Search Results for author:
Found 342 papers, 122 papers with code
Detecting Beneficial Feature Interactions for Recommender Systems
To make the best out of feature interactions, we propose a graph neural network approach to effectively model them, together with a novel technique to automatically detect those feature interactions that are beneficial in terms of recommendation accuracy.
CGNet: A Light-weight Context Guided Network for Semantic Segmentation
To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight and efficient network for semantic segmentation.
Ranked #8 on
Semantic Segmentation
on EventScape
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
BARS: Towards Open Benchmarking for Recommender Systems
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
Ranked #10 on
Semantic Parsing
on spider
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
Improving Text-to-SQL Evaluation Methodology
Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work.
Ranked #1 on
SQL Parsing
on IMDb
ESPRIT: Explaining Solutions to Physical Reasoning Tasks
Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions.
ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks
Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article's impacts on research community.
Ranked #1 on
Scientific Document Summarization
on CL-SciSumm
Neural Re-ranking in Multi-stage Recommender Systems: A Review
As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications.
SParC: Cross-Domain Semantic Parsing in Context
The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
We focus on the cross-domain context-dependent text-to-SQL generation task.
Ranked #5 on
Text-To-SQL
on SParC
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
Ranked #8 on
Dialogue State Tracking
on CoSQL
DART: Open-Domain Structured Data Record to Text Generation
Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures.
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
Ranked #7 on
Text-To-SQL
on SParC
TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data.
Ranked #2 on
Code Generation
on WikiSQL
ISNet: Shape Matters for Infrared Small Target Detection
TOAA block calculates the low-level information with attention mechanism in both row and column directions and fuses it with the high-level information to capture the shape characteristic of targets and suppress noises.
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning
Named Entity Recognition (NER) in Few-Shot setting is imperative for entity tagging in low resource domains.
Ranked #7 on
Few-shot NER
on Few-NERD (INTRA)
(using extra training data)
Selective Annotation Makes Language Models Better Few-Shot Learners
Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time.
FOLIO: Natural Language Reasoning with First-Order Logic
We present FOLIO, a human-annotated, open-domain, and logically complex and diverse dataset for reasoning in natural language (NL), equipped with first order logic (FOL) annotations.
Visual-Language Prompt Tuning with Knowledge-guided Context Optimization
Representative CoOp-based work combines the learnable textual tokens with the class tokens to obtain specific textual knowledge.
FeTaQA: Free-form Table Question Answering
Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to include questions that require complex reasoning and integration of information due to the constraint of the associated short-form answers.
Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents
To the best of our knowledge, Summ$^N$ is the first multi-stage split-then-summarize framework for long input summarization.
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Transformer-based models have achieved state-of-the-art performance on short-input summarization.
Neural Graph Matching based Collaborative Filtering
User and item attributes are essential side-information; their interactions (i. e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems.
Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning
Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future.
Distilling Object Detectors with Feature Richness
Second, these methods imitate some features which are mistakenly regarded as the background by the teacher detector.
Outpainting by Queries
Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision.
SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement
Image-adaptive lookup tables (LUTs) have achieved great success in real-time image enhancement tasks due to their high efficiency for modeling color transforms.
Ranked #5 on
Image Enhancement
on MIT-Adobe 5k
(PSNR on proRGB metric)
MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data
Numerical reasoning over hybrid data containing both textual and tabular content (e. g., financial reports) has recently attracted much attention in the NLP community.
Adaptive Graph Auto-Encoder for General Data Clustering
Therefore, how to extend graph convolution networks into general clustering tasks is an attractive problem.
A Novel SCA-Based Method for Beamforming Optimization in IRS/RIS-Assisted MU-MISO Downlink
In this letter, we consider the fundamental problem of jointly designing the transmit beamformers and the phase-shifts of the intelligent reflecting surface (IRS) / reconfigurable intelligent surface (RIS) to minimize the transmit power, subject to quality-of-service constraints at individual users in an IRS-assisted multiuser multiple-input single-output downlink communication system.
UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization
Our approach introduces a Temporal Feature Abnormal Attention (TFAA) module based on temporal feature reconstruction to enhance the detection of temporal differences.
