Carnegie Mellon University

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943583 Carnegie Mellon University is a private nonprofit research university based in Pittsburgh, Pennsylvania.

Locally Masked Convolution for Autoregressive Models

High-dimensional generative models have many applications including imag...
06/22/2020 ∙ by Ajay Jain, et al. ∙ 61 ∙ share

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The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for...
02/01/2019 ∙ by Nolan Bard, et al. ∙ 54 ∙ share

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Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning

Diabetic Retinopathy (DR) is a leading cause of blindness in working age...
07/27/2020 ∙ by Qiqi Xiao, et al. ∙ 48 ∙ share

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Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

We consider three important challenges in conference peer review: (i) re...
06/29/2020 ∙ by Steven Jecmen, et al. ∙ 41 ∙ share

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Combining Deep Learning and Verification for Precise Object Instance Detection

Deep learning object detectors often return false positives with very hi...
12/27/2019 ∙ by Siddharth Ancha, et al. ∙ 38 ∙ share

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Embodied Multimodal Multitask Learning

Recent efforts on training visual navigation agents conditioned on langu...
02/04/2019 ∙ by Devendra Singh Chaplot, et al. ∙ 36 ∙ share

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Self-training with Noisy Student improves ImageNet classification

We present a simple self-training method that achieves 87.4 on ImageNet,...
11/11/2019 ∙ by Qizhe Xie, et al. ∙ 36 ∙ share

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A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Recent empirical works have successfully used unlabeled data to learn fe...
02/25/2019 ∙ by Sanjeev Arora, et al. ∙ 34 ∙ share

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Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals

In a multi-agent setting, the optimal policy of a single agent is largel...
09/27/2019 ∙ by Anahita Mohseni-Kabir, et al. ∙ 34 ∙ share

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Variational Auto-Decoder: Neural Generative Modeling from Partial Data

Learning a generative model from partial data (data with missingness) is...
03/03/2019 ∙ by Amir Zadeh, et al. ∙ 32 ∙ share

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Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

Emotion is intrinsic to humans and consequently emotion understanding is...
05/08/2019 ∙ by Soujanya Poria, et al. ∙ 32 ∙ share

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Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel

Transformer is a powerful architecture that achieves superior performanc...
08/30/2019 ∙ by Yao-Hung Hubert Tsai, et al. ∙ 32 ∙ share

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Learning Spatial Awareness to Improve Crowd Counting

The aim of crowd counting is to estimate the number of people in images ...
09/16/2019 ∙ by Zhi-Qi Cheng, et al. ∙ 32 ∙ share

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The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

This paper studies the problem of predicting the distribution over multi...
12/13/2019 ∙ by Junwei Liang, et al. ∙ 32 ∙ share

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Driving in Dense Traffic with Model-Free Reinforcement Learning

Traditional planning and control methods could fail to find a feasible t...
09/15/2019 ∙ by Dhruv Mauria Saxena, et al. ∙ 31 ∙ share

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Factorized Multimodal Transformer for Multimodal Sequential Learning

The complex world around us is inherently multimodal and sequential (con...
11/22/2019 ∙ by Amir Zadeh, et al. ∙ 31 ∙ share

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Optimization-Guided Binary Diversification to Mislead Neural Networks for Malware Detection

Motivated by the transformative impact of deep neural networks (DNNs) on...
12/19/2019 ∙ by Mahmood Sharif, et al. ∙ 30 ∙ share

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Person-in-WiFi: Fine-grained Person Perception using WiFi

Fine-grained person perception such as body segmentation and pose estima...
03/30/2019 ∙ by Fei Wang, et al. ∙ 28 ∙ share

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A Deep Factorization of Style and Structure in Fonts

We propose a deep factorization model for typographic analysis that dise...
10/02/2019 ∙ by Akshay Srivatsan, et al. ∙ 28 ∙ share

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Behavior Regularized Offline Reinforcement Learning

In reinforcement learning (RL) research, it is common to assume access t...
11/26/2019 ∙ by Yifan Wu, et al. ∙ 28 ∙ share

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When is it permissible for artificial intelligence to lie? A trust-based approach

Conversational Artificial Intelligence (AI) used in industry settings ca...
03/09/2021 ∙ by Tae Wan Kim, et al. ∙ 28 ∙ share

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Show Your Work: Improved Reporting of Experimental Results

Research in natural language processing proceeds, in part, by demonstrat...
09/06/2019 ∙ by Jesse Dodge, et al. ∙ 26 ∙ share

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Learning the Difference that Makes a Difference with Counterfactually-Augmented Data

Despite alarm over the reliance of machine learning systems on so-called...
09/26/2019 ∙ by Divyansh Kaushik, et al. ∙ 25 ∙ share

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The Non-IID Data Quagmire of Decentralized Machine Learning

Many large-scale machine learning (ML) applications need to train ML mod...
10/01/2019 ∙ by Kevin Hsieh, et al. ∙ 25 ∙ share

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Explosive Proofs of Mathematical Truths

Mathematical proofs are both paradigms of certainty and some of the most...
03/31/2020 ∙ by Scott Viteri, et al. ∙ 25 ∙ share

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Photosequencing of Motion Blur using Short and Long Exposures

