Georgia Institute of Technology

research

∙ 09/06/2021

A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning

The rapid recent progress in machine learning (ML) has raised a number o...

Yehuda Dar, et al. ∙

research

∙ 10/08/2020

Fast Fourier Transformation for Optimizing Convolutional Neural Networks in Object Recognition

This paper proposes to use Fast Fourier Transformation-based U-Net (a re...

Varsha Nair, et al. ∙

research

∙ 01/29/2020

REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild

In recent years, significant attention has been devoted towards integrat...

Rahul Duggal, et al. ∙

research

∙ 02/24/2022

Explanatory Paradigms in Neural Networks

In this article, we present a leap-forward expansion to the study of exp...

Ghassan AlRegib, et al. ∙

research

∙ 03/03/2020

Predicting A Creator's Preferences In, and From, Interactive Generative Art

As a lay user creates an art piece using an interactive generative art t...

Devi Parikh, et al. ∙

research

∙ 06/02/2022

Invertible Neural Networks for Graph Prediction

In this work, we address conditional generation using deep invertible ne...

Chen Xu, et al. ∙

research

∙ 06/21/2020

Feel The Music: Automatically Generating A Dance For An Input Song

We present a general computational approach that enables a machine to ge...

Purva Tendulkar, et al. ∙

research

∙ 03/21/2020

Deep Reinforcement Learning with Smooth Policy

Deep neural networks have been widely adopted in modern reinforcement le...

Qianli Shen, et al. ∙

research

∙ 01/25/2019

Audio-Visual Scene-Aware Dialog

We introduce the task of scene-aware dialog. Given a follow-up question ...

Huda Alamri, et al. ∙

research

∙ 12/20/2018

nocaps: novel object captioning at scale

Image captioning models have achieved impressive results on datasets con...

Harsh Agrawal, et al. ∙

research

∙ 02/10/2020

Statistical Guarantees of Generative Adversarial Networks for Distribution Estimation

Generative Adversarial Networks (GANs) have achieved great success in un...

Minshuo Chen, et al. ∙

research

∙ 01/20/2020

SQuINTing at VQA Models: Interrogating VQA Models with Sub-Questions

Existing VQA datasets contain questions with varying levels of complexit...

Ramprasaath R. Selvaraju, et al. ∙

research

∙ 06/10/2020

A Bayesian Framework for Nash Equilibrium Inference in Human-Robot Parallel Play

We consider shared workspace scenarios with humans and robots acting to ...

Shray Bansal, et al. ∙

research

∙ 08/07/2021

The Effect of Training Parameters and Mechanisms on Decentralized Federated Learning based on MNIST Dataset

Federated Learning is an algorithm suited for training models on decentr...

Zhuofan Zhang, et al. ∙

research

∙ 10/07/2021

GNN is a Counter? Revisiting GNN for Question Answering

Question Answering (QA) has been a long-standing research topic in AI an...

Kuan Wang, et al. ∙

research

∙ 12/31/2018

The role of visual saliency in the automation of seismic interpretation

In this paper, we propose a workflow based on SalSi for the detection an...

Muhammad Amir Shafiq, et al. ∙

research

∙ 12/23/2021

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Despite the myriad peer-reviewed papers demonstrating novel Artificial I...

Felipe Giuste, et al. ∙

research

∙ 09/22/2021

Differentiable Scaffolding Tree for Molecular Optimization

The structural design of functional molecules, also called molecular opt...

Tianfan Fu, et al. ∙

research

∙ 05/20/2022

Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning

Machine learning models with explainable predictions are increasingly so...

Abhijit Suprem, et al. ∙

research

∙ 12/03/2019

Continuous Online Learning and New Insights to Online Imitation Learning

Online learning is a powerful tool for analyzing iterative algorithms. H...

Jonathan Lee, et al. ∙

research

∙ 01/21/2020

Massif: Interactive Interpretation of Adversarial Attacks on Deep Learning

Deep neural networks (DNNs) are increasingly powering high-stakes applic...

Nilaksh Das, et al. ∙

research

∙ 01/07/2020

CNN 101: Interactive Visual Learning for Convolutional Neural Networks

The success of deep learning solving previously-thought hard problems ha...

Zijie J. Wang, et al. ∙

research

∙ 02/20/2020

Learning to Walk in the Real World with Minimal Human Effort

Reliable and stable locomotion has been one of the most fundamental chal...

Sehoon Ha, et al. ∙

research

∙ 01/12/2021

Expanding Explainability: Towards Social Transparency in AI systems

As AI-powered systems increasingly mediate consequential decision-making...

Upol Ehsan, et al. ∙

research

∙ 02/11/2019

Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded

Many vision and language models suffer from poor visual grounding - ofte...

