Yale University

research

∙ 06/24/2020

Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings

Much of biomedical and healthcare data is encoded in discrete, symbolic ...

David Chang, et al. ∙

research

∙ 07/22/2021

QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (...

Hanrui Wang, et al. ∙

research

∙ 09/11/2019

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases

We present CoSQL, a corpus for building cross-domain, general-purpose da...

Tao Yu, et al. ∙

research

∙ 02/24/2020

Using wavelets to analyze similarities in image datasets

Deep learning image classifiers usually rely on huge training sets and t...

Roozbeh Yousefzadeh, et al. ∙

research

∙ 12/21/2019

Spatio-Temporal Segmentation in 3D Echocardiographic Sequences using Fractional Brownian Motion

An important aspect for an improved cardiac functional analysis is the a...

Omar S. Al-Kadi, et al. ∙

research

∙ 01/24/2022

Guided Generative Protein Design using Regularized Transformers

The development of powerful natural language models have increased the a...

Egbert Castro, et al. ∙

research

∙ 01/11/2020

DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior

MRI with multiple protocols is commonly used for diagnosis, but it suffe...

Bo Zhou, et al. ∙

research

∙ 06/23/2020

Learning Potentials of Quantum Systems using Deep Neural Networks

Machine Learning has wide applications in a broad range of subjects, inc...

Arijit Sehanobish, et al. ∙

research

∙ 10/12/2020

Permutation invariant networks to learn Wasserstein metrics

Understanding the space of probability measures on a metric space equipp...

Arijit Sehanobish, et al. ∙

research

∙ 09/16/2021

RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning

Present-day Deep Reinforcement Learning (RL) systems show great promise ...

Adarsh Kumar Kosta, et al. ∙

research

∙ 05/29/2020

Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study

Pulmonary Embolism (PE) is a life-threatening disorder associated with h...

Luyao Shi, et al. ∙

research

∙ 10/14/2021

Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation

Spiking Neural Networks (SNNs) have recently emerged as the low-power al...

Youngeun Kim, et al. ∙

research

∙ 03/04/2022

Building 3D Generative Models from Minimal Data

We propose a method for constructing generative models of 3D objects fro...

Skylar Sutherland, et al. ∙

research

∙ 12/22/2021

Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods

Medical image datasets can have large number of images representing pati...

Roozbeh Yousefzadeh, et al. ∙

research

∙ 11/08/2021

Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation

Medical report generation, which aims to automatically generate a long a...

Fenglin Liu, et al. ∙

research

∙ 12/12/2019

KLT Picker: Particle Picking Using Data-Driven Optimal Templates

Particle picking is currently a critical step in the cryo-EM single part...

Amitay Eldar, et al. ∙

research

∙ 02/21/2021

A Sketching Method for Finding the Closest Point on a Convex Hull

We develop a sketching algorithm to find the point on the convex hull of...

Roozbeh Yousefzadeh, et al. ∙

research

∙ 02/09/2022

Adversarial Detection without Model Information

Most prior state-of-the-art adversarial detection works assume that the ...

Abhishek Moitra, et al. ∙

research

∙ 04/11/2022

MIME: Adapting a Single Neural Network for Multi-task Inference with Memory-efficient Dynamic Pruning

Recent years have seen a paradigm shift towards multi-task learning. Thi...

Abhiroop Bhattacharjee, et al. ∙

research

∙ 02/09/2020

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics

It is increasingly common to encounter data from dynamic processes captu...

Alexander Tong, et al. ∙

research

∙ 07/03/2020

Domain Adaptation without Source Data

Domain adaptation assumes that samples from source and target domains ar...

Youngeun Kim, et al. ∙

research

∙ 07/07/2021

Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review

Electronic health records (EHRs), digital collections of patient healthc...

Irene Li, et al. ∙

research

∙ 12/21/2021

Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling

Federated learning (FL) algorithms usually sample a fraction of clients ...

Bing Luo, et al. ∙

research

∙ 12/27/2021

Self-normalized Classification of Parkinson's Disease DaTscan Images

Classifying SPECT images requires a preprocessing step which normalizes ...

Yuan Zhou, et al. ∙

research

∙ 01/03/2022

KerGNNs: Interpretable Graph Neural Networks with Graph Kernels

Graph kernels are historically the most widely-used technique for graph ...

Aosong Feng, et al. ∙

research

∙ 06/17/2020

Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets

Deep learning models extract, before a final classification layer, featu...

