ 34 ∙ share

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Cascaded Text Generation with Markov Transformers

The two dominant approaches to neural text generation are fully autoregr...
06/01/2020 ∙ by Yuntian Deng, et al. ∙ 31 ∙ share

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Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

Learning to predict scene depth from RGB inputs is a challenging task bo...
11/15/2018 ∙ by Vincent Casser, et al. ∙ 30 ∙ share

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Deep learning tools for the measurement of animal behavior in neuroscience

Recent advances in computer vision have made accurate, fast and robust m...
09/30/2019 ∙ by Mackenzie W. Mathis, et al. ∙ 28 ∙ share

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Unsupervised Discovery of Sparse Multimodal Representations in High Dimensional Data

Extracting an understanding of the underlying system from high dimension...
10/13/2019 ∙ by Samuel Melton, et al. ∙ 26 ∙ share

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Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

AI Safety is a major concern in many deep learning applications such as ...
11/18/2019 ∙ by Tong Che, et al. ∙ 26 ∙ share

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Designing Environments Conducive to Interpretable Robot Behavior

Designing robots capable of generating interpretable behavior is a prere...
07/02/2020 ∙ by Anagha Kulkarni, et al. ∙ 21 ∙ share

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Towards generative adversarial networks as a new paradigm for radiology education

Medical students and radiology trainees typically view thousands of imag...
12/04/2018 ∙ by Samuel G. Finlayson, et al. ∙ 20 ∙ share

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Subgraph Neural Networks

Deep learning methods for graphs achieve remarkable performance on many ...
06/18/2020 ∙ by Emily Alsentzer, et al. ∙ 20 ∙ share

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Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

The rapidly emerging field of computational pathology has the potential ...
04/20/2020 ∙ by Ming Y. Lu, et al. ∙ 19 ∙ share

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Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting

Unsupervised domain adaptation (UDA) for nuclei instance segmentation is...
05/05/2020 ∙ by Dongnan Liu, et al. ∙ 19 ∙ share

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Learning to Complement Humans

A rising vision for AI in the open world centers on the development of s...
05/01/2020 ∙ by Bryan Wilder, et al. ∙ 19 ∙ share

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Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis

Cancer diagnosis, prognosis and therapeutic response predictions are bas...
12/18/2019 ∙ by Richard J. Chen, et al. ∙ 18 ∙ share

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Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation

Fully convolutional neural networks (FCNs), and in particular U-Nets, ha...
11/29/2019 ∙ by Alireza Mehrtash, et al. ∙ 17 ∙ share

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Neuroscience-inspired online unsupervised learning algorithms

Although the currently popular deep learning networks achieve unpreceden...
08/05/2019 ∙ by Cengiz Pehlevan, et al. ∙ 16 ∙ share

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Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks

Solutions to differential equations are of significant scientific and en...
07/21/2020 ∙ by Dylan Randle, et al. ∙ 16 ∙ share

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A Topological Nomenclature for 3D Shape Analysis in Connectomics

An essential task in nano-scale connectomics is the morphology analysis ...
09/27/2019 ∙ by Abhimanyu Talwar, et al. ∙ 15 ∙ share

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Emergent Properties of Foveated Perceptual Systems

We introduce foveated perceptual systems, inspired by human biological s...
06/14/2020 ∙ by Arturo Deza, et al. ∙ 15 ∙ share

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Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images

Supervised deep learning algorithms have enabled significant performance...
12/19/2018 ∙ by Bruno Lecouat, et al. ∙ 14 ∙ share

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Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, ...
09/25/2019 ∙ by Ayush Singh, et al. ∙ 13 ∙ share

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Deep Double Descent: Where Bigger Models and More Data Hurt

We show that a variety of modern deep learning tasks exhibit a "double-d...
12/04/2019 ∙ by Preetum Nakkiran, et al. ∙ 13 ∙ share

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Learning to Prune: Speeding up Repeated Computations

It is common to encounter situations where one must solve a sequence of ...
04/26/2019 ∙ by Daniel Alabi, et al. ∙ 12 ∙ share

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A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

Fetal cortical plate segmentation is essential in quantitative analysis ...
04/27/2020 ∙ by Haoran Dou, et al. ∙ 12 ∙ share

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Too many cooks: Coordinating multi-agent collaboration through inverse planning

Collaboration requires agents to coordinate their behavior on the fly, s...
03/26/2020 ∙ by Rose E. Wang, et al. ∙ 11 ∙ share

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Collapsing Bandits and Their Application to Public Health Interventions

We propose and study Collpasing Bandits, a new restless multi-armed band...
07/05/2020 ∙ by Aditya Mate, et al. ∙ 11 ∙ share

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Computationally Efficient Cascaded Training for Deep Unrolled Network in CT Imaging

Dose reduction in computed tomography (CT) has been of great research in...
10/05/2018 ∙ by Dufan Wu, et al. ∙ 10 ∙ share

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Thwarting Adversarial Examples: An L_0-RobustSparse Fourier Transform

We give a new algorithm for approximating the Discrete Fourier transform...
12/12/2018 ∙ by Mitali Bafna, et al. ∙ 10 ∙ share

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A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging

Multi-compartment modeling of diffusion-weighted magnetic resonance imag...
06/19/2020 ∙ by Davood Karimi, et al. ∙ 10 ∙ share

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Fast, Structured Clinical Documentation via Contextual Autocomplete

We present a system that uses a learned autocompletion mechanism to faci...
07/29/2020 ∙ by Divya Gopinath, et al. ∙ 9 ∙ share

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Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?