Ranked #1 on
Temporal Forgery Localization
on LAV-DF
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL
Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any input.
Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models
In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items.
GraphDialog: Integrating Graph Knowledge into End-to-End Task-Oriented Dialogue Systems
End-to-end task-oriented dialogue systems aim to generate system responses directly from plain text inputs.
Prototypical Representation Learning for Relation Extraction
This approach allows us to learn meaningful, interpretable prototypes for the final classification.
A Benchmark and Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning
In the last few years, the interest in knowledge bases has grown exponentially in both the research community and the industry due to their essential role in AI applications.
MACSum: Controllable Summarization with Mixed Attributes
We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning.
Drug Repurposing for COVID-19 via Knowledge Graph Completion
The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials.
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation
We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation.
Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos.
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.
Resource allocation for text semantic communications
Semantic communications have shown its great potential to improve the transmission reliability, especially in the low signal-to-noise regime.
Logic-Consistency Text Generation from Semantic Parses
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries.
Dirichlet-based Uncertainty Calibration for Active Domain Adaptation
Active domain adaptation (DA) aims to maximally boost the model adaptation on a new target domain by actively selecting limited target data to annotate, whereas traditional active learning methods may be less effective since they do not consider the domain shift issue.
This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
In this paper, we propose and study the task of email subject line generation: automatically generating an email subject line from the email body.
Ranked #2 on
Abstractive Text Summarization
on AESLC
An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation
To obtain a transparent reasoning process, we introduce neuro-symbolic to perform explicit reasoning that justifies model decisions by reasoning chains.
Compressed Interaction Graph based Framework for Multi-behavior Recommendation
However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''.
Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems
Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions.
Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations
Biomedical literature is growing rapidly, making it challenging to curate and extract knowledge manually.
Graph Neural Networks with Diverse Spectral Filtering
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their local contexts.
#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate
We collect a large dataset of tweets during the 1st U. S. Presidential Debate in 2016 (#DebateNight) and we analyze its 1. 5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness.
Social and Information Networks
LGD-GCN: Local and Global Disentangled Graph Convolutional Networks
}, we introduce a novel Local and Global Disentangled Graph Convolutional Network (LGD-GCN) to capture both local and global information for graph disentanglement.
WGCN: Graph Convolutional Networks with Weighted Structural Features
Based on nodes' geometrical relationships in the latent space, WGCN differentiates latent, in-, and out-neighbors with an attention-based geometrical aggregation.
Generalised Image Outpainting with U-Transformer
In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalised image outpainting problem.
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions.
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning
Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years.
Efficient Non-parametric Bayesian Hawkes Processes
On two large-scale Twitter diffusion datasets, we show that our methods outperform the current state-of-the-art in goodness-of-fit and that the time complexity is linear in the size of the dataset.
ANPL: Towards Natural Programming with Interactive Decomposition
We deploy ANPL on the Abstraction and Reasoning Corpus (ARC), a set of unique tasks that are challenging for state-of-the-art AI systems, showing it outperforms baseline programming systems that (a) without the ability to decompose tasks interactively and (b) without the guarantee that the modules can be correctly composed together.
Ranked #4 on
Code Generation
on HumanEval
Relational Contrastive Learning for Scene Text Recognition
We argue that such prior contextual information can be interpreted as the relations of textual primitives due to the heterogeneous text and background, which can provide effective self-supervised labels for representation learning.
Multi-domain Recommendation with Embedding Disentangling and Domain Alignment
We propose a new MDR method named EDDA with two key components, i. e., embedding disentangling recommender and domain alignment, to tackle the two challenges respectively.
Evaluating LLMs at Detecting Errors in LLM Responses
This work introduces ReaLMistake, the first error detection benchmark consisting of objective, realistic, and diverse errors made by LLMs.
Towards Better Understanding of User Satisfaction in Open-Domain Conversational Search
This raises the question of how to accurately model user satisfaction in conversational search scenarios.
From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion
While weakly supervised multi-view face reconstruction (MVR) is garnering increased attention, one critical issue still remains open: how to effectively fuse multiple image information to reconstruct high-precision 3D models.
