
Efficient Meta LifelongLearning with Limited Memory
Current natural language processing models work well on a single task, y...
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Modeling Task Effects on Meaning Representation in the Brain via ZeroShot MEG Prediction
How meaning is represented in the brain is still one of the big open que...
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Robust Handwriting Recognition with Limited and Noisy Data
Despite the advent of deep learning in computer vision, the general hand...
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Nonlinear ISA with Auxiliary Variables for Learning Speech Representations
This paper extends recent work on nonlinear Independent Component Analys...
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Covariate Distribution Aware Metalearning
Metalearning has proven to be successful at fewshot learning across th...
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Politeness Transfer: A Tag and Generate Approach
This paper introduces a new task of politeness transfer which involves c...
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Robust Density Estimation under Besov IPM Losses
We study minimax convergence rates of nonparametric density estimation i...
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Minimizing FLOPs to Learn Efficient Sparse Representations
Deep representation learning has become one of the most widely adopted a...
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Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets
We study distributed optimization algorithms for minimizing the average ...
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Optimal Adaptive Matrix Completion
We study the problem of exact completion for m × n sized matrix of rank ...
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Unsupervised Program Synthesis for Images using TreeStructured LSTM
Program synthesis has recently emerged as a promising approach to the im...
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Learned Interpolation for 3D Generation
In order to generate novel 3D shapes with machine learning, one must all...
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LucidDream: Controlled TemporallyConsistent DeepDream on Videos
In this work, we aim to propose a set of techniques to improve the contr...
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RotationOut as a Regularization Method for Neural Network
In this paper, we propose a novel regularization method, RotationOut, fo...
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Better Approximate Inference for Partial Likelihood Models with a Latent Structure
Temporal Point Processes (TPP) with partial likelihoods involving a late...
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Developing Creative AI to Generate Sculptural Objects
We explore the intersection of human and machine creativity by generatin...
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ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
We describe ChemBO, a Bayesian Optimization framework for generating and...
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A Deep Reinforcement Learning Approach for Global Routing
Global routing has been a historically challenging problem in electronic...
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Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
While graph kernels (GKs) are easy to train and enjoy provable theoretic...
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LBS Autoencoder: Selfsupervised Fitting of Articulated Meshes to Point Clouds
We present LBSAE; a selfsupervised autoencoding algorithm for fitting ...
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Competencebased Curriculum Learning for Neural Machine Translation
Current stateoftheart NMT systems use large neural networks that are ...
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Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
Bayesian Optimisation (BO), refers to a suite of techniques for global o...
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Implicit Kernel Learning
Kernels are powerful and versatile tools in machine learning and statist...
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EndtoEnd Jet Classification of Quarks and Gluons with the CMS Open Data
We describe the construction of endtoend jet image classifiers based o...
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ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization
Optimizing an expensivetoquery function is a common task in science an...
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Kernel Changepoint Detection with Auxiliary Deep Generative Models
Detecting the emergence of abrupt property changes in time series is a c...
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Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities
Multimodal sentiment analysis is a core research area that studies speak...
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Characterizing and Avoiding Negative Transfer
When labeled data is scarce for a specific target task, transfer learnin...
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Learning to Predict the Cosmological Structure Formation
Matter evolved under influence of gravity from minuscule density fluctua...
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Hallucinating Point Cloud into 3D Sculptural Object
Our team of artists and machine learning researchers designed a creative...
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Point Cloud GAN
Generative Adversarial Networks (GAN) can achieve promising performance ...
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Gradient Descent Provably Optimizes Overparameterized Neural Networks
One of the mystery in the success of neural networks is randomly initial...
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EndtoEnd Physics Event Classification with the CMS Open Data: Applying Imagebased Deep Learning on Detector Data to Directly Classify Collision Events at the LHC
We describe the construction of a class of general, endtoend, imageba...
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Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis
Multimodal machine learning is a core research area spanning the languag...
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Subject2Vec: GenerativeDiscriminative Approach from a Set of Image Patches to a Vector
We propose an attentionbased method that aggregates local image feature...
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A Flexible MultiObjective Bayesian Optimization Approach using Random Scalarizations
Many real world applications can be framed as multiobjective optimizati...
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Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
We design a new myopic strategy for a wide class of sequential design of...
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Cautious Deep Learning
Most classifiers operate by selecting the maximum of an estimate of the ...
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Nonparametric Density Estimation under Adversarial Losses
We study minimax convergence rates of nonparametric density estimation u...
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Minimax Estimation of Quadratic Fourier Functionals
We study estimation of (semi)inner products between two nonparametric p...
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Minimax Distribution Estimation in Wasserstein Distance
The Wasserstein metric is an important measure of distance between proba...
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Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent
A major challenge in understanding the generalization of deep learning i...
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Neural Architecture Search with Bayesian Optimisation and Optimal Transport
Bayesian Optimisation (BO) refers to a class of methods for global optim...
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Transformation Autoregressive Networks
The fundamental task of general density estimation has been of keen inte...
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Gradient Descent Learns Onehiddenlayer CNN: Don't be Afraid of Spurious Local Minima
We consider the problem of learning a onehiddenlayer neural network wi...
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Estimating Cosmological Parameters from the Dark Matter Distribution
A grand challenge of the 21st century cosmology is to accurately estimat...
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A Generic Approach for Escaping Saddle points
A central challenge to using firstorder methods for optimizing nonconve...
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On the Reconstruction Risk of Convolutional Sparse Dictionary Learning
Sparse dictionary learning (SDL) has become a popular method for adaptiv...
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Recurrent Estimation of Distributions
This paper presents the recurrent estimation of distributions (RED) for ...
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Gradient Descent Can Take Exponential Time to Escape Saddle Points
Although gradient descent (GD) almost always escapes saddle points asymp...
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