Commercial iterative reconstruction techniques on modern CT scanners tar...
11/08/2018 ∙ by Hongming Shan, et al. ∙ 8 ∙ share

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Seeing in the dark with recurrent convolutional neural networks

Classical convolutional neural networks (cCNNs) are very good at categor...
11/21/2018 ∙ by Till S. Hartmann, et al. ∙ 8 ∙ share

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Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media

In the context of fake news, bias, and propaganda, we study two importan...
04/01/2019 ∙ by Ramy Baly, et al. ∙ 8 ∙ share

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ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries

In this work we introduce ExpertMatcher, a method for automating deep le...
10/05/2019 ∙ by Vivek Sharma, et al. ∙ 8 ∙ share

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A Learning Strategy for Contrast-agnostic MRI Segmentation

We present a deep learning strategy that enables, for the first time, co...
03/04/2020 ∙ by Benjamin Billot, et al. ∙ 8 ∙ share

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Online Residential Demand Response via Contextual Multi-Armed Bandits

Residential load demands have huge potential to be exploited to enhance ...
03/07/2020 ∙ by Xin Chen, et al. ∙ 8 ∙ share

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Bandit-PAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits

Clustering is a ubiquitous task in data science. Compared to the commonl...
06/11/2020 ∙ by Mo Tiwari, et al. ∙ 8 ∙ share

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Fast Physical Activity Suggestions: Efficient Hyperparameter Learning in Mobile Health

Users can be supported to adopt healthy behaviors, such as regular physi...
12/21/2020 ∙ by Marianne Menictas, et al. ∙ 8 ∙ share

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Smarter Parking: Using AI to Identify Parking Inefficiencies in Vancouver

On-street parking is convenient, but has many disadvantages: on-street s...
03/21/2020 ∙ by Devon Graham, et al. ∙ 7 ∙ share

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Assessing the validity of saliency maps for abnormality localization in medical imaging

Saliency maps have become a widely used method to assess which areas of ...
05/29/2020 ∙ by Nishanth Thumbavanam Arun, et al. ∙ 7 ∙ share

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Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation

Image segmentation is a fundamental vision task and a crucial step for m...
05/30/2020 ∙ by Jianqiang Wan, et al. ∙ 7 ∙ share

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Self-Organization and Artificial Life

Self-organization can be broadly defined as the ability of a system to d...
03/14/2019 ∙ by Carlos Gershenson, et al. ∙ 6 ∙ share

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Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures

Generative models have achieved impressive results in many domains inclu...
09/03/2019 ∙ by Jordan Hoffmann, et al. ∙ 6 ∙ share

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Generative-based Airway and Vessel Morphology Quantification on Chest CT Images

Accurately and precisely characterizing the morphology of small pulmonar...
02/13/2020 ∙ by Pietro Nardelli, et al. ∙ 6 ∙ share

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A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

We present a timely and novel methodology that combines disease estimate...
04/08/2020 ∙ by Dianbo Liu, et al. ∙ 6 ∙ share

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A Large Dataset of Historical Japanese Documents with Complex Layouts

Deep learning-based approaches for automatic document layout analysis an...
04/18/2020 ∙ by Zejiang Shen, et al. ∙ 6 ∙ share

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CheckNet: Secure Inference on Untrusted Devices

We introduce CheckNet, a method for secure inference with deep neural ne...
06/17/2019 ∙ by Marcus Comiter, et al. ∙ 5 ∙ share

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Generalized Principal Component Analysis

Generalized principal component analysis (GLM-PCA) facilitates dimension...
07/03/2019 ∙ by F. William Townes, et al. ∙ 5 ∙ share

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High Accuracy Tumor Diagnoses and Benchmarking of Hematoxylin and Eosin Stained Prostate Core Biopsy Images Generated by Explainable Deep Neural Networks

Histopathological diagnoses of tumors in tissue biopsy after Hematoxylin...
08/02/2019 ∙ by Aman Rana, et al. ∙ 5 ∙ share

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InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

This paper studies learning the representations of whole graphs in both ...
07/31/2019 ∙ by Fan-Yun Sun, et al. ∙ 5 ∙ share

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Exploiting Parallelism Opportunities with Deep Learning Frameworks

State-of-the-art machine learning frameworks support a wide variety of d...
08/13/2019 ∙ by Yu Emma Wang, et al. ∙ 5 ∙ share

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