Multi-Level Interaction Reranking with User Behavior History
MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list.
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples
Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills.
Ranked #3 on
Semantic Parsing
on WikiSQL
(Denotation accuracy (test) metric)
MESED: A Multi-modal Entity Set Expansion Dataset with Fine-grained Semantic Classes and Hard Negative Entities
The Entity Set Expansion (ESE) task aims to expand a handful of seed entities with new entities belonging to the same semantic class.
Multi View Spatial-Temporal Model for Travel Time Estimation
Specifically, we use graph2vec to model the spatial view, dual-channel temporal module to model the trajectory view, and structural embedding to model traffic semantics.
Improving Transferability for Domain Adaptive Detection Transformers
We implement the findings and the alignment modules into our adaptation method, and it benchmarks the DETR-style detector on the domain shift settings.
Hermes: Unlocking Security Analysis of Cellular Network Protocols by Synthesizing Finite State Machines from Natural Language Specifications
In this paper, we present Hermes, an end-to-end framework to automatically generate formal representations from natural language cellular specifications.
XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations
However, existing CLSP models are separately proposed and evaluated on datasets of limited tasks and applications, impeding a comprehensive and unified evaluation of CLSP on a diverse range of NLs and MRs. To this end, we present XSemPLR, a unified benchmark for cross-lingual semantic parsing featured with 22 natural languages and 8 meaning representations by examining and selecting 9 existing datasets to cover 5 tasks and 164 domains.
KnowledgeMath: Knowledge-Intensive Math Word Problem Solving in Finance Domains
We introduce KnowledgeMath, a novel benchmark designed to evaluate LLMs' capabilities in applying financial knowledge to solve complex math word problems.
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure
Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples.
Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
As feature interactions bring in non-linearity, they are widely adopted to improve the performance of CTR prediction models.
Ranked #1 on
Click-Through Rate Prediction
on Avazu
Fair Abstractive Summarization of Diverse Perspectives
However, current work in summarization metrics and Large Language Models (LLMs) evaluation has not explored fair abstractive summarization.
TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model
However, those textual tokens have a limited generalization ability regarding unseen domains, as they cannot dynamically adjust to the distribution of testing classes.
Context Shift Reduction for Offline Meta-Reinforcement Learning
In this paper, we propose a novel approach called Context Shift Reduction for OMRL (CSRO) to address the context shift problem with only offline datasets.
Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Then, another VFM trained on fine-grained 2D masks is adopted to guide the generation of semantically augmented images and point clouds to enhance the performance of neural networks, which mix the data from source and target domains like view frustums (FrustumMixing).
A Survey on Self-Supervised Pre-Training of Graph Foundation Models: A Knowledge-Based Perspective
Graph self-supervised learning is now a go-to method for pre-training graph foundation models, including graph neural networks, graph transformers, and more recent large language model (LLM)-based graph models.
Instrumental Variable Regression via Kernel Maximum Moment Loss
We investigate a simple objective for nonlinear instrumental variable (IV) regression based on a kernelized conditional moment restriction (CMR) known as a maximum moment restriction (MMR).
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction
We propose a continuous neural network architecture, termed Explainable Tensorized Neural Ordinary Differential Equations (ETN-ODE), for multi-step time series prediction at arbitrary time points.
Deciphering and integrating invariants for neural operator learning with various physical mechanisms
To this end, we propose Physical Invariant Attention Neural Operator (PIANO) to decipher and integrate the physical invariants (PI) for operator learning from the PDE series with various physical mechanisms.
Improving Model Robustness with Latent Distribution Locally and Globally
The proposed adversarial training with latent distribution (ATLD) method defends against adversarial attacks by crafting LMAEs with the latent manifold in an unsupervised manner.
Optimal Sparse Regression Trees
Regression trees are one of the oldest forms of AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications.
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
To address these limitations, we propose the Monte Carlo Neural PDE Solver (MCNP Solver) for training unsupervised neural solvers via the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles.
FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization
In this paper, we investigate and improve faithfulness in summarization on a broad range of medical summarization tasks.
