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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Small in-distribution changes in 3D perspective and lighting fool both CNNs and Transformers

Neural networks are susceptible to small transformations including 2D ro...
06/30/2021 ∙ by Spandan Madan, et al. ∙ 32 ∙ 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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Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare

In many public health settings, it is important for patients to adhere t...
05/17/2021 ∙ by Arpita Biswas, et al. ∙ 23 ∙ 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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Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior

Direct reconstruction methods have been developed to estimate parametric...
06/18/2021 ∙ by Kuang Gong, 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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Towards a Unified Framework for Fair and Stable Graph Representation Learning

As the representations output by Graph Neural Networks (GNNs) are increa...
02/25/2021 ∙ by Chirag Agarwal, et al. ∙ 16 ∙ share

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Gaussian Process Convolutional Dictionary Learning

Convolutional dictionary learning (CDL), the problem of estimating shift...
03/28/2021 ∙ by Andrew H. Song, 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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An Empirical Framework for Domain Generalization in Clinical Settings

Clinical machine learning models experience significantly degraded perfo...
03/20/2021 ∙ by Haoran Zhang, 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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TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning

Dynamic graph modeling has recently attracted much attention due to its ...
05/17/2021 ∙ by Lu Wang, 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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What can human minimal videos tell us about dynamic recognition models?

In human vision objects and their parts can be visually recognized from ...
04/19/2021 ∙ by Guy Ben-Yosef, et al. ∙ 10 ∙ share

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Statistical Inference with M-Estimators on Bandit Data

Bandit algorithms are increasingly used in real world sequential decisio...
04/29/2021 ∙ by Kelly W. Zhang, 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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Deep Neural Networks are Surprisingly Reversible: A Baseline for Zero-Shot Inversion

Understanding the behavior and vulnerability of pre-trained deep neural ...
07/13/2021 ∙ by Xin Dong, 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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