Search Results for author:
Found 113 papers, 51 papers with code
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources.
Ranked #1 on
Image Retrieval
on Localized Narratives
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
A robust evaluation metric has a profound impact on the development of text generation systems.
Toward Abstractive Summarization Using Semantic Representations
We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR).
Lidar Point Cloud Guided Monocular 3D Object Detection
We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels.
Scoring Sentence Singletons and Pairs for Abstractive Summarization
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction
Machine learning (ML) methods have gained increasing popularity in exploring and developing new materials.
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment Analysis
While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) --- extraction of fine-grained opinion polarity w. r. t.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on Sentihood
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images
Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs).
Algorithm Evolution Using Large Language Model
In this paper, we propose a novel approach called Algorithm Evolution using Large Language Model (AEL).
An Example of Evolutionary Computation + Large Language Model Beating Human: Design of Efficient Guided Local Search
Recently, we have proposed a novel Algorithm Evolution using Large Language Model (AEL) framework for automatic algorithm design.
Recurrent Affine Transformation for Text-to-image Synthesis
The main difficulty of this task lies in effectively fusing text information into the image synthesis process.
Ranked #4 on
Text-to-Image Generation
on Oxford 102 Flowers
Evaluating the Utility of Hand-crafted Features in Sequence Labelling
Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora.
Ranked #42 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
Controlling the Amount of Verbatim Copying in Abstractive Summarization
In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones.
Ranked #12 on
Text Summarization
on GigaWord
Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
Neural combinatorial optimization (NCO) is a promising learning-based approach for solving challenging combinatorial optimization problems without specialized algorithm design by experts.
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
Generating a text abstract from a set of documents remains a challenging task.
Toward Unifying Text Segmentation and Long Document Summarization
The problem is only exacerbated by a lack of segmentation in transcripts of audio/video recordings.
Ranked #5 on
Text Summarization
on Pubmed
Structure-Infused Copy Mechanisms for Abstractive Summarization
In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence.
Ranked #34 on
Text Summarization
on GigaWord
InFoBench: Evaluating Instruction Following Ability in Large Language Models
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions.
Understanding Points of Correspondence between Sentences for Abstractive Summarization
We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences.
The Referential Reader: A Recurrent Entity Network for Anaphora Resolution
We present a new architecture for storing and accessing entity mentions during online text processing.
CATE: Computation-aware Neural Architecture Encoding with Transformers
Our experiments show that CATE is beneficial to the downstream search, especially in the large search space.
Ranked #15 on
Neural Architecture Search
on CIFAR-10
Large Language Model for Multi-objective Evolutionary Optimization
It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings.
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security.
Learning to Fuse Sentences with Transformers for Summarization
The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts.
Guiding Extractive Summarization with Question-Answering Rewards
However, a major obstacle to the development of a supervised summarizer is the lack of ground-truth.
Joint Parsing and Generation for Abstractive Summarization
If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged.
Ranked #27 on
Text Summarization
on GigaWord
Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization
The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data.
Development of a Flexible Coupling Framework for Coastal Inundation Studies
To enable flexible model coupling in coastal inundation studies, a coupling framework based on ESMF/NUOPC technology under a common modeling framework called the NOAA Environmental Modeling System (NEMS) was developed.
Atmospheric and Oceanic Physics
Towards Abstractive Grounded Summarization of Podcast Transcripts
Summarization of podcast transcripts is of practical benefit to both content providers and consumers.
Gated End-to-End Memory Networks
Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge.
Narrative Modeling with Memory Chains and Semantic Supervision
Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task.
A New Approach to Overgenerating and Scoring Abstractive Summaries
We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users' needs.
Ranked #10 on
Text Summarization
on GigaWord
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition
We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot.
Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields
Despite successful applications across a broad range of NLP tasks, conditional random fields ("CRFs"), in particular the linear-chain variant, are only able to model local features.
How Domain Terminology Affects Meeting Summarization Performance
We then analyze the performance of a meeting summarization system with and without jargon terms.
StreamHover: Livestream Transcript Summarization and Annotation
With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands.
Score Mismatching for Generative Modeling
This model has the following advantages: 1) For sampling, it generates a fake image with only one step forward.
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
The results show that the unified model demonstrates superior performance in the eleven VRPs, reducing the average gap to around 5% from over 20% in the existing approach and achieving a significant performance boost on benchmark datasets as well as a real-world logistics application.
DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing
In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education.
MeetingBank: A Benchmark Dataset for Meeting Summarization
However, there is a crucial lack of annotated meeting corpora for developing this technology, as it can be hard to collect meetings, especially when the topics discussed are confidential.
