
Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
Much of biomedical and healthcare data is encoded in discrete, symbolic ...
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CoSQL: A Conversational TexttoSQL Challenge Towards CrossDomain Natural Language Interfaces to Databases
We present CoSQL, a corpus for building crossdomain, generalpurpose da...
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Using wavelets to analyze similarities in image datasets
Deep learning image classifiers usually rely on huge training sets and t...
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SpatioTemporal Segmentation in 3D Echocardiographic Sequences using Fractional Brownian Motion
An important aspect for an improved cardiac functional analysis is the a...
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DuDoRNet: Learning a DualDomain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior
MRI with multiple protocols is commonly used for diagnosis, but it suffe...
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Learning Potentials of Quantum Systems using Deep Neural Networks
Machine Learning has wide applications in a broad range of subjects, inc...
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Permutation invariant networks to learn Wasserstein metrics
Understanding the space of probability measures on a metric space equipp...
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Automatic Diagnosis of Pulmonary Embolism Using an Attentionguided Framework: A Largescale Study
Pulmonary Embolism (PE) is a lifethreatening disorder associated with h...
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KLT Picker: Particle Picking Using DataDriven Optimal Templates
Particle picking is currently a critical step in the cryoEM single part...
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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...
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TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
It is increasingly common to encounter data from dynamic processes captu...
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Domain Adaptation without Source Data
Domain adaptation assumes that samples from source and target domains ar...
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Using Wavelets and Spectral Methods to Study Patterns in ImageClassification Datasets
Deep learning models extract, before a final classification layer, featu...
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Visual Explanations from Spiking Neural Networks using Interspike Intervals
Spiking Neural Networks (SNNs) compute and communicate with asynchronous...
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A Spectral Regularizer for Unsupervised Disentanglement
Generative models that learn to associate variations in the output along...
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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...
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LOCA: LOcal Conformal Autoencoder for standardized data coordinates
We propose a deeplearning based method for obtaining standardized data ...
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Submodular Maximization Through Barrier Functions
In this paper, we introduce a novel technique for constrained submodular...
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Improving TexttoSQL Evaluation Methodology
To be informative, an evaluation must measure how well systems generaliz...
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TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts
Scientific documents rely on both mathematics and text to communicate id...
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Auditing and Debugging Deep Learning Models via Decision Boundaries: Individuallevel and Grouplevel Analysis
Deep learning models have been criticized for their lack of easy interpr...
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Efficiencydriven Hardware Optimization for Adversarially Robust Neural Networks
With a growing need to enable intelligence in embedded devices in the In...
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Let the Data Choose its Features: Differentiable Unsupervised Feature Selection
Scientific observations often consist of a large number of variables (fe...
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Learningbased Regularization for Cardiac Strain Analysis with Ability for Domain Adaptation
Reliable motion estimation and strain analysis using 3D+time echocardiog...
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Black Box Submodular Maximization: Discrete and Continuous Settings
In this paper, we consider the problem of black box continuous submodula...
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Visualizing the PHATE of Neural Networks
Understanding why and how certain neural networks outperform others is k...
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Activation Density driven EnergyEfficient Pruning in Training
The process of neural network pruning with suitable finetuning and retr...
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Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
Spiking Neural Networks (SNNs) operate with asynchronous discrete events...
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Beyond Imitation: Generative and Variational Choreography via Machine Learning
Our team of dance artists, physicists, and machine learning researchers ...
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Surfing: Iterative optimization over incrementally trained deep networks
We investigate a sequential optimization procedure to minimize the empir...
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Fair quantile regression
Quantile regression is a tool for learning conditional distributions. In...
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Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Big data often has emergent structure that exists at multiple levels of ...
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Neural Embedding for Physical Manipulations
In common realworld robotic operations, action and state spaces can be ...
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Unsupervised Domain Adaptation via Disentangled Representations: Application to CrossModality Liver Segmentation
A deep learning model trained on some labeled data from a certain source...
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DoublyStochastic Normalization of the Gaussian Kernel is Robust to Heteroskedastic Noise
A fundamental step in many dataanalysis techniques is the construction ...
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DANTE: Deep Affinity Network for Clustering Conversational Interactants
We propose a datadriven approach to visually detect conversational grou...
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GraPPa: GrammarAugmented PreTraining for Table Semantic Parsing
We present GraPPa, an effective pretraining approach for table semantic...
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Feature Selection Facilitates Learning Mixtures of Discrete Product Distributions
Feature selection can facilitate the learning of mixtures of discrete ra...
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Manifold learning with bistochastic kernels
In this paper we answer the following question: what is the infinitesima...
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Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science
If we pick n random points uniformly in [0,1]^d and connect each point t...
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Multilayer tensor factorization with applications to recommender systems
Recommender systems have been widely adopted by electronic commerce and ...
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Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection
The mean field variational Bayes method is becoming increasingly popular...
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Minimax Estimation of Bandable Precision Matrices
The inverse covariance matrix provides considerable insight for understa...
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Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Submodular functions are a broad class of set functions, which naturally...
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DataDriven Tree Transforms and Metrics
We consider the analysis of high dimensional data given in the form of a...
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Mahalanonbis Distance Informed by Clustering
A fundamental question in data analysis, machine learning and signal pro...
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Common Variable Learning and Invariant Representation Learning using Siamese Neural Networks
We consider the statistical problem of learning common source of variabi...
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Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization
We propose a general framework for increasing local stability of Artific...
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Clustering with tSNE, provably
tdistributed Stochastic Neighborhood Embedding (tSNE), a clustering an...
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The Geometry of Nodal Sets and Outlier Detection
Let (M,g) be a compact manifold and let Δϕ_k = λ_k ϕ_k be the sequence ...
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