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Found 288 papers, 84 papers with code
Toward Knowledge-Enriched Conversational Recommendation Systems
Conversational Recommendation Systems recommend items through language based interactions with users. In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie.
Beyond 3DMM Space: Towards Fine-grained 3D Face Reconstruction
However, most of their training data is constructed by 3D Morphable Model, whose space spanned is only a small part of the shape space.
融合零指代识别的篇章级机器翻译(Context-aware Machine Translation Integrating Zero Pronoun Recognition)
“在汉语等其他有省略代词习惯的语言中, 通常会删掉可从上下文信息推断出的代词。尽管以Transformer为代表的的神经机器翻译模型取得了巨大的成功, 但这种省略现象依旧对神经机器翻译模型造成了很大的挑战。本文在Transformer基础上提出了一个融合零指代识别的翻译模型, 并引入篇章上下文来丰富指代信息。具体地, 该模型采用联合学习的框架, 在翻译模型基础上, 联合了一个分类任务, 即判别句子中省略代词在句子所表示的成分, 使得模型能够融合零指代信息辅助翻译。通过在中英对话数据集上的实验, 验证了本文提出方法的有效性, 与基准模型相比, 翻译性能提升了1. 48个BLEU值。”
Theoretically Accurate Regularization Technique for Matrix Factorization based Recommender Systems
Regularization is a popular technique to solve the overfitting problem of machine learning algorithms.
Probability Distribution of Hypervolume Improvement in Bi-objective Bayesian Optimization
This work provides the exact expression of the probability distribution of the hypervolume improvement (HVI) for bi-objective generalization of Bayesian optimization.
"My nose is running.""Are you also coughing?": Building A Medical Diagnosis Agent with Interpretable Inquiry Logics
In this paper, we explore how to bring interpretability to data-driven DSMD.
High Dimensional Bayesian Optimization with Kernel Principal Component Analysis
Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an acquisition function to suggest candidate points.
RankMat : Matrix Factorization with Calibrated Distributed Embedding and Fairness Enhancement
Matrix Factorization is a widely adopted technique in the field of recommender system.
Extremal GloVe: Theoretically Accurate Distributed Word Embedding by Tail Inference
However, the initial formulation of GloVe is not theoretically sound in two aspects, namely the selection of the weighting function and its power exponent is ad-hoc.
MovieMat: Context-aware Movie Recommendation with Matrix Factorization by Matrix Fitting
In this paper, we take advantage of the MatMat framework, which factorizes matrices by matrix fitting to build a context-aware movie recommender system that is superior to classic matrix factorization and comparable in the fairness metric.
KL-Mat : Fair Recommender System via Information Geometry
Recommender system has intrinsic problems such as sparsity and fairness.
Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks
The destruction to DNNs brought by the adversarial attack sparks the potential that adversarial examples serve as a new protection mechanism for privacy security in social networks.
Causal Transportability for Visual Recognition
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features.
Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms
Particularly, we show that the number of runs used in many benchmarking studies, e. g., the default value of 15 suggested by the COCO environment, can be insufficient to reliably rank algorithms on well-known numerical optimization benchmarks.
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features
In contrast to other recent work on online per-run algorithm selection, we warm-start the second optimizer using information accumulated during the first optimization phase.
Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction
To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i. e., preference topics generation, preference shift modeling, and social influence activation.
Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach
Coverage and capacity are the important metrics for performance evaluation in wireless networks, while the coverage and capacity have several conflicting relationships, e. g. high transmit power contributes to large coverage but high inter-cell interference reduces the capacity performance.
Chaining of Numerical Black-box Algorithms: Warm-Starting and Switching Points
Lastly, with a sensitivity analysis, we find the actual performance gain is hugely affected by the switching point, and in some cases, the switching point yielding the best actual performance differs from the one computed from the theoretical gain.
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape
3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori.
Context-Sensitive Temporal Feature Learning for Gait Recognition
Specifically, CSTL contains an adaptive temporal aggregation module that subsequently performs local relation modeling and global relation modeling to fuse the multi-scale features.
ESCM$^2$: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation
In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion.
DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow
Recently, the dense correlation volume method achieves state-of-the-art performance in optical flow.
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors.
Knowledge Mining with Scene Text for Fine-Grained Recognition
Recently, the semantics of scene text has been proven to be essential in fine-grained image classification.