Photosequencing aims to transform a motion blurred image to a sequence o...
12/11/2019 ∙ by Vijay Rengarajan, et al. ∙ 24 ∙ share

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Single-Network Whole-Body Pose Estimation

We present the first single-network approach for 2D whole-body pose esti...
09/30/2019 ∙ by Gines Hidalgo, et al. ∙ 23 ∙ share

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Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning

Monitoring physiological responses to hemodynamic stress can help in det...
11/12/2019 ∙ by Chufan Gao, et al. ∙ 23 ∙ share

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Geometry-Aware Gradient Algorithms for Neural Architecture Search

Many recent state-of-the-art methods for neural architecture search (NAS...
04/16/2020 ∙ by Liam Li, et al. ∙ 23 ∙ share

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Few-Shot Learning with Intra-Class Knowledge Transfer

We consider the few-shot classification task with an unbalanced dataset,...
08/22/2020 ∙ by Vivek Roy, et al. ∙ 23 ∙ share

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Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

Two-dimensional nanomaterials, such as graphene, have been extensively s...
01/19/2021 ∙ by Yuyang Wang, et al. ∙ 23 ∙ share

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Anycost GANs for Interactive Image Synthesis and Editing

Generative adversarial networks (GANs) have enabled photorealistic image...
03/04/2021 ∙ by Ji Lin, et al. ∙ 23 ∙ share

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TarMAC: Targeted Multi-Agent Communication

We explore a collaborative multi-agent reinforcement learning setting wh...
10/26/2018 ∙ by Abhishek Das, et al. ∙ 22 ∙ share

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Online Model Distillation for Efficient Video Inference

High-quality computer vision models typically address the problem of und...
12/06/2018 ∙ by Ravi Teja Mullapudi, et al. ∙ 22 ∙ share

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UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Humor is a unique and creative communicative behavior displayed during s...
04/14/2019 ∙ by Md. Kamrul Hasan, et al. ∙ 22 ∙ share

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A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text

When trained effectively, the Variational Autoencoder (VAE) is both a po...
09/02/2019 ∙ by Bohan Li, et al. ∙ 22 ∙ share

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Adversary A3C for Robust Reinforcement Learning

Asynchronous Advantage Actor Critic (A3C) is an effective Reinforcement ...
12/01/2019 ∙ by Zhaoyuan Gu, et al. ∙ 21 ∙ share

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A Recommendation and Risk Classification System for Connecting Rough Sleepers to Essential Outreach Services

Rough sleeping is a chronic problem faced by some of the most disadvanta...
07/30/2020 ∙ by Harrison Wilde, et al. ∙ 21 ∙ share

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Regularizing Black-box Models for Improved Interpretability

Most work on interpretability in machine learning has focused on designi...
02/18/2019 ∙ by Gregory Plumb, et al. ∙ 20 ∙ share

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MAME : Model-Agnostic Meta-Exploration

Meta-Reinforcement learning approaches aim to develop learning procedure...
11/11/2019 ∙ by Swaminathan Gurumurthy, et al. ∙ 20 ∙ share

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Deep Multivariate Mixture of Gaussians for Object Detection under Occlusion

In this paper, we consider the problem of detecting object under occlusi...
11/24/2019 ∙ by Yihui He, et al. ∙ 20 ∙ share

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Estimating 3D Camera Pose from 2D Pedestrian Trajectories

We consider the task of re-calibrating the 3D pose of a static surveilla...
12/12/2019 ∙ by Yan Xu, et al. ∙ 20 ∙ share

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Learning from Positive and Unlabeled Data by Identifying the Annotation Process

In binary classification, Learning from Positive and Unlabeled data (LeP...
03/02/2020 ∙ by Naji Shajarisales, et al. ∙ 20 ∙ share

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Minimizing FLOPs to Learn Efficient Sparse Representations

Deep representation learning has become one of the most widely adopted a...
04/12/2020 ∙ by Biswajit Paria, et al. ∙ 20 ∙ share

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MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

Given multiple input signals, how can we infer node importance in a know...
06/22/2020 ∙ by Namyong Park, et al. ∙ 20 ∙ share

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Universal Inference Using the Split Likelihood Ratio Test

We propose a general method for constructing hypothesis tests and confid...
12/24/2019 ∙ by Larry Wasserman, et al. ∙ 19 ∙ share

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TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting

We present a lightweight video motion retargeting approach TransMoMo tha...
03/31/2020 ∙ by Zhuoqian Yang, et al. ∙ 19 ∙ share

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Towards Better Interpretability in Deep Q-Networks

Deep reinforcement learning techniques have demonstrated superior perfor...
09/15/2018 ∙ by Raghuram Mandyam Annasamy, et al. ∙ 18 ∙ share

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The Laplacian in RL: Learning Representations with Efficient Approximations

The smallest eigenvectors of the graph Laplacian are well-known to provi...
10/10/2018 ∙ by Yifan Wu, et al. ∙ 18 ∙ share

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Adaptive Semantic Segmentation with a Strategic Curriculum of Proxy Labels

Training deep networks for semantic segmentation requires annotation of ...
11/08/2018 ∙ by Kashyap Chitta, et al. ∙ 18 ∙ share

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