Carnegie Mellon University

research

∙ 08/09/2021

Efficient Hyperparameter Optimization for Differentially Private Deep Learning

Tuning the hyperparameters in the differentially private stochastic grad...

Aman Priyanshu, et al. ∙

research

∙ 06/22/2020

Locally Masked Convolution for Autoregressive Models

High-dimensional generative models have many applications including imag...

Ajay Jain, et al. ∙

research

∙ 02/01/2019

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for...

Nolan Bard, et al. ∙

research

∙ 05/26/2022

Transformer for Partial Differential Equations' Operator Learning

Data-driven learning of partial differential equations' solution operato...

Zijie Li, et al. ∙

research

∙ 07/27/2020

Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning

Diabetic Retinopathy (DR) is a leading cause of blindness in working age...

Qiqi Xiao, et al. ∙

research

∙ 06/29/2020

Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments

We consider three important challenges in conference peer review: (i) re...

Steven Jecmen, et al. ∙

research

∙ 12/27/2019

Combining Deep Learning and Verification for Precise Object Instance Detection

Deep learning object detectors often return false positives with very hi...

Siddharth Ancha, et al. ∙

research

∙ 02/04/2019

Embodied Multimodal Multitask Learning

Recent efforts on training visual navigation agents conditioned on langu...

Devendra Singh Chaplot, et al. ∙

research

∙ 11/11/2019

Self-training with Noisy Student improves ImageNet classification

We present a simple self-training method that achieves 87.4 on ImageNet,...

Qizhe Xie, et al. ∙

research

∙ 02/25/2019

A Theoretical Analysis of Contrastive Unsupervised Representation Learning

Recent empirical works have successfully used unlabeled data to learn fe...

Sanjeev Arora, et al. ∙

research

∙ 09/27/2019

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...

Anahita Mohseni-Kabir, et al. ∙

research

∙ 10/26/2021

Relay Variational Inference: A Method for Accelerated Encoderless VI

Variational Inference (VI) offers a method for approximating intractable...

Amir Zadeh, et al. ∙

research

∙ 12/02/2021

A Fast Knowledge Distillation Framework for Visual Recognition

While Knowledge Distillation (KD) has been recognized as a useful tool i...

Zhiqiang Shen, et al. ∙

research

∙ 03/03/2019

Variational Auto-Decoder: Neural Generative Modeling from Partial Data

Learning a generative model from partial data (data with missingness) is...

Amir Zadeh, et al. ∙

research

∙ 05/08/2019

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

Emotion is intrinsic to humans and consequently emotion understanding is...

Soujanya Poria, et al. ∙

research

∙ 08/30/2019

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

Transformer is a powerful architecture that achieves superior performanc...

Yao-Hung Hubert Tsai, et al. ∙

research

∙ 09/16/2019

Learning Spatial Awareness to Improve Crowd Counting

The aim of crowd counting is to estimate the number of people in images ...

Zhi-Qi Cheng, et al. ∙

research

∙ 12/13/2019

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

This paper studies the problem of predicting the distribution over multi...

Junwei Liang, et al. ∙

research

∙ 06/27/2022

Efficient Deep Learning Using Non-Volatile Memory Technology

Embedded machine learning (ML) systems have now become the dominant plat...

Ahmet Inci, et al. ∙

research

∙ 09/15/2019

Driving in Dense Traffic with Model-Free Reinforcement Learning

Traditional planning and control methods could fail to find a feasible t...

Dhruv Mauria Saxena, et al. ∙

research

∙ 11/22/2019

Factorized Multimodal Transformer for Multimodal Sequential Learning

The complex world around us is inherently multimodal and sequential (con...

Amir Zadeh, et al. ∙

research

∙ 09/14/2021

Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation

Natural language generation (NLG) spans a broad range of tasks, each of ...

Mingkai Deng, et al. ∙

research

∙ 12/19/2019

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

Motivated by the transformative impact of deep neural networks (DNNs) on...

Mahmood Sharif, et al. ∙

research

∙ 09/06/2021

Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

3D human shape and pose estimation is the essential task for human motio...

Ziniu Wan, et al. ∙

research

∙ 07/29/2022

Adaptive Gradient Methods at the Edge of Stability

Very little is known about the training dynamics of adaptive gradient me...

