Rutgers University

research

∙ 01/14/2020

Anomaly Detection with Density Estimation

We leverage recent breakthroughs in neural density estimation to propose...

Benjamin Nachman, et al. ∙

research

∙ 10/02/2020

Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop

We study the problem of balancing effectiveness and efficiency in automa...

Wei Fan, et al. ∙

research

∙ 02/28/2022

A Multi-scale Transformer for Medical Image Segmentation: Architectures, Model Efficiency, and Benchmarks

Transformers have emerged to be successful in a number of natural langua...

Yunhe Gao, et al. ∙

research

∙ 01/11/2020

Embedding Compression with Isotropic Iterative Quantization

Continuous representation of words is a standard component in deep learn...

Siyu Liao, et al. ∙

research

∙ 02/25/2020

CookGAN: Meal Image Synthesis from Ingredients

In this work we propose a new computational framework, based on generati...

Fangda Han, et al. ∙

research

∙ 09/26/2019

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

In this paper, we study the novel problem of not only predicting ingredi...

Jiatong Li, et al. ∙

research

∙ 11/25/2019

Point Cloud Processing via Recurrent Set Encoding

We present a new permutation-invariant network for 3D point cloud proces...

Pengxiang Wu, et al. ∙

research

∙ 09/09/2019

Learning a Self-inverse Network for Unpaired Bidirectional Image-to-image Translation

Recently image-to-image translation has attracted significant interests ...

Zengming Shen, et al. ∙

research

∙ 05/23/2020

S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation

We propose a sequential variational autoencoder to learn disentangled re...

Yizhe Zhu, et al. ∙

research

∙ 12/17/2021

Learned Half-Quadratic Splitting Network for Magnetic Resonance Image Reconstruction

Magnetic Resonance (MR) image reconstruction from highly undersampled k-...

Bingyu Xin, et al. ∙

research

∙ 04/24/2019

Deep Sparse Representation-based Classification

We present a transductive deep learning-based formulation for the sparse...

Mahdi Abavisani, et al. ∙

research

∙ 06/07/2021

TENGraD: Time-Efficient Natural Gradient Descent with Exact Fisher-Block Inversion

This work proposes a time-efficient Natural Gradient Descent method, cal...

Saeed Soori, et al. ∙

research

∙ 11/20/2019

Object-Guided Instance Segmentation for Biological Images

Instance segmentation of biological images is essential for studying obj...

Jingru Yi, et al. ∙

research

∙ 11/10/2020

An End-to-End Differentiable but Explainable Physics Engine for Tensegrity Robots: Modeling and Control

This work proposes an end-to-end differentiable physics engine for tense...

Kun Wang, et al. ∙

research

∙ 05/20/2021

More Than Just Attention: Learning Cross-Modal Attentions with Contrastive Constraints

Attention mechanisms have been widely applied to cross-modal tasks such ...

Yuxiao Chen, et al. ∙

research

∙ 10/22/2021

Challenges in Procedural Multimodal Machine Comprehension:A Novel Way To Benchmark

We focus on Multimodal Machine Reading Comprehension (M3C) where a model...

Pritish Sahu, et al. ∙

research

∙ 04/22/2019

Learning Feature-to-Feature Translator by Alternating Back-Propagation for Zero-Shot Learning

We investigate learning feature-to-feature translator networks by altern...

Yizhe Zhu, et al. ∙

research

∙ 09/09/2019

Towards Learning a Self-inverse Network for Bidirectional Image-to-image Translation

The one-to-one mapping is necessary for many bidirectional image-to-imag...

Zengming Shen, et al. ∙

research

∙ 09/28/2019

Accelerating the Computation of UCB and Related Indices for Reinforcement Learning

In this paper we derive an efficient method for computing the indices as...

Wesley Cowan, et al. ∙

research

∙ 05/18/2020

Scaling-up Distributed Processing of Data Streams for Machine Learning

Emerging applications of machine learning in numerous areas involve cont...

Matthew Nokleby, et al. ∙

research

∙ 08/18/2020

PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data

The 3D volumetric shape of the heart's left ventricle (LV) myocardium (M...

Meng Ye, et al. ∙

research

∙ 03/30/2021

Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training

Data augmentation has proved extremely useful by increasing training dat...

Yunhe Gao, et al. ∙

research

∙ 12/18/2019

Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data

Automatic segmentation of anatomical landmarks from ultrasound (US) play...

Jeya Maria Jose V., et al. ∙

research

∙ 07/17/2020

OnlineAugment: Online Data Augmentation with Less Domain Knowledge

Data augmentation is one of the most important tools in training modern ...

Zhiqiang Tang, et al. ∙

research

∙ 10/17/2021

AE-StyleGAN: Improved Training of Style-Based Auto-Encoders

StyleGANs have shown impressive results on data generation and manipulat...