On Multi-Domain Long-Tailed Recognition, Generalization and Beyond
We formalize the task of Multi-Domain Long-Tailed Recognition (MDLT), which learns from multi-domain imbalanced data, addresses label imbalance, domain shift, and divergent label distributions across domains, and generalizes to all domain-class pairs.
Domain Adaptation with Factorizable Joint Shift
In this paper, we propose a new assumption, Factorizable Joint Shift (FJS), to handle the co-existence of sampling bias in covariates and labels.
Adversarial samples for deep monocular 6D object pose estimation
We also introduce a new U6DA-Linemod dataset for robustness study of the 6D pose estimation task.
Full RGB Just Noticeable Difference (JND) Modelling
Besides, the JND of the red and blue channels are larger than that of the green one according to the experimental results of the proposed model, which demonstrates that more changes can be tolerated in the red and blue channels, in line with the well-known fact that the human visual system is more sensitive to the green channel in comparison with the red and blue ones.
Privacy protection based on mask template
Powerful recognition algorithms are widely used in the Internet or important medical systems, which poses a serious threat to personal privacy.
Graph-Relational Domain Adaptation
In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e. g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure.
ABG: A Multi-Party Mixed Protocol Framework for Privacy-Preserving Cooperative Learning
To address the issue of privacy-preserving in collaborative learning, secure outsourced computation and federated learning are two typical methods.
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems.
Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study
An important application of DL methods is extracting driving safety metrics from traffic imagery.
Improved normal-boundary intersection algorithm: a method for energy optimization strategy in smart buildings
With the widespread use of distributed energy sources, the advantages of smart buildings over traditional buildings are becoming increasingly obvious.
Efficient Data-Plane Memory Scheduling for In-Network Aggregation
However, switch memory is scarce compared to the volume of gradients transmitted in distributed training.
Mutations make pandemics worse or better: modeling SARS-CoV-2 variants and imperfect vaccination
To address the novel dynamics of the disease, we propose a dynamical model of two strains, namely native and mutant, transmission dynamics with mutation and imperfect vaccination.
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples
To alleviate feature suppression, we propose contrastive learning for unsupervised sentence embedding with soft negative samples (SNCSE).
From policy to prediction: Forecasting COVID-19 dynamics under imperfect vaccination
Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model [WWR+].
Automatic Configuration for Optimal Communication Scheduling in DNN Training
ByteScheduler partitions and rearranges tensor transmissions to improve the communication efficiency of distributed Deep Neural Network (DNN) training.
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Spatial-Temporal Graph Information
To solve the MOO problem, we develop a novel DRL algorithm leveraging the graphical information of the distribution network.
Continual Learning with Knowledge Transfer for Sentiment Classification
In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where each task builds a classifier to classify the sentiment of reviews of a particular product category or domain.
Ranked #4 on
Continual Learning
on DSC (10 tasks)
Time-of-use Pricing for Energy Storage Investment
Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals.
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations
We propose a method for learning constraints represented as Gaussian processes (GPs) from locally-optimal demonstrations.
A hypothesis-free bridging of disease dynamics and non-pharmaceutical policies
Typically, a hypothesis is made on the form of the transmission rate with respect to time.
Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification
In this work, we address point detection and time series classification for machine-part interactions with a deep Convolutional Neural Network (CNN) based framework.
ZeroMat: Solving Cold-start Problem of Recommender System with No Input Data
Recommender system is an applicable technique in most E-commerce commercial product technical designs.
MatMat: Matrix Factorization by Matrix Fitting
Our framework is friendly to multitask learning and side information incorporation.
Adaptive label thresholding methods for online multi-label classification
The key feature of our framework is that both scoring and thresholding models are included as important components of the online multi-label classifier and are incorporated into one online optimization problem.
ProtGNN: Towards Self-Explaining Graph Neural Networks
In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs.
Data privacy protection in microscopic image analysis for material data mining
The core contributions are as follows: 1) the federated learning algorithm is introduced into the polycrystalline microstructure image segmentation task to make full use of different user data to carry out machine learning, break the data island and improve the model generalization ability under the condition of ensuring the privacy and security of user data; 2) A data sharing strategy based on style transfer is proposed.
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics
IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other components of IOHprofiler such as IOHanalyzer, the module for interactive performance analysis and visualization.
Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer.
Knowledge Base Question Answering
Knowledge Graph Embedding
+1
E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches
We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness.
