Barnabas Poczos

research

∙ 08/24/2023

Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE

Variational autoencoder (VAE) is a popular method for drug discovery and...

0 Chenghui Zhou, et al. ∙

research

∙ 12/06/2022

Improving Molecule Properties Through 2-Stage VAE

Variational autoencoder (VAE) is a popular method for drug discovery and...

0 Chenghui Zhou, et al. ∙

research

∙ 06/08/2021

Coarse-to-Fine Curriculum Learning

When faced with learning challenging new tasks, humans often follow sequ...

5 Otilia Stretcu, et al. ∙

research

∙ 04/16/2021

Re-TACRED: Addressing Shortcomings of the TACRED Dataset

TACRED is one of the largest and most widely used sentence-level relatio...

0 George Stoica, et al. ∙

research

∙ 12/09/2020

Improving Relation Extraction by Leveraging Knowledge Graph Link Prediction

Relation extraction (RE) aims to predict a relation between a subject an...

0 George Stoica, et al. ∙

research

∙ 10/06/2020

Efficient Meta Lifelong-Learning with Limited Memory

Current natural language processing models work well on a single task, y...

0 Zirui Wang, et al. ∙

research

∙ 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...

5 Mariya Toneva, et al. ∙

research

∙ 08/18/2020

Robust Handwriting Recognition with Limited and Noisy Data

Despite the advent of deep learning in computer vision, the general hand...

11 Hai Pham, et al. ∙

research

∙ 07/25/2020

Nonlinear ISA with Auxiliary Variables for Learning Speech Representations

This paper extends recent work on nonlinear Independent Component Analys...

7 Amrith Setlur, et al. ∙

research

∙ 07/06/2020

Covariate Distribution Aware Meta-learning

Meta-learning has proven to be successful at few-shot learning across th...

5 Amrith Setlur, et al. ∙

research

∙ 04/29/2020

Politeness Transfer: A Tag and Generate Approach

This paper introduces a new task of politeness transfer which involves c...

0 Aman Madaan, et al. ∙

research

∙ 04/18/2020

Robust Density Estimation under Besov IPM Losses

We study minimax convergence rates of nonparametric density estimation i...

5 Ananya Uppal, et al. ∙

research

∙ 04/12/2020

Minimizing FLOPs to Learn Efficient Sparse Representations

Deep representation learning has become one of the most widely adopted a...

20 Biswajit Paria, et al. ∙

research

∙ 02/20/2020

Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets

We study distributed optimization algorithms for minimizing the average ...

0 Ilqar ramazanli, et al. ∙

research

∙ 02/06/2020

Optimal Adaptive Matrix Completion

We study the problem of exact completion for m × n sized matrix of rank ...

0 Ilqar ramazanli, et al. ∙

research

∙ 01/27/2020

Unsupervised Program Synthesis for Images using Tree-Structured LSTM

Program synthesis has recently emerged as a promising approach to the im...

12 Chenghui Zhou, et al. ∙

research

∙ 12/08/2019

Learned Interpolation for 3D Generation

In order to generate novel 3D shapes with machine learning, one must all...

0 Austin Dill, et al. ∙

research

∙ 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...

13 Joel Ruben Antony Moniz, et al. ∙

research

∙ 11/18/2019

RotationOut as a Regularization Method for Neural Network

In this paper, we propose a novel regularization method, RotationOut, fo...

0 Kai Hu, et al. ∙

research

∙ 10/22/2019

Better Approximate Inference for Partial Likelihood Models with a Latent Structure

Temporal Point Processes (TPP) with partial likelihoods involving a late...

0 Amrith Setlur, et al. ∙

research

∙ 08/20/2019

Developing Creative AI to Generate Sculptural Objects

We explore the intersection of human and machine creativity by generatin...

8 Songwei Ge, et al. ∙

research

∙ 08/05/2019

ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

We describe ChemBO, a Bayesian Optimization framework for generating and...

