Search Results for author:
Found 156 papers, 62 papers with code
WIKIBIAS: Detecting Multi-Span Subjective Biases in Language
Biases continue to be prevalent in modern text and media, especially subjective bias – a special type of bias that introduces improper attitudes or presents a statement with the presupposition of truth.
Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective
Given the fixed point equation (FPE) derived from the variational inference on the Markov random fields, the deep GNNs, including JKNet, GCNII, DGCN, and the classical GNNs, such as GCN, GAT, and APPNP, can be regarded as different approximations of the FPE.
AI in HCI Design and User Experience
We then discuss how AI technology can be used to enhance HCI/UX design.
LENS: A Learnable Evaluation Metric for Text Simplification
Training on SIMPEVAL_ASSET, we present LENS, a Learnable Evaluation Metric for Text Simplification.
Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments
We present a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that violate relevant policies.
Frustratingly Easy Label Projection for Cross-lingual Transfer
Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer.
Stanceosaurus: Classifying Stance Towards Multilingual Misinformation
We present Stanceosaurus, a new corpus of 28, 033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims.
arXivEdits: Understanding the Human Revision Process in Scientific Writing
We first introduce arXivEdits, a new annotated corpus of 751 full papers from arXiv with gold sentence alignment across their multiple versions of revision, as well as fine-grained span-level edits and their underlying intentions for 1, 000 sentence pairs.
PaCo: Parameter-Compositional Multi-Task Reinforcement Learning
However, the gaps between contents and difficulties of different tasks bring us challenges on both which tasks should share the parameters and what parameters should be shared, as well as the optimization challenges due to parameter sharing.
Improving Large-scale Paraphrase Acquisition and Generation
This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identification and generation tasks.
Intelligent MIMO Detection Using Meta Learning
By treating K as a variable that can be adjusted according to a fitting function of some learnable coefficients, an intelligent MIMO detection network based on deep neural networks (DNN) is proposed to reduce complexity of the detection algorithm with little performance degradation.
Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics
Deep learning (DL) applied to a device's radio-frequency fingerprint~(RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance.
Semi-supervised MIMO Detection Using Cycle-consistent Generative Adversarial Network
In this paper, a new semi-supervised deep multiple-input multiple-output (MIMO) detection approach using a cycle-consistent generative adversarial network (cycleGAN) is proposed for communication systems without any prior knowledge of underlying channel distributions.
HMRNet: High and Multi-Resolution Network with Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapy
Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy.
Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing
In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading.
PNM: Pixel Null Model for General Image Segmentation
A major challenge in image segmentation is classifying object boundaries.
An End-to-End Transformer Model for Crowd Localization
Crowd localization, predicting head positions, is a more practical and high-level task than simply counting.
A Bayesian Deep Learning Approach to Near-Term Climate Prediction
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction.
A Geometric Understanding of Natural Gradient
While natural gradients have been widely studied from both theoretical and empirical perspectives, we argue that some fundamental theoretical issues regarding the existence of gradients in infinite dimensional function spaces remain underexplored.
Towards Safe Reinforcement Learning with a Safety Editor Policy
On 12 Safety Gym tasks and 2 safe racing tasks, SEditor obtains much a higher overall safety-weighted-utility (SWU) score than the baselines, and demonstrates outstanding utility performance with constraint violation rates as low as once per 2k time steps, even in obstacle-dense environments.
Do You Need the Entropy Reward (in Practice)?
On the other hand, our large-scale empirical study shows that using entropy regularization alone in policy improvement, leads to comparable or even better performance and robustness than using it in both policy improvement and policy evaluation.
Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning
GPM can therefore leverage its generated multi-step plans for temporally coordinated exploration towards high value regions, which is potentially more effective than a sequence of actions generated by perturbing each action at single step level, whose consistent movement decays exponentially with the number of exploration steps.
User Centered Design (VI): Human Factors Approaches for Intelligent Human-Computer Interaction
With the help of the concept and framework, the paper analyzes the human factors issues in the ecosystem of autonomous vehicle co-driving and proposes an initial human factors solution.
Natural Language for Human-Robot Collaboration: Problems Beyond Language Grounding
To enable robots to instruct humans in collaborations, we identify several aspects of language processing that are not commonly studied in this context.