Consensus-Based Decentralized Energy Trading for Distributed Energy Resources
In smart grids, distributed energy resources (DERs) have penetrated residential zones to provide a new form of electricity supply, mainly from renewable energy.
Constrained Optimization Involving Nonconvex $\ell_p$ Norms: Optimality Conditions, Algorithm and Convergence
We also derive the sequential optimality conditions for both problems and study the conditions under which these conditions imply the first-order necessary conditions.
Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework
In this work, we show how data labeled with semantically continuous attributes can be utilized to conduct a quantitative evaluation of latent-space interpolation algorithms, for variational autoencoders.
RelationRS: Relationship Representation Network for Object Detection in Aerial Images
To address these issues, we propose a relationship representation network for object detection in aerial images (RelationRS): 1) Firstly, multi-scale features are fused and enhanced by a dual relationship module (DRM) with conditional convolution.
Learning Structural Representations for Recipe Generation and Food Retrieval
We also validate the usefulness of our learned tree structures in the food cross-modal retrieval task, where the proposed model with tree representations can outperform state-of-the-art benchmark results.
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
To bridge this gap, we propose Motif-based Graph Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif generation framework for GNNs.
Improving Load Forecast in Energy Markets During COVID-19
The abrupt outbreak of the COVID-19 pandemic was the most significant event in 2020, which had profound and lasting impacts across the world.
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN
However, it is challenging for GANs to model distributions of separate non-i. i. d.
Interpreting Graph Neural Networks via Unrevealed Causal Learning
This paper proposes a new explanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors.
Collaborative Three-Stream Transformers for Video Captioning
In this way, the three branches of transformers can support each other to exploit the most discriminative semantic information in different modalities for accurate predictions of captions, especially for the subject, predicate, and object parts in a sentence.
Language Models as Recommender Systems: Evaluations and Limitations
Pre-trained language models (PLMs) such as BERT and GPT learn general text representations and encode extensive world knowledge; thus, they can be efficiently and accurately adapted to various downstream tasks.
Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values
We investigate the fairness concerns of training a machine learning model using data with missing values.
PC$^2$-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling
Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for the underlying surface.
Semi-Supervised Wide-Angle Portraits Correction by Multi-Scale Transformer
We propose a semi-supervised network for wide-angle portraits correction.
Critical Learning Periods in Federated Learning
In this work, we show that the final test accuracy of FL is dramatically affected by the early phase of the training process, i. e., FL exhibits critical learning periods, in which small gradient errors can have irrecoverable impact on the final test accuracy.
Niche differentiation in the light spectrum promotes coexistence of phytoplankton species: a spatial modelling approach
The paradox of the plankton highlights the apparent contradiction between Gause's law of competitive exclusion and the observed diversity of phytoplankton.
Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing
Compared with image scene parsing, video scene parsing introduces temporal information, which can effectively improve the consistency and accuracy of prediction.
Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging
Multi-spectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Accelerating Serverless Computing by Harvesting Idle Resources
Serverless computing automates fine-grained resource scaling and simplifies the development and deployment of online services with stateless functions.
Multi-Modulation Network for Audio-Visual Event Localization
We propose a novel MultiModulation Network (M2N) to learn the above correlation and leverage it as semantic guidance to modulate the related auditory, visual, and fused features.
Analyze and Design Network Architectures by Recursion Formulas
The effectiveness of shortcut/skip-connection has been widely verified, which inspires massive explorations on neural architecture design.
Cross-Modal Graph with Meta Concepts for Video Captioning
Video captioning targets interpreting the complex visual contents as text descriptions, which requires the model to fully understand video scenes including objects and their interactions.
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
Neural painting refers to the procedure of producing a series of strokes for a given image and non-photo-realistically recreating it using neural networks.
Ranked #1 on
Object Detection
on SIXray
Hyperparameter-free and Explainable Whole Graph Embedding
Graphs can be used to describe complex systems.
Cycle-Consistent Inverse GAN for Text-to-Image Synthesis
In this paper, we propose a novel unified framework of Cycle-consistent Inverse GAN (CI-GAN) for both text-to-image generation and text-guided image manipulation tasks.
A Novel Mathematical Model for Infrastructure Planning of Dynamic Wireless Power Transfer Systems for Electric Vehicles
Electric vehicles (EVs) provide an alternative solution that decreases the dependency on traditional fuels such as gasoline and reduces hazardous gas emissions.
STRODE: Stochastic Boundary Ordinary Differential Equation
In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training.
Self-supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss
To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data.