MT-Ranker: Reference-free machine translation evaluation by inter-system ranking
Traditionally, Machine Translation (MT) Evaluation has been treated as a regression problem -- producing an absolute translation-quality score.
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting
While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily.
Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges
To this end, we propose a more realistic and challenging scenario named open-pose 3D zero-shot classification, focusing on the recognition of 3D objects regardless of their orientation.
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
Moreover, unlike other CNN-based models that analyze sentences locally by sliding windows, our system captures both the dependency information within each sentence and relationships across sentences in the same document.
Variational Inference for Sparse Gaussian Process Modulated Hawkes Process
We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection.
Active and Passive IRS Jointly Aided Communication: Deployment Design and Achievable Rate
In this letter, we study the wireless point-to-point communication from a transmitter (Tx) to a receiver (Rx), which is jointly aided by an active intelligent reflecting surface (AIRS) and a passive IRS (PIRS).
A Bird's-eye View of Reranking: from List Level to Page Level
Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists.
FewSAR: A Few-shot SAR Image Classification Benchmark
Motivated by this observation, we propose a novel few-shot SAR image classification benchmark (FewSAR) to address this issue.
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP).
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments.
Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs
In this paper, we study the problem of addressee and response selection in multi-party conversations.
Consistent and Flexible Selectivity Estimation for High-Dimensional Data
Selectivity estimation aims at estimating the number of database objects that satisfy a selection criterion.
An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.
An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.
Optimal Sparse Survival Trees
Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health.
"Adversarial Examples" for Proof-of-Learning
Jia et al. claimed that an adversary merely knowing the final model and training dataset cannot efficiently find a set of intermediate models with correct data points.
Code Comment Inconsistency Detection with BERT and Longformer
Comments, or natural language descriptions of source code, are standard practice among software developers.
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables
While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in the present continuous methods.
Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses
In this paper, we explore the application of large language models (LLMs) for generating code-tracing questions in introductory programming courses.
A Hybrid Approach to Privacy-Preserving Federated Learning
Federated learning facilitates the collaborative training of models without the sharing of raw data.
Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment
In this paper, we propose to replace the state value function with a novel hindsight value function, which leverages the information from the future to reduce the variance of the gradient estimate for stochastic dynamic environments.
An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages
However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block (i. e., multi-list) style instead of a single list, which requires different assumptions to model user behaviors more accurately.
ConEntail: An Entailment-based Framework for Universal Zero and Few Shot Classification with Supervised Contrastive Pretraining
To fill these gaps, we propose ConEntail, a new framework for universal zero and few shot classification with supervised contrastive pretraining.
Towards Better Robustness against Common Corruptions for Unsupervised Domain Adaptation
Recent studies have investigated how to achieve robustness for unsupervised domain adaptation (UDA).
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
Unfortunately, this requires formatting them into specialized augmented format unknown to the base pretrained language model (PLMs) necessitating finetuning to the target format.
Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering
Coreference resolution aims to identify in a text all mentions that refer to the same real-world entity.
Consensus-Based Transfer Linear Support Vector Machines for Decentralized Multi-Task Multi-Agent Learning
We show that the risks of the target tasks in the nodes without the data of the source tasks can also be reduced using the information transferred from the nodes who contain the data of the source tasks.
A framework with updateable joint images re-ranking for Person Re-identification
In this paper, we propose a novel framework with rules of updating images for person re-identification in real-world surveillance system.
Deep Structured Scene Parsing by Learning with Image Descriptions
This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i. e., a semantic object hierarchy with object interaction relations) that finely accords with human perception.
Matrix completion with deterministic pattern - a geometric perspective
We argue that this condition is necessary for local stability of MRMC solutions, and we show that the condition is generic using the characteristic rank.
Collaborative Learning for Weakly Supervised Object Detection
For object detection, taking WSDDN-like architecture as weakly supervised detector sub-network and Faster-RCNN-like architecture as strongly supervised detector sub-network, we propose an end-to-end Weakly Supervised Collaborative Detection Network.
A Game-Theoretic Approach to Design Secure and Resilient Distributed Support Vector Machines
Distributed Support Vector Machines (DSVM) have been developed to solve large-scale classification problems in networked systems with a large number of sensors and control units.
