Yale University

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Since its founding in 1701, Yale University has been dedicated to expanding and sharing knowledge, inspiring innovation, and preserving cultural and scientific information for future generations.

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...
09/11/2019 ∙ by Tao Yu, et al. ∙ 39 ∙ share

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Using wavelets to analyze similarities in image datasets

Deep learning image classifiers usually rely on huge training sets and t...
02/24/2020 ∙ by Roozbeh Yousefzadeh, et al. ∙ 30 ∙ share

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

An important aspect for an improved cardiac functional analysis is the a...
12/21/2019 ∙ by Omar S. Al-Kadi, et al. ∙ 26 ∙ share

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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...
01/11/2020 ∙ by Bo Zhou, et al. ∙ 24 ∙ share

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KLT Picker: Particle Picking Using Data-Driven Optimal Templates

Particle picking is currently a critical step in the cryo-EM single part...
12/12/2019 ∙ by Amitay Eldar, et al. ∙ 16 ∙ share

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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...
02/09/2020 ∙ by Alexander Tong, et al. ∙ 14 ∙ share

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A Spectral Regularizer for Unsupervised Disentanglement

Generative models that learn to associate variations in the output along...
12/04/2018 ∙ by Aditya Ramesh, et al. ∙ 10 ∙ share

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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-...
02/25/2020 ∙ by Yifei Min, et al. ∙ 10 ∙ share

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LOCA: LOcal Conformal Autoencoder for standardized data coordinates

We propose a deep-learning based method for obtaining standardized data ...
04/15/2020 ∙ by Erez Peterfreund, et al. ∙ 10 ∙ share

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Submodular Maximization Through Barrier Functions

In this paper, we introduce a novel technique for constrained submodular...
02/10/2020 ∙ by Ashwinkumar Badanidiyuru, et al. ∙ 9 ∙ share

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Improving Text-to-SQL Evaluation Methodology

To be informative, an evaluation must measure how well systems generaliz...
06/23/2018 ∙ by Catherine Finegan-Dollak, et al. ∙ 8 ∙ share

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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...
02/16/2019 ∙ by Michihiro Yasunaga, et al. ∙ 8 ∙ share

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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...
01/03/2020 ∙ by Roozbeh Yousefzadeh, et al. ∙ 8 ∙ share

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

Reliable motion estimation and strain analysis using 3D+time echocardiog...
07/12/2018 ∙ by Allen Lu, et al. ∙ 6 ∙ share

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Black Box Submodular Maximization: Discrete and Continuous Settings

In this paper, we consider the problem of black box continuous submodula...
01/28/2019 ∙ by Lin Chen, et al. ∙ 6 ∙ share

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Visualizing the PHATE of Neural Networks

Understanding why and how certain neural networks outperform others is k...
08/07/2019 ∙ by Scott Gigante, et al. ∙ 6 ∙ share

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Activation Density driven Energy-Efficient Pruning in Training

The process of neural network pruning with suitable fine-tuning and retr...
02/07/2020 ∙ by Timothy Foldy-Porto, et al. ∙ 6 ∙ share

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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...
05/04/2020 ∙ by Nitin Rathi, et al. ∙ 6 ∙ share

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Beyond Imitation: Generative and Variational Choreography via Machine Learning

Our team of dance artists, physicists, and machine learning researchers ...
07/11/2019 ∙ by Mariel Pettee, et al. ∙ 4 ∙ share

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Surfing: Iterative optimization over incrementally trained deep networks

We investigate a sequential optimization procedure to minimize the empir...
07/19/2019 ∙ by Ganlin Song, et al. ∙ 3 ∙ share

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Fair quantile regression

Quantile regression is a tool for learning conditional distributions. In...
07/19/2019 ∙ by Dana Yang, et al. ∙ 3 ∙ share

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Coarse Graining of Data via Inhomogeneous Diffusion Condensation

Big data often has emergent structure that exists at multiple levels of ...
07/10/2019 ∙ by Nathan Brugnone, et al. ∙ 2 ∙ share

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Neural Embedding for Physical Manipulations

In common real-world robotic operations, action and state spaces can be ...
07/13/2019 ∙ by Lingzhi Zhang, et al. ∙ 2 ∙ share

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Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation

A deep learning model trained on some labeled data from a certain source...
07/31/2019 ∙ by Junlin Yang, et al. ∙ 2 ∙ share

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DANTE: Deep Affinity Network for Clustering Conversational Interactants

We propose a data-driven approach to visually detect conversational grou...
07/24/2019 ∙ by Mason Swofford, et al. ∙ 1 ∙ share

