Search Results for author:
Found 48 papers, 8 papers with code
CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset
Cross-lingual summarization, which produces the summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation
Seq2seq language generation models that are trained offline with multiple domains in a sequential fashion often suffer from catastrophic forgetting.
Personalized Search-based Query Rewrite System for Conversational AI
Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect.
Recursive Generalization Transformer for Image Super-Resolution
In this work, we propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images.
IDA: Informed Domain Adaptive Semantic Segmentation
To improve the UDA-SS performance, we propose an Informed Domain Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance, which aims to emphasize small-region semantics during mixup.
SePaint: Semantic Map Inpainting via Multinomial Diffusion
Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the current sensing range or resolution.
Drugs Resistance Analysis from Scarce Health Records via Multi-task Graph Representation
In this paper, we propose a novel framework for rapid clinical intervention by viewing health records as graphs whose nodes are mapped from medical events and edges as correspondence between events in given a time window.
KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting
Query Rewriting (QR) plays a critical role in large-scale dialogue systems for reducing frictions.
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence
Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickly.
Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks
Federated Learning (FL) is a collaborative machine learning (ML) framework that combines on-device training and server-based aggregation to train a common ML model among distributed agents.
Cross Aggregation Transformer for Image Restoration
The core of our CAT is the Rectangle-Window Self-Attention (Rwin-SA), which utilizes horizontal and vertical rectangle window attention in different heads parallelly to expand the attention area and aggregate the features cross different windows.
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations
In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server at the same time.
StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints
Neural Radiance Fields (NeRF) achieve photo-realistic view synthesis with densely captured input images.
Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder
As such, existing methods often impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy.
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning
Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entropy and sparse entropy.
$q$-Munchausen Reinforcement Learning
The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the current stochastic policy.
Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling
In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment.
Multi-Tier Platform for Cognizing Massive Electroencephalogram
A spiking neural network (SNN) based tier is designed to distill the principle information in terms of spike-streams from the rare features, which maintains the temporal implication in the nature of EEGs.
Automated Sleep Staging via Parallel Frequency-Cut Attention
This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance.
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring
Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG).
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data
In this paper, we propose to investigate automatic subtyping from an unsupervised learning perspective by directly constructing the underlying data distribution itself, hence sufficient data can be generated to alleviate the issue of overfitting.
On-Demand AoI Minimization in Resource-Constrained Cache-Enabled IoT Networks with Energy Harvesting Sensors
We consider a resource-constrained IoT network, where multiple users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an energy harvesting sensor.
Polyline Generative Navigable Space Segmentation for Autonomous Visual Navigation
Detecting navigable space is a fundamental capability for mobile robots navigating in unknown or unmapped environments.
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning
Federated Learning (FL) is a newly emerged decentralized machine learning (ML) framework that combines on-device local training with server-based model synchronization to train a centralized ML model over distributed nodes.
SPSA-Based Successive Beamforming for Mobile Satellite Receivers with Phased Arrays
Efficient and low-complexity beamforming design is an important element of satellite communication systems with mobile receivers equipped with phased arrays.
Theoretical analysis on the transient ignition of premixed expanding flame in a quiescent mixture
It is found that as the heating power grows, the memory effect becomes increasingly important and it can greatly reduce the minimum ignition energy.
Fluid Dynamics
Self-Supervised Transfer Learning for Hand Mesh Recovery From Binocular Images
Traditional methods for RGB hand mesh recovery usually need to train a separate model for each dataset with the corresponding ground truth and are hardly adapted to new scenarios without the ground truth for supervision.
Consensus-Based Distributed Computation of Link-Based Network Metrics
Average consensus algorithms have wide applications in distributed computing systems where all the nodes agree on the average value of their initial states by only exchanging information with their local neighbors.
Distributed Computing
Distributed, Parallel, and Cluster Computing
Social and Information Networks
Signal Processing
Time-Optimal Guidance to Intercept Moving Targets by Dubins Vehicles
When the target's velocity is constant, by employing the geometric properties, those 4 candidates are transformed to a class of sufficiently smooth real-valued functions.
Optimization and Control
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation
First, ForceReader proposes a novel solution called the Attention Separation Representation to respond to attention deconcentration.
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
Hybrid Ranking Network for Text-to-SQL
In this paper, we study how to leverage pre-trained language models in Text-to-SQL.
Neural Stochastic Block Model & Scalable Community-Based Graph Learning
Compared with SBM, our framework is flexible, naturally allows soft labels and digestion of complex node attributes.
An Ontology-driven Treatment Article Retrieval System for Precision Oncology
This paper presents an ontology-driven treatment article retrieval system developed and experimented using the data and ground truths provided by the TREC 2017 precision medicine track.
Pre-Training for Query Rewriting in A Spoken Language Understanding System
Then, inspired by the wide success of pre-trained contextual language embeddings, and also as a way to compensate for insufficient QR training data, we propose a language-modeling (LM) based approach to pre-train query embeddings on historical user conversation data with a voice assistant.
Optimizing Information Freshness in a Multiple Access Channel with Heterogeneous Devices
We formulate an optimization problem that aims at minimizing the average age of information (AoI) of the EH node subject to the queue stability condition of the grid-connected node.
Information Theory Networking and Internet Architecture Information Theory
Creating Navigable Space from Sparse Noisy Map Points
We present a framework for creating navigable space from sparse and noisy map points generated by sparse visual SLAM methods.
Robotics
Large-Scale Joint Topic, Sentiment & User Preference Analysis for Online Reviews
This paper presents a non-trivial reconstruction of a previous joint topic-sentiment-preference review model TSPRA with stick-breaking representation under the framework of variational inference (VI) and stochastic variational inference (SVI).
Fast Botnet Detection From Streaming Logs Using Online Lanczos Method
Botnet, a group of coordinated bots, is becoming the main platform of malicious Internet activities like DDOS, click fraud, web scraping, spam/rumor distribution, etc.
Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews
TSPRA combines topics (i. e. product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as "critical aspect" analysis altogether in one framework.
Correlated Anomaly Detection from Large Streaming Data
Correlated anomaly detection (CAD) from streaming data is a type of group anomaly detection and an essential task in useful real-time data mining applications like botnet detection, financial event detection, industrial process monitor, etc.
Detecting and Explaining Causes From Text For a Time Series Event
Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.
Knowledge Graph Embedding by Translating on Hyperplanes
Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling.