Adaptive 3D descattering with a dynamic synthesis network
In addition, we show that by training the DSN entirely on simulated data, the network can generalize to experiments and achieve robust 3D descattering.
Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches.
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution
In addition, we propose a deep reinforcement learning scheme with a latitude adaptive reward, in order to automatically select optimal upscaling factors for different latitude bands.
Structured Multi-Level Interaction Network for Video Moment Localization via Language Query
In particular, for cross-modal interaction, we interact the sentence-level query with the whole moment while interact the word-level query with content and boundary, as in a coarse-to-fine manner.
Enhancing Question Generation with Commonsense Knowledge
Question generation (QG) is to generate natural and grammatical questions that can be answered by a specific answer for a given context.
Correcting Exposure Bias for Link Recommendation
Link prediction methods are frequently applied in recommender systems, e. g., to suggest citations for academic papers or friends in social networks.
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation.
Initialization Matters: Regularizing Manifold-informed Initialization for Neural Recommendation Systems
In this work, we propose a new initialization scheme for user and item embeddings called Laplacian Eigenmaps with Popularity-based Regularization for Isolated Data (LEPORID).
DMBGN: Deep Multi-Behavior Graph Networks for Voucher Redemption Rate Prediction
In this paper, we propose a Deep Multi-behavior Graph Networks (DMBGN) to shed light on this field for the voucher redemption rate prediction.
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.
Characterizing Residential Load Patterns by Household Demographic and Socioeconomic Factors
We develop a deep neural network (DNN) to analyze the relationship between users' load patterns and their demographic and socioeconomic features.
Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learning
First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information.
Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction
We demonstrate that our approach can boost the fairness of the algorithm and enhances performance evaluated by traditional metrics.
Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification
Compared with the state-of-the-art methods, our proposed PS-HRNet improves 3. 4\%, 6. 2\%, 2. 5\%, 1. 1\% and 4. 2\% at Rank-1 on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets, respectively.
Zero-Shot Recommender Systems
This poses a chicken-and-egg problem for early-stage products, whose amount of data, in turn, relies on the performance of their RS.
KECRS: Towards Knowledge-Enriched Conversational Recommendation System
The chit-chat-based conversational recommendation systems (CRS) provide item recommendations to users through natural language interactions.
Quantifying the Impact of Boundary Constraint Handling Methods on Differential Evolution
Constraint handling is one of the most influential aspects of applying metaheuristics to real-world applications, which can hamper the search progress if treated improperly.
Learning to Ask Appropriate Questions in Conversational Recommendation
Hence, the ability to generate suitable clarifying questions is the key to timely tracing users' dynamic preferences and achieving successful recommendations.
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech
In contrast to conventional pipeline Spoken Language Understanding (SLU) which consists of automatic speech recognition (ASR) and natural language understanding (NLU), end-to-end SLU infers the semantic meaning directly from speech and overcomes the error propagation caused by ASR.
Automatic Speech Recognition
Natural Language Understanding
+3
Is there Anisotropy in Structural Bias?
We find that anisotropy is very rare, and even in cases where it is present, there are clear tests for SB which do not rely on any assumptions of isotropy, so we can safely expand the suite of SB tests to encompass these kinds of deficiencies not found by the original tests.
Covariance Matrix Adaptation Evolution Strategy Assisted by Principal Component Analysis
Over the past decades, more and more methods gain a giant development due to the development of technology.
Blockchain-Based Decentralized Energy Management Platform for Residential Distributed Energy Resources in A Virtual Power Plant
In this paper, we develop a blockchain-based VPP energy management platform to facilitate a rich set of transactive energy activities among residential users with renewables, energy storage, and flexible loads in a VPP.
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage
This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries.
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants
From our analysis, we saw that the features can classify the CMA-ES variants, or the function groups decently, and show a potential for predicting the performance of those variants.
Machine Learning Approach to Uncovering Residential Energy Consumption Patterns Based on Socioeconomic and Smart Meter Data
We model consumption patterns by representative loads and reveal the relationship between load patterns and socioeconomic characteristics.
An Iteratively Reweighted Method for Sparse Optimization on Nonconvex $\ell_{p}$ Ball
Specifically, if the current iterate is in the interior of the feasible set, then the weighted $\ell_{1}$ ball is formed by linearizing the $\ell_{p}$ norm at the current iterate.
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning
In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes.
