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08/24/2023
Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE
Variational autoencoder (VAE) is a popular method for drug discovery and...
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12/06/2022
Improving Molecule Properties Through 2-Stage VAE
Variational autoencoder (VAE) is a popular method for drug discovery and...
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06/08/2021
Coarse-to-Fine Curriculum Learning
When faced with learning challenging new tasks, humans often follow sequ...
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04/16/2021
Re-TACRED: Addressing Shortcomings of the TACRED Dataset
TACRED is one of the largest and most widely used sentence-level relatio...
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12/09/2020
Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction
Relation extraction (RE) aims to predict a relation between a subject an...
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10/06/2020
Efficient Meta Lifelong-Learning with Limited Memory
Current natural language processing models work well on a single task, y...
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09/17/2020
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction
How meaning is represented in the brain is still one of the big open que...
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08/18/2020
Robust Handwriting Recognition with Limited and Noisy Data
Despite the advent of deep learning in computer vision, the general hand...
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07/25/2020
Nonlinear ISA with Auxiliary Variables for Learning Speech Representations
This paper extends recent work on nonlinear Independent Component Analys...
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07/06/2020
Covariate Distribution Aware Meta-learning
Meta-learning has proven to be successful at few-shot learning across th...
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04/29/2020
Politeness Transfer: A Tag and Generate Approach
This paper introduces a new task of politeness transfer which involves c...
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04/18/2020
Robust Density Estimation under Besov IPM Losses
We study minimax convergence rates of nonparametric density estimation i...
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04/12/2020
Minimizing FLOPs to Learn Efficient Sparse Representations
Deep representation learning has become one of the most widely adopted a...
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02/20/2020
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets
We study distributed optimization algorithms for minimizing the average ...
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02/06/2020
Optimal Adaptive Matrix Completion
We study the problem of exact completion for m × n sized matrix of rank ...
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01/27/2020
Unsupervised Program Synthesis for Images using Tree-Structured LSTM
Program synthesis has recently emerged as a promising approach to the im...
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12/08/2019
Learned Interpolation for 3D Generation
In order to generate novel 3D shapes with machine learning, one must all...
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11/27/2019
LucidDream: Controlled Temporally-Consistent DeepDream on Videos
In this work, we aim to propose a set of techniques to improve the contr...
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11/18/2019
RotationOut as a Regularization Method for Neural Network
In this paper, we propose a novel regularization method, RotationOut, fo...
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10/22/2019
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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08/20/2019
Developing Creative AI to Generate Sculptural Objects
We explore the intersection of human and machine creativity by generatin...
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08/05/2019
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
We describe ChemBO, a Bayesian Optimization framework for generating and...
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06/20/2019
A Deep Reinforcement Learning Approach for Global Routing
Global routing has been a historically challenging problem in electronic...
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05/30/2019
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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04/22/2019
LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds
We present LBS-AE; a self-supervised autoencoding algorithm for fitting ...
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03/23/2019
Competence-based Curriculum Learning for Neural Machine Translation
Current state-of-the-art NMT systems use large neural networks that are ...
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03/15/2019
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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02/26/2019
Implicit Kernel Learning
Kernels are powerful and versatile tools in machine learning and statist...
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02/21/2019
End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data
We describe the construction of end-to-end jet image classifiers based o...
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01/31/2019
ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization
Optimizing an expensive-to-query function is a common task in science an...
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01/18/2019
Kernel Change-point Detection with Auxiliary Deep Generative Models
Detecting the emergence of abrupt property changes in time series is a c...
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12/19/2018
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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11/24/2018
Characterizing and Avoiding Negative Transfer
When labeled data is scarce for a specific target task, transfer learnin...
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11/15/2018
Learning to Predict the Cosmological Structure Formation
Matter evolved under influence of gravity from minuscule density fluctua...
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11/13/2018
Hallucinating Point Cloud into 3D Sculptural Object
Our team of artists and machine learning researchers designed a creative...
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10/13/2018
Point Cloud GAN
Generative Adversarial Networks (GAN) can achieve promising performance ...
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10/04/2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
One of the mystery in the success of neural networks is randomly initial...
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07/31/2018
End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC
We describe the construction of a class of general, end-to-end, image-ba...
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07/11/2018
Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis
Multimodal machine learning is a core research area spanning the languag...
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06/28/2018
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
We propose an attention-based method that aggregates local image feature...
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05/30/2018
A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations
Many real world applications can be framed as multi-objective optimizati...
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05/25/2018
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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05/24/2018
Cautious Deep Learning
Most classifiers operate by selecting the maximum of an estimate of the ...
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05/22/2018
Nonparametric Density Estimation under Adversarial Losses
We study minimax convergence rates of nonparametric density estimation u...
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03/30/2018
Minimax Estimation of Quadratic Fourier Functionals
We study estimation of (semi-)inner products between two nonparametric p...
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02/24/2018
Minimax Distribution Estimation in Wasserstein Distance
The Wasserstein metric is an important measure of distance between proba...
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02/13/2018
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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02/11/2018
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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01/30/2018
Transformation Autoregressive Networks
The fundamental task of general density estimation has been of keen inte...
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12/03/2017
