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Found 57 papers, 14 papers with code
A Deep Learning based No-reference Quality Assessment Model for UGC Videos
The proposed model utilizes very sparse frames to extract spatial features and dense frames (i. e. the video chunk) with a very low spatial resolution to extract motion features, which thereby has low computational complexity.
Cyber-Physical Vulnerability Assessment of P2P Energy Exchanges in Active Distribution Networks
Owing to the decreasing costs of distributed energy resources (DERs) as well as decarbonization policies, power systems are undergoing a modernization process.
Hydrogen and Battery Storage Technologies for Low Cost Energy Decarbonization in Distribution Networks
Deep energy decarbonization cannot be achieved without high penetration of renewables.
A Unified Review of Deep Learning for Automated Medical Coding
This review proposes a unified framework to provide a general understanding of the building blocks of medical coding models and summarizes recent advanced models under the proposed framework.
Enhancing Counterfactual Classification via Self-Training
We approach this task as a domain adaptation problem and propose a self-training algorithm which imputes outcomes with categorical values for finite unseen actions in the observational data to simulate a randomized trial through pseudolabeling, which we refer to as Counterfactual Self-Training (CST).
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks
However, the existing N:M algorithms only address the challenge of how to train N:M sparse neural networks in a uniform fashion (i. e. every layer has the same N:M sparsity) and suffer from a significant accuracy drop for high sparsity (i. e. when sparsity > 80\%).
Loss Functions for Discrete Contextual Pricing with Observational Data
To achieve this, we adapt ideas from machine learning with corrupted labels, where we can consider each observed customer's outcome (purchased or not for a prescribed price), as a (known) probabilistic transformation of the customer's valuation.
iShape: A First Step Towards Irregular Shape Instance Segmentation
In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes.
Ranked #1 on
Instance Segmentation
on iShape
Multitask Balanced and Recalibrated Network for Medical Code Prediction
Nevertheless, automated medical coding is still challenging because of the imbalanced class problem, complex code association, and noise in lengthy documents.
Proactive Rolling-Horizon based Scheduling of Hydrogen Systems for Resilient Power Grids
Deploying distributed energy resources (DERs) and other smart grid technologies have increased the complexity of power grids and made them more vulnerable to natural disasters and cyber-physical-human (CPH) threats.
Learning-Based Nonlinear $H^\infty$ Control via Game-Theoretic Differential Dynamic Programming
In this work, we present a learning-based nonlinear $H^\infty$ control algorithm that guarantee system performance under learned dynamics and disturbance estimate.
No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models
Therefore, many related studies such as point cloud quality assessment (PCQA) and mesh quality assessment (MQA) have been carried out to measure the visual quality degradations of 3D models.
Learned Interpretable Residual Extragradient ISTA for Sparse Coding
Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions.
Learning Audio-Visual Dereverberation
The visual environment surrounding a human speaker reveals important cues about the room geometry, materials, and speaker location, all of which influence the precise reverberation effects in the audio stream.
Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics
This paper investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions.
Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos
The proposed VQA framework consists of three modules, the feature extraction module, the quality regression module, and the quality pooling module.
Proactive Scheduling of Hydrogen Systems for Resilience Enhancement of Distribution Networks
Recent advances in smart grid technologies bring opportunities to better control the modern and complex power grids with renewable integration.
End-to-End Jet Classification of Boosted Top Quarks with the CMS Open Data
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon.
Multitask Recalibrated Aggregation Network for Medical Code Prediction
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement.
Deep Consensus Learning
For the real image corresponding to the input layout, its mask also is computed by the inference network, and then used by the generator to reconstruct the real image.
Deep Learning based 3D Segmentation: A Survey
3D object segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis.
Sum-Rate Maximization in Distributed Intelligent Reflecting Surfaces-Aided mmWave Communications
The solution to transmit beamforming at the BS and the phase shifts at the IRS are derived by using the successive convex approximation (SCA)-based algorithm, and a greedy algorithm is proposed to design the IRS switch vector.
Non-uniform Motion Deblurring with Blurry Component Divided Guidance
To tackle the above problems, we present a deep two-branch network to deal with blurry images via a component divided module, which divides an image into two components based on the representation of blurry degree.
Counterfactual Self-Training
We approach this task as a domain adaptation problem and propose a self-training algorithm which imputes outcomes for the unseen actions in the observational data to simulate a randomized trial.
Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs
In this letter, we investigate the uplink of a reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multi-user system.
Multi-Objective PMU Allocation for Resilient Power System Monitoring
Phasor measurement units (PMUs) enable better system monitoring and security enhancement in smart grids.
Fatigue-aware Bandits for Dependent Click Models
Based on user's feedback, the platform learns the relevance of the underlying content as well as the discounting effect due to content fatigue.
Secrecy Rate Maximization for Intelligent Reflecting Surface Aided SWIPT Systems
Simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) are two promising techniques for providing enhanced wireless communication capability and sustainable energy supply to energy-constrained wireless devices.
Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation,Phase Shifts, and Hybrid Beamforming Optimization
Then, we solve the power allocation problem under fixed phase shifts of the RIS and hybrid beamforming.
Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product.
Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis
This paper focuses on a recent emerged task, layout-to-image, to learn generative models that are capable of synthesizing photo-realistic images from spatial layout (i. e., object bounding boxes configured in an image lattice) and style (i. e., structural and appearance variations encoded by latent vectors).
Ranked #2 on
Layout-to-Image Generation
on COCO-Stuff 128x128
Machine learning based co-creative design framework
We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs.
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation
Most learning-based super-resolution (SR) methods aim to recover high-resolution (HR) image from a given low-resolution (LR) image via learning on LR-HR image pairs.
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation.
Ranked #1 on
6D Pose Estimation using RGBD
on YCB-Video
(Mean ADD-S metric)
Making Predictive Coding Networks Generative
This paper studies this phenomenon, and proposes a simple solution that promotes the generation of input samples that resemble the training inputs.
Image Synthesis From Reconfigurable Layout and Style
Despite remarkable recent progress on both unconditional and conditional image synthesis, it remains a long-standing problem to learn generative models that are capable of synthesizing realistic and sharp images from reconfigurable spatial layout (i. e., bounding boxes + class labels in an image lattice) and style (i. e., structural and appearance variations encoded by latent vectors), especially at high resolution.
Ranked #2 on
Layout-to-Image Generation
on COCO-Stuff 64x64
Attentive Normalization
In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous.
Ranked #47 on
Instance Segmentation
on COCO minival
3D Virtual Garment Modeling from RGB Images
The predicted landmarks are used for estimating sizing information of the garment.
A support vector regression-based multi-fidelity surrogate model
Computational simulations with different fidelity have been widely used in engineering design.
Pose Guided Fashion Image Synthesis Using Deep Generative Model
In order to formulate the framework, we employ one generator and two discriminators for image synthesis.
High-low level support vector regression prediction approach (HL-SVR) for data modeling with input parameters of unequal sample sizes
The proposed approach is consisted of low-level SVR models for the input parameters of larger sample sizes and high-level SVR model for the input parameters of smaller sample sizes.
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem
Motivated by the phenomenon that companies introduce new products to keep abreast with customers' rapidly changing tastes, we consider a novel online learning setting where a profit-maximizing seller needs to learn customers' preferences through offering recommendations, which may contain existing products and new products that are launched in the middle of a selling period.
Ranking-Based Autoencoder for Extreme Multi-label Classification
Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers of labels, features and instances could be thousands or millions.
Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue
Based on user feedback, the platform dynamically learns users' abandonment distribution and their valuations of messages to determine the length of the sequence and the order of the messages, while maximizing the cumulative payoff over a horizon of length T. We refer to this online learning task as the sequential choice bandit problem.
Real time backbone for semantic segmentation
The rapid development of autonomous driving in recent years presents lots of challenges for scene understanding.
Learning Spatial Pyramid Attentive Pooling in Image Synthesis and Image-to-Image Translation
In experiments, the proposed SPAP is tested in GANs on the Celeba-HQ-128 dataset~\cite{karras2017progressive}, and tested in CycleGANs on the Image-to-Image translation datasets including the Cityscape dataset~\cite{cordts2016cityscapes}, Facade and Aerial Maps dataset~\cite{zhu2017unpaired}, both obtaining better performance.
Sketching Method for Large Scale Combinatorial Inference
In order to test the hypotheses on their topological structures, we propose two adjacency matrix sketching frameworks: neighborhood sketching and subgraph sketching.
Interpretable Spatio-temporal Attention for Video Action Recognition
Inspired by the observation that humans are able to process videos efficiently by only paying attention where and when it is needed, we propose an interpretable and easy plug-in spatial-temporal attention mechanism for video action recognition.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization System
Based on the extended fingerprint database, the accuracy of indoor localization system can be improved with reduced human effort.
Networking and Internet Architecture
Partial Labeled Gastric Tumor Segmentation via patch-based Reiterative Learning
Gastric cancer is the second leading cause of cancer-related deaths worldwide, and the major hurdle in biomedical image analysis is the determination of the cancer extent.
Towards Interpretable R-CNN by Unfolding Latent Structures
We focus on weakly-supervised extractive rationale generation, that is learning to unfold latent discriminative part configurations of object instances automatically and simultaneously in detection without using any supervision for part configurations.
OpenFermion: The Electronic Structure Package for Quantum Computers
Quantum simulation of chemistry and materials is predicted to be an important application for both near-term and fault-tolerant quantum devices.
Quantum Physics Chemical Physics Computational Physics
Multimodal Content Analysis for Effective Advertisements on YouTube
The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users.
Non-convex Statistical Optimization for Sparse Tensor Graphical Model
We consider the estimation of sparse graphical models that characterize the dependency structure of high-dimensional tensor-valued data.
Stabilized Nearest Neighbor Classifier and Its Statistical Properties
In this paper, we introduce a general measure of classification instability (CIS) to quantify the sampling variability of the prediction made by a classification method.
Consistent selection of tuning parameters via variable selection stability
The key idea is to select the tuning parameters so that the resultant penalized regression model is stable in variable selection.