Scene Text Retrieval via Joint Text Detection and Similarity Learning
Such a task is usually realized by matching a query text to the recognized words, outputted by an end-to-end scene text spotter.
Adversarial Attacks are Reversible with Natural Supervision
We find that images contain intrinsic structure that enables the reversal of many adversarial attacks.
Any Part of Bayesian Network Structure Learning
We study an interesting and challenging problem, learning any part of a Bayesian network (BN) structure.
Potential Advantages of Peak Picking Multi-Voltage Threshold Digitizer in Energy Determination in Radiation Measurement
After processing 30, 000 randomly-chosen pulses sampled by the oscilloscope with a 22Na point source, our method achieves an energy resolution of 17. 50% within a 450-650 KeV energy window, which is 2. 44% better than the result of traditional MVT with same thresholds; and we get a count number at 15225 in the same energy window while the result of MVT is at 14678.
Towards Efficient Local Causal Structure Learning
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data.
Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules
However, when introducing a new component into an existing algorithm, assessing its potential benefits is a challenging task.
Residual-Aided End-to-End Learning of Communication System without Known Channel
However, the gradient vanishing and overfitting problems of GAN will result in the serious performance degradation of E2E learning of communication system.
Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion
Joint event and causality extraction is a challenging yet essential task in information retrieval and data mining.
Delving into Deep Imbalanced Regression
We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain target values, and generalizing to the entire target range.
Temporal Memory Attention for Video Semantic Segmentation
Video semantic segmentation requires to utilize the complex temporal relations between frames of the video sequence.
Ranked #1 on
Video Semantic Segmentation
on Cityscapes val
Domain Adaptation for Time Series Forecasting via Attention Sharing
Recently, deep neural networks have gained increasing popularity in the field of time series forecasting.
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels
In this paper, we analyze the generalization of models trained by noisy iterative algorithms.
Towards Natural and Controllable Cross-Lingual Voice Conversion Based on Neural TTS Model and Phonetic Posteriorgram
Cross-lingual voice conversion (VC) is an important and challenging problem due to significant mismatches of the phonetic set and the speech prosody of different languages.
Network Clustering for Multi-task Learning
The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representations over all tasks and specific layers are prone to learn specific representations for each task.
Privacy-Preserving Transactive Energy Management for IoT-aided Smart Homes via Blockchain
Further, we design an efficient blockchain system tailored for IoT devices and develop the smart contract to support the holistic transactive energy management system.
Learning Guided Electron Microscopy with Active Acquisition
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection.
Towards An Efficient Approach for the Nonconvex $\ell_p$ Ball Projection: Algorithm and Analysis
This paper primarily focuses on computing the Euclidean projection of a vector onto the $\ell_{p}$ ball in which $p\in(0, 1)$.
Recurrent Exploration Networks for Recommender Systems
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems.
Self-Supervised Vessel Segmentation via Adversarial Learning
Vessel segmentation is critically essential for diagnosinga series of diseases, e. g., coronary artery disease and retinal disease.
HCGrid: A Convolution-based Gridding Framework for RadioAstronomy in Hybrid Computing Environments
Gridding operation, which is to map non-uniform data samples onto a uniformly distributedgrid, is one of the key steps in radio astronomical data reduction process.
Instrumentation and Methods for Astrophysics
Generative Interventions for Causal Learning
We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts.
Catalytically Potent and Selective Clusterzymes for Modulation of Neuroinflammation Through Single-Atom Substitutions
Emerging artificial enzymes with reprogrammed and augmented catalytic activity and substrate selectivity have long been pursued with sustained efforts.
Biological Physics Medical Physics
Squirrel: A Switching Hyperparameter Optimizer
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge.
CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts
Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.
Keyword-Guided Neural Conversational Model
We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast.
Federated Learning for Personalized Humor Recognition
In this paper, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL).
Learning Spatial Attention for Face Super-Resolution
Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e. g., $16\times16$).
On Statistical Analysis of MOEAs with Multiple Performance Indicators
Assessing the empirical performance of Multi-Objective Evolutionary Algorithms (MOEAs) is vital when we extensively test a set of MOEAs and aim to determine a proper ranking thereof.
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification
After each sentiment classification task is learned, its knowledge is retained to help future task learning.
Argumentation Mining on Essays at Multi Scales
Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level.
Face Forgery Detection by 3D Decomposition
Detecting digital face manipulation has attracted extensive attention due to fake media's potential harms to the public.