Roozbeh Yousefzadeh, et al. ∙

research

∙ 03/26/2021

Visual Explanations from Spiking Neural Networks using Interspike Intervals

Spiking Neural Networks (SNNs) compute and communicate with asynchronous...

Youngeun Kim, et al. ∙

research

∙ 12/13/2021

A Homotopy Algorithm for Optimal Transport

The optimal transport problem has many applications in machine learning,...

Roozbeh Yousefzadeh, et al. ∙

research

∙ 12/04/2018

A Spectral Regularizer for Unsupervised Disentanglement

Generative models that learn to associate variations in the output along...

Aditya Ramesh, et al. ∙

research

∙ 02/25/2020

The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization

Despite remarkable success, deep neural networks are sensitive to human-...

Yifei Min, et al. ∙

research

∙ 04/15/2020

LOCA: LOcal Conformal Autoencoder for standardized data coordinates

We propose a deep-learning based method for obtaining standardized data ...

Erez Peterfreund, et al. ∙

research

∙ 08/13/2021

SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation

Automated segmentation in medical image analysis is a challenging task t...

Chenyu You, et al. ∙

research

∙ 08/29/2021

SummerTime: Text Summarization Toolkit for Non-experts

Recent advances in summarization provide models that can generate summar...

Ansong Ni, et al. ∙

research

∙ 03/11/2022

Neuromorphic Data Augmentation for Training Spiking Neural Networks

Developing neuromorphic intelligence on event-based datasets with spikin...

Yuhang Li, et al. ∙

research

∙ 04/10/2022

MedDistant19: A Challenging Benchmark for Distantly Supervised Biomedical Relation Extraction

Relation Extraction in the biomedical domain is challenging due to the l...

Saadullah Amin, et al. ∙

research

∙ 02/10/2020

Submodular Maximization Through Barrier Functions

In this paper, we introduce a novel technique for constrained submodular...

Ashwinkumar Badanidiyuru, et al. ∙

research

∙ 02/08/2022

Do Language Models Learn Position-Role Mappings?

How is knowledge of position-role mappings in natural language learned? ...

Jackson Petty, et al. ∙

research

∙ 06/23/2018

Improving Text-to-SQL Evaluation Methodology

To be informative, an evaluation must measure how well systems generaliz...

Catherine Finegan-Dollak, et al. ∙

research

∙ 02/16/2019

TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts

Scientific documents rely on both mathematics and text to communicate id...

Michihiro Yasunaga, et al. ∙

research

∙ 01/03/2020

Auditing and Debugging Deep Learning Models via Decision Boundaries: Individual-level and Group-level Analysis

Deep learning models have been criticized for their lack of easy interpr...

Roozbeh Yousefzadeh, et al. ∙

research

∙ 05/09/2021

Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks

With a growing need to enable intelligence in embedded devices in the In...

Abhiroop Bhattacharjee, et al. ∙

research

∙ 07/09/2020

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection

Scientific observations often consist of a large number of variables (fe...

Ofir Lindenbaum, et al. ∙

research

∙ 07/12/2018

Learning-based Regularization for Cardiac Strain Analysis with Ability for Domain Adaptation

Reliable motion estimation and strain analysis using 3D+time echocardiog...

Allen Lu, et al. ∙

research

∙ 01/28/2019

Black Box Submodular Maximization: Discrete and Continuous Settings

In this paper, we consider the problem of black box continuous submodula...

Lin Chen, et al. ∙

research

∙ 08/07/2019

Visualizing the PHATE of Neural Networks

Understanding why and how certain neural networks outperform others is k...

Scott Gigante, et al. ∙

research

∙ 02/07/2020

Activation Density driven Energy-Efficient Pruning in Training

The process of neural network pruning with suitable fine-tuning and retr...

Timothy Foldy-Porto, et al. ∙

research

∙ 05/04/2020

Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation

Spiking Neural Networks (SNNs) operate with asynchronous discrete events...

Nitin Rathi, et al. ∙

research

∙ 01/13/2022

Examining and Mitigating the Impact of Crossbar Non-idealities for Accurate Implementation of Sparse Deep Neural Networks

Recently several structured pruning techniques have been introduced for ...

Abhiroop Bhattacharjee, et al. ∙

research

∙ 09/17/2021

Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains

Prerequisite chain learning helps people acquire new knowledge efficient...

Irene Li, et al. ∙

research

∙ 10/04/2021

3D-Transformer: Molecular Representation with Transformer in 3D Space

Spatial structures in the 3D space are important to determine molecular ...

Fang Wu, et al. ∙

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