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion
We present an empirical study in favor of a cascade architecture to neural text summarization.
Better Highlighting: Creating Sub-Sentence Summary Highlights
Amongst the best means to summarize is highlighting.
Point Spread Function Estimation of Defocus
In this work, we develop an alternative method to estimate the precise mathematical model of the point spread function to describe the defocus process.
A Sliding-Window Approach to Automatic Creation of Meeting Minutes
Meeting minutes record any subject matters discussed, decisions reached and actions taken at meetings.
ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images
Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images.
PaniniQA: Enhancing Patient Education Through Interactive Question Answering
Patient portal allows discharged patients to access their personalized discharge instructions in electronic health records (EHRs).
Generation of Patient After-Visit Summaries to Support Physicians
Crucially, we introduce a feedback mechanism that alerts physicians when an automatic summary fails to capture the important details of the clinical notes or when it contains hallucinated facts that are potentially detrimental to the summary quality.
Modeling Endorsement for Multi-Document Abstractive Summarization
In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization.
Reducing Model Churn: Stable Re-training of Conversational Agents
Lastly, we discuss practical trade-offs between such techniques and show that co-distillation provides a sweet spot in terms of churn reduction with only a modest increase in resource usage.
Abstract Meaning Representation for Multi-Document Summarization
Generating an abstract from a collection of documents is a desirable capability for many real-world applications.
An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses
Teaching large classes remains a great challenge, primarily because it is difficult to attend to all the student needs in a timely manner.
Automatic Summarization of Student Course Feedback
Student course feedback is generated daily in both classrooms and online course discussion forums.
Modeling Language Vagueness in Privacy Policies using Deep Neural Networks
Website privacy policies are too long to read and difficult to understand.
Reinforced Extractive Summarization with Question-Focused Rewards
We use reinforcement learning to explore the space of possible extractive summaries and introduce a question-focused reward function to promote concise, fluent, and informative summaries.
Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks
Forum threads are lengthy and rich in content.
A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads
Asking effective questions is a powerful social skill.
An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis
The base classifier of the ensemble method is a modified k-means algorithm.
Dialog state tracking, a machine reading approach using Memory Network
In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules.
A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts
Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e. g. linear regression or Support Vector Machines.
From Incremental Meaning to Semantic Unit (phrase by phrase)
This paper describes an experimental approach to Detection of Minimal Semantic Units and their Meaning (DiMSUM), explored within the framework of SemEval 2016 Task 10.
A Novel ILP Framework for Summarizing Content with High Lexical Variety
Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events.
Automatic Detection of Vague Words and Sentences in Privacy Policies
In this paper, we seek to identify vague content in privacy policies.
A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts
Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e. g. linear regression or Support Vector Machines.
End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN
Limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards practical vision applications.
Analyzing Sentence Fusion in Abstractive Summarization
While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences.
Towards Annotating and Creating Sub-Sentence Summary Highlights
Highlighting is a powerful tool to pick out important content and emphasize.
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations
Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion.
Towards Annotating and Creating Summary Highlights at Sub-sentence Level
Highlighting is a powerful tool to pick out important content and emphasize.
Self-Attention Network for Skeleton-based Human Action Recognition
In order to come up with a better representation and capturing of long term spatio-temporal relationships, we propose three variants of Self-Attention Network (SAN), namely, SAN-V1, SAN-V2 and SAN-V3.
Ranked #62 on
Skeleton Based Action Recognition
on NTU RGB+D
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification.
Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity
In this paper, we apply pre-trained language models to the Semantic Textual Similarity (STS) task, with a specific focus on the clinical domain.
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles
As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and control.
The NVIDIA PilotNet Experiments
Four years ago, an experimental system known as PilotNet became the first NVIDIA system to steer an autonomous car along a roadway.
Automatic Summarization of Open-Domain Podcast Episodes
Instead, we investigate several less-studied aspects of neural abstractive summarization, including (i) the importance of selecting important segments from transcripts to serve as input to the summarizer; (ii) striking a balance between the amount and quality of training instances; (iii) the appropriate summary length and start/end points.
On a tilted Liouville-master equation of open quantum systems
We demonstrate that it is the unraveling of the tilted quantum master equation.
Statistical Mechanics Probability
Model-Predictive Control of Blood Suction for Surgical Hemostasis using Differentiable Fluid Simulations
The fully differentiable fluid dynamics is integrated with a novel suction model for effective model predictive control of the tool.