Jeremy M Cohen, et al. ∙

research

∙ 03/30/2019

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

Fine-grained person perception such as body segmentation and pose estima...

Fei Wang, et al. ∙

research

∙ 10/02/2019

A Deep Factorization of Style and Structure in Fonts

We propose a deep factorization model for typographic analysis that dise...

Akshay Srivatsan, et al. ∙

research

∙ 11/26/2019

Behavior Regularized Offline Reinforcement Learning

In reinforcement learning (RL) research, it is common to assume access t...

Yifan Wu, et al. ∙

research

∙ 03/09/2021

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

Conversational Artificial Intelligence (AI) used in industry settings ca...

Tae Wan Kim, et al. ∙

research

∙ 09/06/2019

Show Your Work: Improved Reporting of Experimental Results

Research in natural language processing proceeds, in part, by demonstrat...

Jesse Dodge, et al. ∙

research

∙ 09/26/2019

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

Despite alarm over the reliance of machine learning systems on so-called...

Divyansh Kaushik, et al. ∙

research

∙ 10/01/2019

The Non-IID Data Quagmire of Decentralized Machine Learning

Many large-scale machine learning (ML) applications need to train ML mod...

Kevin Hsieh, et al. ∙

research

∙ 03/31/2020

Explosive Proofs of Mathematical Truths

Mathematical proofs are both paradigms of certainty and some of the most...

Scott Viteri, et al. ∙

research

∙ 06/04/2022

Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation

Data augmentation has been proven to be an effective technique for devel...

Haohan Wang, et al. ∙

research

∙ 12/11/2019

Photosequencing of Motion Blur using Short and Long Exposures

Photosequencing aims to transform a motion blurred image to a sequence o...

Vijay Rengarajan, et al. ∙

research

∙ 11/07/2021

Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines

Input pipelines, which ingest and transform input data, are an essential...

Michael Kuchnik, et al. ∙

research

∙ 09/30/2019

Single-Network Whole-Body Pose Estimation

We present the first single-network approach for 2D whole-body pose esti...

Gines Hidalgo, et al. ∙

research

∙ 11/12/2019

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

Monitoring physiological responses to hemodynamic stress can help in det...

Chufan Gao, et al. ∙

research

∙ 04/16/2020

Geometry-Aware Gradient Algorithms for Neural Architecture Search

Many recent state-of-the-art methods for neural architecture search (NAS...

Liam Li, et al. ∙

research

∙ 08/22/2020

Few-Shot Learning with Intra-Class Knowledge Transfer

We consider the few-shot classification task with an unbalanced dataset,...

Vivek Roy, et al. ∙

research

∙ 01/19/2021

Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

Two-dimensional nanomaterials, such as graphene, have been extensively s...

Yuyang Wang, et al. ∙

research

∙ 03/04/2021

Anycost GANs for Interactive Image Synthesis and Editing

Generative adversarial networks (GANs) have enabled photorealistic image...

Ji Lin, 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

∙ 12/06/2018

Online Model Distillation for Efficient Video Inference

High-quality computer vision models typically address the problem of und...

Ravi Teja Mullapudi, et al. ∙

research

∙ 04/14/2019

UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Humor is a unique and creative communicative behavior displayed during s...

Md. Kamrul Hasan, et al. ∙

research

∙ 09/02/2019

A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text

When trained effectively, the Variational Autoencoder (VAE) is both a po...

Bohan Li, et al. ∙

research

∙ 03/31/2022

A survey of neural models for the automatic analysis of conversation: Towards a better integration of the social sciences

Some exciting new approaches to neural architectures for the analysis of...

Chloé Clavel, et al. ∙

research

∙ 12/01/2019

Adversary A3C for Robust Reinforcement Learning

Asynchronous Advantage Actor Critic (A3C) is an effective Reinforcement ...

Zhaoyuan Gu, et al. ∙

research

∙ 07/30/2020

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...

Harrison Wilde, et al. ∙

research

∙ 02/18/2019

Regularizing Black-box Models for Improved Interpretability

Most work on interpretability in machine learning has focused on designi...

Gregory Plumb, et al. ∙

Carnegie Mellon University

Featured Researchers