Ligong Han, et al. ∙

research

∙ 12/06/2021

Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams

Timeseries partitioning is an essential step in most machine-learning dr...

Tahiya Chowdhury, et al. ∙

research

∙ 11/22/2019

ViewSynth: Learning Local Features from Depth using View Synthesis

We address the problem of jointly detecting keypoints and learning descr...

Jisan Mahmud, et al. ∙

research

∙ 12/11/2021

COMPOSER: Compositional Learning of Group Activity in Videos

Group Activity Recognition (GAR) detects the activity performed by a gro...

Honglu Zhou, et al. ∙

research

∙ 03/15/2020

MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps

The ability to reliably perceive the environmental states, particularly ...

Pengxiang Wu, et al. ∙

research

∙ 08/27/2021

A Guide to Reproducible Research in Signal Processing and Machine Learning

Reproducibility is a growing problem that has been extensively studied a...

Joseph Shenouda, et al. ∙

research

∙ 09/27/2019

ASSD: Attentive Single Shot Multibox Detector

This paper proposes a new deep neural network for object detection. The ...

Jingru Yi, et al. ∙

research

∙ 02/04/2021

SelfNorm and CrossNorm for Out-of-Distribution Robustness

Normalization techniques are crucial in stabilizing and accelerating the...

Zhiqiang Tang, et al. ∙

research

∙ 12/23/2021

Learning to Walk with Dual Agents for Knowledge Graph Reasoning

Graph walking based on reinforcement learning (RL) has shown great succe...

Denghui Zhang, et al. ∙

research

∙ 12/16/2019

CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator

Deep neural networks (DNNs) are vulnerable to adversarial attack despite...

Huy Phan, et al. ∙

research

∙ 02/18/2020

Machine Learning for Motor Learning: EEG-based Continuous Assessment of Cognitive Engagement for Adaptive Rehabilitation Robots

Although cognitive engagement (CE) is crucial for motor learning, it rem...

Neelesh Kumar, et al. ∙

research

∙ 05/02/2020

Are Emojis Emotional? A Study to Understand the Association between Emojis and Emotions

Given the growing ubiquity of emojis in language, there is a need for me...

Abu Awal Md Shoeb, et al. ∙

research

∙ 10/24/2021

Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness

As one of the most popular generative models, Variational Autoencoder (V...

Dazhong Shen, et al. ∙

research

∙ 01/22/2022

Modality Bank: Learn multi-modality images across data centers without sharing medical data

Multi-modality images have been widely used and provide comprehensive in...

Qi Chang, et al. ∙

research

∙ 01/14/2020

Simulation Assisted Likelihood-free Anomaly Detection

Given the lack of evidence for new particle discoveries at the Large Had...

Anders Andreassen, et al. ∙

research

∙ 08/09/2021

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction

Neural painting refers to the procedure of producing a series of strokes...

Songhua Liu, et al. ∙

research

∙ 01/21/2022

Contrastive and Selective Hidden Embeddings for Medical Image Segmentation

Medical image segmentation has been widely recognized as a pivot procedu...

Zhuowei Li, et al. ∙

research

∙ 12/12/2018

Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks

The large domain discrepancy between faces captured in polarimetric (or ...

He Zhang, et al. ∙

research

∙ 03/19/2021

Hopper: Multi-hop Transformer for Spatiotemporal Reasoning

This paper considers the problem of spatiotemporal object-centric reason...

Honglu Zhou, et al. ∙

research

∙ 02/08/2022

Graph-Relational Domain Adaptation

Existing domain adaptation methods tend to treat every domain equally an...

Zihao Xu, et al. ∙

research

∙ 02/28/2022

A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data

Tensegrity robots, composed of rigid rods and flexible cables, are diffi...

Kun Wang, et al. ∙

research

∙ 05/06/2019

Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking

In this paper, we propose a generative framework that unifies depth-base...

Lu Sheng, et al. ∙

research

∙ 08/05/2019

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost

The paper presents Imbalance-XGBoost, a Python package that combines the...

Chen Wang, et al. ∙

research

∙ 10/06/2019

Rethinking Kernel Methods for Node Representation Learning on Graphs

Graph kernels are kernel methods measuring graph similarity and serve as...

Yu Tian, et al. ∙

research

∙ 10/06/2019

Neural Multisensory Scene Inference

For embodied agents to infer representations of the underlying 3D physic...

Jae Hyun Lim, et al. ∙

research

∙ 10/11/2019

Scene-level Pose Estimation for Multiple Instances of Densely Packed Objects

This paper introduces key machine learning operations that allow the rea...

Chaitanya Mitash, et al. ∙

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