Robotics
OCM3D: Object-Centric Monocular 3D Object Detection
Image-only and pseudo-LiDAR representations are commonly used for monocular 3D object detection.
Simulated Data Generation Through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling
Using physics models can be inaccurate because they cannot account for unknown factors and the effects of the deformation of the object as it is launched; moreover, deriving force coefficients for these models is not possible without extensive experimental testing.
HAIR: Hierarchical Visual-Semantic Relational Reasoning for Video Question Answering
Specifically, we present a novel graph memory mechanism to perform relational reasoning, and further develop two types of graph memory: a) visual graph memory that leverages visual information of video for relational reasoning; b) semantic graph memory that is specifically designed to explicitly leverage semantic knowledge contained in the classes and attributes of video objects, and perform relational reasoning in the semantic space.
Semantic Parsing of Brief and Multi-Intent Natural Language Utterances
Many military communication domains involve rapidly conveying situation awareness with few words.
Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects
In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge.
Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments
Lastly, we discuss practical trade-offs between such techniques and show that co-distillation provides a sweet spot in terms of jitter reduction for semantic parsing systems with only a modest increase in resource usage.
Multi-task Learning for Monocular Depth and Defocus Estimations with Real Images
We set up a dataset (named ALL-in-3D dataset) which is the first all-real image dataset consisting of 100K sets of all-in-focus images, focused images with focus depth, depth maps, and defocus maps.
APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing
If a student wants to answer a question correctly, the student should not only master the set of skills involved in the question but also think and obtain the associative path on the skills graph.
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied environments and classifies the mainstream methods into Relative Vision Localization (RVL) and Absolute Vision Localization (AVL).
Optical Flow Estimation
Simultaneous Localization and Mapping
+2
Even Order Explicit Symplectic Geometric Algorithms for Quaternion Kinematical Differential Equation in Guidance Navigation and Control via Diagonal Padè Approximation and Cayley Transform
Although explicit symplectic geometric algorithms (ESGA) for this problem are available, there is a lack of a unified way for constructing high order symplectic difference schemes with configurable order parameter.
Vision-Based Environmental Perception for Autonomous Driving
In this paper, we introduce and compare various methods of object detection and identification, then explain the development of depth estimation and compare various methods based on monocular, stereo, and RDBG sensors, next review and compare various methods of SLAM, and finally summarize the current problems and present the future development trends of vision technologies.
Image-based Pose Estimation and Shape Reconstruction for Robot Manipulators and Soft, Continuum Robots via Differentiable Rendering
We demonstrate that our method of using geometrical shape primitives can achieve high accuracy in shape reconstruction for a soft continuum robot and pose estimation for a robot manipulator.
Heuristics for Vehicle Routing Problem: A Survey and Recent Advances
Vehicle routing is a well-known optimization research topic with significant practical importance.
DecipherPref: Analyzing Influential Factors in Human Preference Judgments via GPT-4
Human preference judgments are pivotal in guiding large language models (LLMs) to produce outputs that align with human values.
SuPerPM: A Large Deformation-Robust Surgical Perception Framework Based on Deep Point Matching Learned from Physical Constrained Simulation Data
Thus, for tuning the learning model, we gather endoscopic data of soft tissue being manipulated by a surgical robot and then establish correspondences between point clouds at different time points to serve as ground truth.
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation
This method trains the model to prioritize the best responses from a pool of candidates created for a particular task.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
Factuality of Large Language Models in the Year 2024
Large language models (LLMs), especially when instruction-tuned for chat, have become part of our daily lives, freeing people from the process of searching, extracting, and integrating information from multiple sources by offering a straightforward answer to a variety of questions in a single place.
SportsMetrics: Blending Text and Numerical Data to Understand Information Fusion in LLMs
In this paper, we introduce four novel tasks centered around sports data analytics to evaluate the numerical reasoning and information fusion capabilities of LLMs.
Identifying Factual Inconsistency in Summaries: Towards Effective Utilization of Large Language Model
Factual inconsistency poses a significant hurdle for the commercial deployment of abstractive summarizers.
Smooth Tchebycheff Scalarization for Multi-Objective Optimization
Multi-objective optimization problems can be found in many real-world applications, where the objectives often conflict each other and cannot be optimized by a single solution.
Can Large Language Models do Analytical Reasoning?
Our analytical reasoning embodies the tasks of letting large language models count how many points each team scores in a quarter in the NBA and NFL games.
Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives
Uncertainties in mechanical systems as well as camera calibration create errors in this coordinate frame transformation.
Estimation and Inference for Three-Dimensional Panel Data Models
Hierarchical panel data models have recently garnered significant attention.