Ramprasaath R. Selvaraju, et al. ∙

research

∙ 04/26/2019

Perceptual Attention-based Predictive Control

In this paper, we present a novel information processing architecture fo...

Keuntaek Lee, et al. ∙

research

∙ 11/21/2019

Automatically Neutralizing Subjective Bias in Text

Texts like news, encyclopedias, and some social media strive for objecti...

Reid Pryzant, et al. ∙

research

∙ 03/29/2021

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data

Self-supervised learning has enabled significant improvements on natural...

Alex Fedorov, et al. ∙

research

∙ 02/10/2019

EvalAI: Towards Better Evaluation Systems for AI Agents

We introduce EvalAI, an open source platform for evaluating and comparin...

Deshraj Yadav, et al. ∙

research

∙ 03/11/2019

GOGGLES: Automatic Training Data Generation with Affinity Coding

Generating large labeled training data is becoming the biggest bottlenec...

Nilaksh Das, et al. ∙

research

∙ 04/14/2019

Probabilistic Kernel Support Vector Machines

We propose a probabilistic enhancement of standard kernel Support Vecto...

Yongxin Chen, et al. ∙

research

∙ 01/03/2020

Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency

Analysis of state-of-the-art VO/VSLAM system exposes a gap in balancing ...

Yipu Zhao, et al. ∙

research

∙ 12/04/2021

Variational Wasserstein gradient flow

The gradient flow of a function over the space of probability densities ...

Jiaojiao Fan, et al. ∙

research

∙ 02/08/2022

Police Text Analysis: Topic Modeling and Spatial Relative Density Estimation

We analyze a large corpus of police incident narrative documents in unde...

Sarah Huestis-Mitchell, et al. ∙

research

∙ 05/18/2022

Constraint-Based Causal Structure Learning from Undersampled Graphs

Graphical structures estimated by causal learning algorithms from time s...

Mohammadsajad Abavisani, et al. ∙

research

∙ 03/19/2019

Trick or TReAT: Thematic Reinforcement for Artistic Typography

An approach to make text visually appealing and memorable is semantic re...

Purva Tendulkar, et al. ∙

research

∙ 10/26/2018

TarMAC: Targeted Multi-Agent Communication

We explore a collaborative multi-agent reinforcement learning setting wh...

Abhishek Das, et al. ∙

research

∙ 02/11/2019

Synthesizing New Retinal Symptom Images by Multiple Generative Models

Age-Related Macular Degeneration (AMD) is an asymptomatic retinal diseas...

Yi-Chieh Liu, et al. ∙

research

∙ 09/04/2019

Meta Learning with Relational Information for Short Sequences

This paper proposes a new meta-learning method -- named HARMLESS (HAwkes...

Yujia Xie, et al. ∙

research

∙ 08/04/2020

Implicit Saliency in Deep Neural Networks

In this paper, we show that existing recognition and localization deep a...

Yutong Sun, et al. ∙

research

∙ 05/18/2022

CARNet: A Dynamic Autoencoder for Learning Latent Dynamics in Autonomous Driving Tasks

Autonomous driving has received a lot of attention in the automotive ind...

Andrey Pak, et al. ∙

research

∙ 10/04/2019

Learning Robust Representations with Graph Denoising Policy Network

Graph representation learning, aiming to learn low-dimensional represent...

Lu Wang, et al. ∙

research

∙ 11/12/2019

Pose estimation and bin picking for deformable products

Robotic systems in manufacturing applications commonly assume known obje...

Benjamin Joffe, et al. ∙

research

∙ 03/23/2021

Extracting Causal Visual Features for Limited label Classification

Neural networks trained to classify images do so by identifying features...

Mohit Prabhushankar, et al. ∙

research

∙ 09/16/2021

RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning

Present-day Deep Reinforcement Learning (RL) systems show great promise ...

Adarsh Kumar Kosta, et al. ∙

research

∙ 05/27/2022

Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport

The problem of optimization on Stiefel manifold, i.e., minimizing functi...

Lingkai Kong, et al. ∙

research

∙ 03/18/2019

Lemotif: Abstract Visual Depictions of your Emotional States in Life

We present Lemotif. Lemotif generates a motif for your emotional life. Y...

Devi Parikh, et al. ∙

research

∙ 02/26/2020

Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data

Deep neural networks have attained remarkable performance when applied t...

Yen-Chang Hsu, et al. ∙

research

∙ 06/16/2022

Open-Set Recognition with Gradient-Based Representations

Neural networks for image classification tasks assume that any given ima...

Jinsol Lee, et al. ∙

research

∙ 06/16/2022

Gradient-Based Adversarial and Out-of-Distribution Detection

We propose to utilize gradients for detecting adversarial and out-of-dis...

Jinsol Lee, et al. ∙

Georgia Institute of Technology

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