Learning causal representations for robust domain adaptation
In this paper, we study the cases where at the training phase the target domain data is unavailable and only well-labeled source domain data is available, called robust domain adaptation.
MatRec: Matrix Factorization for Highly Skewed Dataset
Recommender systems is one of the most successful AI technologies applied in the internet cooperations.
Neural Network Design: Learning from Neural Architecture Search
Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interests.
Cooperative Energy Management of HVAC via Transactive Energy
However, how to scale up HVAC energy management for a group of users while persevering users' privacy remains a big challenge.
Activity Detection And Modeling Using Smart Meter Data: Concept And Case Studies
Electricity consumed by residential consumers counts for a significant part of global electricity consumption and utility companies can collect high-resolution load data thanks to the widely deployed advanced metering infrastructure.
COSEA: Convolutional Code Search with Layer-wise Attention
However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes.
Towards Natural Bilingual and Code-Switched Speech Synthesis Based on Mix of Monolingual Recordings and Cross-Lingual Voice Conversion
With these data, three neural TTS models -- Tacotron2, Transformer and FastSpeech are applied for building bilingual and code-switched TTS.
Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples
With the development of deep learning, supervised learning methods perform well in remote sensing images (RSIs) scene classification.
Contract-based Time-of-use Pricing for Energy Storage Investment
We also show that the proposed contracts can reduce the system social cost by over 30%, compared with no storage investment benchmark.
Systems and Control Systems and Control
Detection Defense Against Adversarial Attacks with Saliency Map
Our experimental results of some representative adversarial attacks on common datasets including ImageNet and popular models show that our method can detect all the attacks with high detection success rate effectively.
Structure-Aware Generation Network for Recipe Generation from Images
We investigate an open research task of generating cooking instructions based on only food images and ingredients, which is similar to the image captioning task.
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.
Domain-specific Communication Optimization for Distributed DNN Training
Communication overhead poses an important obstacle to distributed DNN training and draws increasing attention in recent years.
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective
Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug.
Decomposing Generation Networks with Structure Prediction for Recipe Generation
Recipe generation from food images and ingredients is a challenging task, which requires the interpretation of the information from another modality.
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction
Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules.
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Lying at the core of human intelligence, relational thinking is characterized by initially relying on innumerable unconscious percepts pertaining to relations between new sensory signals and prior knowledge, consequently becoming a recognizable concept or object through coupling and transformation of these percepts.
Task-agnostic Temporally Consistent Facial Video Editing
Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.
Continuously Indexed Domain Adaptation
Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
Ranked #1 on
Domain Adaptation
on Rotating MNIST
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
Bayesian Optimization (BO) is a surrogate-assisted global optimization technique that has been successfully applied in various fields, e. g., automated machine learning and design optimization.
Entity-Aware Dependency-Based Deep Graph Attention Network for Comparative Preference Classification
This paper studies the task of comparative preference classification (CPC).
Continuously Index Domain Adaptation
Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
A Modular Hybridization of Particle Swarm Optimization and Differential Evolution
In swarm intelligence, Particle Swarm Optimization (PSO) and Differential Evolution (DE) have been successfully applied in many optimization tasks, and a large number of variants, where novel algorithm operators or components are implemented, has been introduced to boost the empirical performance.
Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case
One of the most challenging problems in evolutionary computation is to select from its family of diverse solvers one that performs well on a given problem.
Benchmarking a $(μ+λ)$ Genetic Algorithm with Configurable Crossover Probability
Moreover, we observe that the ``fast'' mutation scheme with its are power-law distributed mutation strengths outperforms standard bit mutation on complex optimization tasks when it is combined with crossover, but performs worse in the absence of crossover.
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry.
Deep Hierarchical Classification for Category Prediction in E-commerce System
In e-commerce system, category prediction is to automatically predict categories of given texts.
Towards Persona-Based Empathetic Conversational Models
To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
Secret Sharing based Secure Regressions with Applications
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns.
Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries
Actor and action video segmentation with language queries aims to segment out the expression referred objects in the video.
Ranked #7 on
Referring Expression Segmentation
on J-HMDB
Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval
Specifically, with the supervision of original semantic knowledge, PDFD decomposes visual features into domain features and semantic ones, and then the semantic features are projected into common space as retrieval features for ZS-SBIR.
Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism
Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc.
Ranked #5 on
Cross-Modal Retrieval
on Recipe1M
Towards information-rich, logical text generation with knowledge-enhanced neural models
Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life.