0 Ksenia Korovina, et al. ∙

research

∙ 06/20/2019

A Deep Reinforcement Learning Approach for Global Routing

Global routing has been a historically challenging problem in electronic...

19 Haiguang Liao, et al. ∙

research

∙ 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...

4 Simon S. Du, et al. ∙

research

∙ 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 ...

0 Chun-Liang Li, et al. ∙

research

∙ 03/23/2019

Competence-based Curriculum Learning for Neural Machine Translation

Current state-of-the-art NMT systems use large neural networks that are ...

14 Emmanouil Antonios Platanios, et al. ∙

research

∙ 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...

14 Kirthevasan Kandasamy, et al. ∙

research

∙ 02/26/2019

Implicit Kernel Learning

Kernels are powerful and versatile tools in machine learning and statist...

0 Chun-Liang Li, et al. ∙

research

∙ 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...

0 Michael Andrews, et al. ∙

research

∙ 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...

18 Willie Neiswanger, et al. ∙

research

∙ 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...

0 Wei-Cheng Chang, et al. ∙

research

∙ 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...

0 Hai Pham, et al. ∙

research

∙ 11/24/2018

Characterizing and Avoiding Negative Transfer

When labeled data is scarce for a specific target task, transfer learnin...

0 Zirui Wang, et al. ∙

research

∙ 11/15/2018

Learning to Predict the Cosmological Structure Formation

Matter evolved under influence of gravity from minuscule density fluctua...

0 Siyu He, et al. ∙

research

∙ 11/13/2018

Hallucinating Point Cloud into 3D Sculptural Object

Our team of artists and machine learning researchers designed a creative...

0 Chun-Liang Li, et al. ∙

research

∙ 10/13/2018

Point Cloud GAN

Generative Adversarial Networks (GAN) can achieve promising performance ...

0 Chun-Liang Li, et al. ∙

research

∙ 10/04/2018

Gradient Descent Provably Optimizes Over-parameterized Neural Networks

One of the mystery in the success of neural networks is randomly initial...

30 Simon S. Du, et al. ∙

research

∙ 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...

0 Michael Andrews, et al. ∙

research

∙ 07/11/2018

Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis

Multimodal machine learning is a core research area spanning the languag...

0 Hai Pham, et al. ∙

research

∙ 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...

0 Sumedha Singla, et al. ∙

research

∙ 05/30/2018

A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations

Many real world applications can be framed as multi-objective optimizati...

0 Biswajit Paria, et al. ∙

research

∙ 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...

0 Kirthevasan Kandasamy, et al. ∙

research

∙ 05/24/2018

Cautious Deep Learning

Most classifiers operate by selecting the maximum of an estimate of the ...

2 Yotam Hechtlinger, et al. ∙

research

∙ 05/22/2018

Nonparametric Density Estimation under Adversarial Losses

We study minimax convergence rates of nonparametric density estimation u...

0 Shashank Singh, et al. ∙

research

∙ 03/30/2018

Minimax Estimation of Quadratic Fourier Functionals

We study estimation of (semi-)inner products between two nonparametric p...

0 Shashank Singh, et al. ∙

research

∙ 02/24/2018

Minimax Distribution Estimation in Wasserstein Distance

The Wasserstein metric is an important measure of distance between proba...

0 Shashank Singh, et al. ∙

research

∙ 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...

0 Yifan Wu, et al. ∙

research

∙ 02/11/2018

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Bayesian Optimisation (BO) refers to a class of methods for global optim...

0 Kirthevasan Kandasamy, et al. ∙

research

∙ 01/30/2018

Transformation Autoregressive Networks

The fundamental task of general density estimation has been of keen inte...

0 Junier B. Oliva, et al. ∙

research

∙ 12/03/2017

Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima

We consider the problem of learning a one-hidden-layer neural network wi...

0 Simon S. Du, et al. ∙

Barnabas Poczos

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