Museum Exhibit Identification Challenge for Domain Adaptation and Beyond
To achieve robust baselines, we build on a recent approach that aligns per-class scatter matrices of the source and target CNN streams [15].
Deep Learning from Noisy Image Labels with Quality Embedding
However, the label noise among the datasets severely degenerates the \mbox{performance of deep} learning approaches.
Game-Theoretic Design of Secure and Resilient Distributed Support Vector Machines with Adversaries
The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed learning algorithms.
Graph-based Neural Multi-Document Summarization
We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs.
Ranked #1 on
Multi-Document Summarization
on DUC 2004
Robust Tracking Using Region Proposal Networks
Recent advances in visual tracking showed that deep Convolutional Neural Networks (CNN) trained for image classification can be strong feature extractors for discriminative trackers.
TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs
This paper explores the suitability of using automatically discovered topics from MOOC discussion forums for modelling students' academic abilities.
Improving Efficiency of SVM k-fold Cross-validation by Alpha Seeding
The k-fold cross-validation is commonly used to evaluate the effectiveness of SVMs with the selected hyper-parameters.
Cruciform: Solving Crosswords with Natural Language Processing
We describe the results of several of our experiments with the system.
MOOCs Meet Measurement Theory: A Topic-Modelling Approach
Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions.
Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification
Furthermore, each bit of our hashing codes is unequally weighted so that we can manipulate the code lengths by truncating the insignificant bits.
Artwork Identification from Wearable Camera Images for Enhancing Experience of Museum Audiences
We discuss the application of wearable cameras, and the practical and technical challenges in devising a robust system that can recognize artworks viewed by the visitors to create a detailed record of their visit.
Style Separation and Synthesis via Generative Adversarial Networks
The S3-GAN consists of an encoder network, a generator network, and an adversarial network.
Tree-structured Kronecker Convolutional Network for Semantic Segmentation
Therefore, it can capture partial information and enlarge the receptive field of filters simultaneously without introducing extra parameters.
PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages
Publication information in a researcher{'}s academic homepage provides insights about the researcher{'}s expertise, research interests, and collaboration networks.
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task
In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data
However, this representation is not in a natural language form, which is difficult for humans to understand.
Ranked #12 on
Data-to-Text Generation
on WebNLG
KDGAN: Knowledge Distillation with Generative Adversarial Networks
An alternative method is to adversarially train the classifier against a discriminator in a two-player game akin to generative adversarial networks (GAN), which can ensure the classifier to learn the true data distribution at the equilibrium of this game.
Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond
To achieve robust baselines, we build on a recent approach that aligns per-class scatter matrices of the source and target CNN streams.
LEARNING ADVERSARIAL EXAMPLES WITH RIEMANNIAN GEOMETRY
In this paper, we propose a generalized framework that addresses the learning problem of adversarial examples with Riemannian geometry.
Generative Adversarial Classifier for Handwriting Characters Super-Resolution
Generative Adversarial Networks (GAN) receive great attentions recently due to its excellent performance in image generation, transformation, and super-resolution.
Scale-Adaptive Convolutions for Scene Parsing
Through adding a new scale regression layer, we can dynamically infer the position-adaptive scale coefficients which are adopted to resize the convolutional patches.
Harmonic Unpaired Image-to-image Translation
The recent direction of unpaired image-to-image translation is on one hand very exciting as it alleviates the big burden in obtaining label-intensive pixel-to-pixel supervision, but it is on the other hand not fully satisfactory due to the presence of artifacts and degenerated transformations.
CBHE: Corner-based Building Height Estimation for Complex Street Scene Images
CBHE first obtains building corner and roofline candidates in street scene images based on building footprints from 2D maps and the camera parameters.
From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD).
Exploiting Offset-guided Network for Pose Estimation and Tracking
Human pose estimation has witnessed a significant advance thanks to the development of deep learning.
Applications of Natural Language Processing in Clinical Research and Practice
Through this tutorial, we would like to introduce NLP methodologies and tools developed in the clinical domain, and showcase the real-world NLP applications in clinical research and practice at Mayo Clinic (the No.