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Feature Selection Facilitates Learning Mixtures of Discrete Product Distributions

Feature selection can facilitate the learning of mixtures of discrete ra...
11/25/2017 ∙ by Vincent Zhao, et al. ∙ 0 ∙ share

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Manifold learning with bi-stochastic kernels

In this paper we answer the following question: what is the infinitesima...
11/17/2017 ∙ by Ronald R. Coifman, et al. ∙ 0 ∙ share

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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...
11/13/2017 ∙ by George C. Linderman, et al. ∙ 0 ∙ share

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Multilayer tensor factorization with applications to recommender systems

Recommender systems have been widely adopted by electronic commerce and ...
11/05/2017 ∙ by Xuan Bi, et al. ∙ 0 ∙ share

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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...
10/30/2017 ∙ by Anderson Y. Zhang, et al. ∙ 0 ∙ share

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Minimax Estimation of Bandable Precision Matrices

The inverse covariance matrix provides considerable insight for understa...
10/19/2017 ∙ by Addison Hu, et al. ∙ 0 ∙ share

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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...
07/13/2017 ∙ by Lin Chen, et al. ∙ 0 ∙ share

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Data-Driven Tree Transforms and Metrics

We consider the analysis of high dimensional data given in the form of a...
08/18/2017 ∙ by Gal Mishne, et al. ∙ 0 ∙ share

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Mahalanonbis Distance Informed by Clustering

A fundamental question in data analysis, machine learning and signal pro...
08/13/2017 ∙ by Almog Lahav, et al. ∙ 0 ∙ share

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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...
12/29/2015 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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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...
11/17/2015 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Clustering with t-SNE, provably

t-distributed Stochastic Neighborhood Embedding (t-SNE), a clustering an...
06/08/2017 ∙ by George C. Linderman, et al. ∙ 0 ∙ share

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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 ...
06/05/2017 ∙ by Xiuyuan Cheng, et al. ∙ 0 ∙ share

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CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

Simulation is a key component of physics analysis in particle physics an...
05/05/2017 ∙ by Michela Paganini, et al. ∙ 0 ∙ share

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Estimating the coefficients of a mixture of two linear regressions by expectation maximization

We give convergence guarantees for estimating the coefficients of a symm...
04/26/2017 ∙ by Jason M. Klusowski, et al. ∙ 0 ∙ share

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Sentence Ordering using Recurrent Neural Networks

Modeling the structure of coherent texts is a task of great importance i...
11/08/2016 ∙ by Lajanugen Logeswaran, et al. ∙ 0 ∙ share

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Unsupervised Ensemble Regression

Consider a regression problem where there is no labeled data and the onl...
03/08/2017 ∙ by Omer Dror, et al. ∙ 0 ∙ share

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Scalable Greedy Feature Selection via Weak Submodularity

Greedy algorithms are widely used for problems in machine learning such ...
03/08/2017 ∙ by Rajiv Khanna, et al. ∙ 0 ∙ share

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On Approximation Guarantees for Greedy Low Rank Optimization

We provide new approximation guarantees for greedy low rank matrix estim...
03/08/2017 ∙ by Rajiv Khanna, et al. ∙ 0 ∙ share

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Streaming Weak Submodularity: Interpreting Neural Networks on the Fly

In many machine learning applications, it is important to explain the pr...
03/08/2017 ∙ by Ethan R. Elenberg, et al. ∙ 0 ∙ share

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Synaptic Scaling Balances Learning in a Spiking Model of Neocortex

Learning in the brain requires complementary mechanisms: potentiation an...
04/08/2013 ∙ by Mark Rowan, et al. ∙ 0 ∙ share

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Minimax Lower Bounds for Ridge Combinations Including Neural Nets

Estimation of functions of d variables is considered using ridge combi...
02/09/2017 ∙ by Jason M. Klusowski, et al. ∙ 0 ∙ share

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Stochastic Neighbor Embedding separates well-separated clusters

Stochastic Neighbor Embedding and its variants are widely used dimension...
02/09/2017 ∙ by Uri Shaham, et al. ∙ 0 ∙ share

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Critical Contours: An Invariant Linking Image Flow with Salient Surface Organization

We exploit a key result from visual psychophysics -- that individuals pe...
05/20/2017 ∙ by Benjamin S. Kunsberg, et al. ∙ 0 ∙ share

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Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

We provide a bridge between generative modeling in the Machine Learning ...
01/20/2017 ∙ by Luke de Oliveira, et al. ∙ 0 ∙ share

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