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations.
Cross-Lingual Information Retrieval
Cross-Lingual Word Embeddings
+3
Defending Adversarial Attacks by Correcting logits
Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning.
A Strong Feature Representation for Siamese Network Tracker
Firstly, the modified pre-trained VGG16 network is fine-tuned as the backbone.
A Learning-Based Two-Stage Spectrum Sharing Strategy with Multiple Primary Transmit Power Levels
Then, based on a novel normalized power level alignment metric, we propose two prediction-transmission structures, namely periodic and non-periodic, for spectrum access (the second part in Stage II), which enable the secondary transmitter (ST) to closely follow the PT power level variation.
Neural Relation Extraction for Knowledge Base Enrichment
This way, NED errors may cause extraction errors that affect the overall precision and recall. To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model.
Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States
We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).
Consensus Feature Network for Scene Parsing
However, classification networks are dominated by the discriminative portion, so directly applying classification networks to scene parsing will result in inconsistent parsing predictions within one instance and among instances of the same category.
Generative Image Inpainting with Submanifold Alignment
Image inpainting aims at restoring missing regions of corrupted images, which has many applications such as image restoration and object removal.
MMF: Attribute Interpretable Collaborative Filtering
Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry.
CARL: Aggregated Search with Context-Aware Module Embedding Learning
To model and utilize the context information for aggregated search, we propose a model with context attention and representation learning (CARL).
Boosted GAN with Semantically Interpretable Information for Image Inpainting
Forexample, given a male image with image region of one eye missing, current models may restore it with a female eye.
Goodness-of-fit tests on manifolds
We develop a general theory for the goodness-of-fit test to non-linear models.
Joint Active and Passive Beamforming Optimization for Intelligent Reflecting Surface Assisted SWIPT under QoS Constraints
Moreover, by exploiting the short-range coverage of IRSs, we further propose a low-complexity algorithm by optimizing the phase shifts of all IRSs in parallel.
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers
Adaptive Precision Training: Quantify Back Propagation in Neural Networks with Fixed-point Numbers.
Robust Principal Component Analysis with Adaptive Neighbors
More significantly, the degree of the sparsity is steerable such that only exact k well-fitting samples with least reconstruction errors are activated during the optimization, while the residual samples, i. e., the extreme noised ones are eliminated for the global robustness.
Entity Extraction with Knowledge from Web Scale Corpora
Entity extraction is an important task in text mining and natural language processing.
Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends
Future wireless networks are expected to evolve towards an intelligent and software reconfigurable paradigm enabling ubiquitous communications between humans and mobile devices.
MALA: Cross-Domain Dialogue Generation with Action Learning
These two components, however, have a discrepancy in their objectives, i. e., task completion and language quality.
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4.
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
We show that QP matches quantile functions rather than moments as in EP and has the same mean update but a smaller variance update than EP, thereby alleviating EP's tendency to over-estimate posterior variances.
Asymmetric GAN for Unpaired Image-to-image Translation
While in situations where two domains are asymmetric in complexity, i. e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity.
DWM: A Decomposable Winograd Method for Convolution Acceleration
In this paper, we propose a novel Decomposable Winograd Method (DWM), which breaks through the limitation of original Winograd's minimal filtering algorithm to a wide and general convolutions.
Embedding Graph Auto-Encoder for Graph Clustering
Driven by theoretical analysis about relaxed k-means, we design a specific GAE-based model for graph clustering to be consistent with the theory, namely Embedding Graph Auto-Encoder (EGAE).
Unsupervised Graph Embedding via Adaptive Graph Learning
Moreover, the adjacency matrix can be self-learned for better embedding performance when the original graph structure is incomplete.
Security of Distributed Machine Learning: A Game-Theoretic Approach to Design Secure DSVM
Distributed machine learning algorithms play a significant role in processing massive data sets over large networks.
ReADS: A Rectified Attentional Double Supervised Network for Scene Text Recognition
In this paper, we elaborately design a Rectified Attentional Double Supervised Network (ReADS) for general scene text recognition.
Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation
This approach requires complete state-action annotations of human-to-human dialogues (i. e., expert demonstrations), which is labor intensive.
Map Generation from Large Scale Incomplete and Inaccurate Data Labels
Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc..
MATERIALizing Cross-Language Information Retrieval: A Snapshot
At about the midpoint of the IARPA MATERIAL program in October 2019, an evaluation was conducted on systems{'} abilities to find Lithuanian documents based on English queries.
A heterogeneous branch and multi-level classification network for person re-identification
A new heterogeneous branch, SE-Res-Branch, is proposed based on the SE-Res module, which consists of the Squeeze-and-Excitation block and the residual block.
Grafted network for person re-identification
In order to relieve this problem, we propose a novel grafted network (GraftedNet), which is designed by grafting a high-accuracy rootstock and a light-weighted scion.
Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial
Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal prorogation in wireless networks.
FedOCR: Communication-Efficient Federated Learning for Scene Text Recognition
In this paper, we study how to make use of decentralized datasets for training a robust scene text recognizer while keeping them stay on local devices.
Short-Term and Long-Term Context Aggregation Network for Video Inpainting
Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal.
Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text
The best performed NER and RE models were further applied on clinical notes mentioning 88 DS for discovering DS adverse events and indications, which were compared with a DS knowledge base.
Learning Based Distributed Tracking
Inspired by the great success of machine learning in the past decade, people have been thinking about the possibility of improving the theoretical results by exploring data distribution.
Data Structures and Algorithms
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing
Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e. g., SQL, SPARQL) that can be executed against a structured ontology (e. g. databases, knowledge bases).
Ranked #3 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
Generalizable and Explainable Dialogue Generation via Explicit Action Learning
To alleviate the need of action annotations, latent action learning is introduced to map each utterance to a latent representation.
Intelligent Reflecting Surface Assisted Multi-User OFDMA: Channel Estimation and Training Design
To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users.
Information Theory Information Theory
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
In recommender systems, usually the ratings of a user to most items are missing and a critical problem is that the missing ratings are often missing not at random (MNAR) in reality.
Aerial Intelligent Reflecting Surface: Joint Placement and Passive Beamforming Design with 3D Beam Flattening
Specifically, we aim to maximize the worst-case signal-to-noise ratio (SNR) over all locations in a target area by jointly optimizing the transmit beamforming for the source node and the placement as well as 3D passive beamforming for the AIRS.
Scene Text Detection with Scribble Lines
It is a general labeling method for texts with various shapes and requires low labeling costs.
TEMImageNet Training Library and AtomSegNet Deep-Learning Models for High-Precision Atom Segmentation, Localization, Denoising, and Super-Resolution Processing of Atomic-Resolution Images
Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task.
Compressive Circular Polarization Snapshot Spectral Imaging
A compressive sensing based circular polarization snapshot spectral imaging system is proposed in this paper to acquire two-dimensional spatial, one-dimensional circular polarization (the right and left circular polarization), and one-dimensional spectral information, simultaneously.
Compressive Sensing
Optics
Image and Video Processing
78A10 (Primary)
I.4.2; I.4.4; I.4.5
Refined Nonlinear Rectenna Modeling and Optimal Waveform Design for Multi-User Multi-Antenna Wireless Power Transfer
Leveraging this new rectenna model, we first consider the single-ER case and study the multisine-based power waveform design based on the wireless channel to maximize the harvested power at the ER.
Information Theory Information Theory
Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision
We performed two case studies: physical activity and excessive diet, in order to validate the effectiveness of BERT models in classifying lifestyle factors for AD.
HexCNN: A Framework for Native Hexagonal Convolutional Neural Networks
Experimental results show that compared with the state-of-the-art models, which imitate hexagonal processing but using rectangle-shaped filters, HexCNN reduces the training time by up to 42. 2%.
The evolution of network controllability in growing networks
The study of network structural controllability focuses on the minimum number of driver nodes needed to control a whole network.
Physics and Society
Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review
Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive Model
Distributed quantum phase estimation with entangled photons
Distributed quantum metrology can enhance the sensitivity for sensing spatially distributed parameters beyond the classical limits.
Quantum Physics