Faster Reinforcement Learning with Value Target Lower Bounding
In practice, discounted episodic return from the training experience or discounted goal return from hindsight relabeling can serve as the value lower bound when the environment is deterministic.
Pre-train or Annotate? Domain Adaptation with a Constrained Budget
Our experiments suggest task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.
BiSECT: Learning to Split and Rephrase Sentences with Bitexts
We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited.
Feature Importance in a Deep Learning Climate Emulator
From the perspective of climate dynamics, these findings suggest a dominant role for local processes and a negligible role for remote teleconnections at the spatial and temporal scales we consider.
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need
In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval.
A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication
To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
Neural semi-Markov CRF for Monolingual Word Alignment
Monolingual word alignment is important for studying fine-grained editing operations (i. e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc.
Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI
We propose alternative methods that can help overcome these limitations and effectively help HCI professionals apply the HCAI approach to the development of AI systems.
Super Solutions of the Model RB
The concept of super solution is a special type of generalized solutions with certain degree of robustness and stability.
TransCrowd: weakly-supervised crowd counting with transformers
Current weakly-supervised counting methods adopt the CNN to regress a total count of the crowd by an image-to-count paradigm.
TAAC: Temporally Abstract Actor-Critic for Continuous Control
TAAC has two important features: a) persistent exploration, and b) a new compare-through Q operator for multi-step TD backup, specially tailored to the action repetition scenario.
Mutual Information State Intrinsic Control
Reinforcement learning has been shown to be highly successful at many challenging tasks.
Generative Particle Variational Inference via Estimation of Functional Gradients
Recently, particle-based variational inference (ParVI) methods have gained interest because they can avoid arbitrary parametric assumptions that are common in variational inference.
R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping
Our proposed framework is composed of two parts: the filter-based odometry and factor graph optimization.
Robotics
Focal Inverse Distance Transform Maps for Crowd Localization
Most regression-based methods utilize convolution neural networks (CNN) to regress a density map, which can not accurately locate the instance in the extremely dense scene, attributed to two crucial reasons: 1) the density map consists of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense region of the density map.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Hierarchical Reinforcement Learning By Discovering Intrinsic Options
We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Meanwhile, such applications usually require modeling the intrinsic clusters in high-dimensional data, which usually displays heterogeneous statistical patterns as the patterns of different clusters may appear in different dimensions.
FAWA: Fast Adversarial Watermark Attack on Optical Character Recognition (OCR) Systems
In both letter-level and word-level attacks, our experiments show that in addition to natural appearance, FAWA achieves a 100% attack success rate with 60% less perturbations and 78% fewer iterations on average.
Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting
In this paper, we propose a simple yet efficient counting network based on point-level annotations.
Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images
To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.
WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols
This paper presents the results of the wet lab information extraction task at WNUT 2020.
Ranked #1 on
Relation Extraction
on WNUT 2020
Controllable Text Simplification with Explicit Paraphrasing
Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting.
Sliding Differential Evolution Scheduling for Federated Learning in Bandwidth-Limited Networks
Federated learning (FL) in a bandwidth-limited network with energy-limited user equipments (UEs) is under-explored.
FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
To lower the computation load in the presence of large number of measurements, we present a new formula to compute the Kalman gain.
Robotics
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards
In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals.
Robots State Estimation and Observability Analysis Based on Statistical Motion Models
It is shown that this new paradigm is much simpler and more natural than existing methods based on quaternion parameterizations.
Beamforming Design for Multiuser Transmission Through Reconfigurable Intelligent Surface
To solve this problem, a dual method is proposed, where the dual problem is obtained as a semidefinite programming problem.
Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing
An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground.
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images.
AnciNet: An Efficient Deep Learning Approach for Feedback Compression of Estimated CSI in Massive MIMO Systems
Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith.
Analysis and Optimization for RIS-Aided Multi-Pair Communications Relying on Statistical CSI
Simulation results verify the correctness of the obtained results and show that the proposed GA method has almost the same performance as the globally optimal solution.
Joint Transmit Power and Placement Optimization for URLLC-enabled UAV Relay Systems
This letter considers an unmanned aerial vehicle (UAV)-enabled relay communication system for delivering latency-critical messages with ultra-high reliability, where the relay is operating under amplifier-and-forward (AF) mode.
Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network
Meanwhile, we propose Gated-RGCN to accumulate evidence on the path-based reasoning graph, which contains a new question-aware gating mechanism to regulate the usefulness of information propagating across documents and add question information during reasoning.
Extracting a Knowledge Base of COVID-19 Events from Social Media
In this paper, we present a manually annotated corpus of 10, 000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions.
Feature Statistics Guided Efficient Filter Pruning
Building compact convolutional neural networks (CNNs) with reliable performance is a critical but challenging task, especially when deploying them in real-world applications.
Neural CRF Model for Sentence Alignment in Text Simplification
The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles.
Ranked #1 on
Text Simplification
on Newsela
Code and Named Entity Recognition in StackOverflow
We also present the SoftNER model which achieves an overall 79. 10 F$_1$ score for code and named entity recognition on StackOverflow data.
Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs.
An Empirical Study of Pre-trained Transformers for Arabic Information Extraction
Multilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable the effective cross-lingual zero-shot transfer.
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
Based on the datasets, we propose novel tasks such as multi-hop knowledge abstraction (MKA), multi-hop knowledge concretization (MKC) and then design a comprehensive benchmark.
DCMD: Distance-based Classification Using Mixture Distributions on Microbiome Data
The performance of the model is assessed using simulations and applied to a human microbiome study, with results compared against a number of existing machine learning and distance-based approaches.
Learning Relation Entailment with Structured and Textual Information
We propose several methods that incorporate both structured and textual information to represent relations for this task.
AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment -- Challenges and Solutions
By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation networks.
Attacking Optical Character Recognition (OCR) Systems with Adversarial Watermarks
Optical character recognition (OCR) is widely applied in real applications serving as a key preprocessing tool.
Mutual Information-based State-Control for Intrinsically Motivated Reinforcement Learning
In reinforcement learning, an agent learns to reach a set of goals by means of an external reward signal.
Interpreting Galaxy Deblender GAN from the Discriminator's Perspective
Using the Galaxy Zoo dataset we demonstrate that our method clearly reveals attention areas of the Discriminator when differentiating generated galaxy images from ground truth images.
Discourse Level Factors for Sentence Deletion in Text Simplification
We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected.
Implicit Generative Modeling for Efficient Exploration
Each random draw from our generative model is a neural network that instantiates the dynamic function, hence multiple draws would approximate the posterior, and the variance in the future prediction based on this posterior is used as an intrinsic reward for exploration.
Generalizing Natural Language Analysis through Span-relation Representations
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.
Ranked #1 on
Relation Extraction
on WLPC
Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing
In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE).
Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images
This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images.
Identifying Candidate Spaces for Advert Implantation
Virtual advertising is an important and promising feature in the area of online advertising.
Adversarial Interpolation Training: A Simple Approach for Improving Model Robustness
We propose a simple approach for adversarial training.
Self-Supervised State-Control through Intrinsic Mutual Information Rewards
Our results show that the mutual information between the context states and the states of interest can be an effective ingredient for overcoming challenges in robotic manipulation tasks with sparse rewards.
Bit-level Optimized Neural Network for Multi-antenna Channel Quantization
Quantized channel state information (CSI) plays a critical role in precoding design which helps reap the merits of multiple-input multiple-output (MIMO) technology.
Information Theory Signal Processing Information Theory
CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels.
Acceleration Based Iterative Learning Control for Pugachev's Cobra Maneuver with Quadrotor Tail-sitter VTOL UAVs
Both the feedback controller and the iterative learning feed-forward controller are based on the aircraft acceleration model, which is directly measurable by the onboard accelerometer.
Systems and Control
Multi-task Pairwise Neural Ranking for Hashtag Segmentation
Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics.
Localizing Adverts in Outdoor Scenes
Such techniques involve replacing an existing advertisement in a video frame, with a new advertisement.
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Most existing event extraction (EE) methods merely extract event arguments within the sentence scope.
Ranked #4 on
Document-level Event Extraction
on ChFinAnn
The ALOS Dataset for Advert Localization in Outdoor Scenes
The rapid increase in the number of online videos provides the marketing and advertising agents ample opportunities to reach out to their audience.
Learning Good Representation via Continuous Attention
In this paper we present our scientific discovery that good representation can be learned via continuous attention during the interaction between Unsupervised Learning(UL) and Reinforcement Learning(RL) modules driven by intrinsic motivation.
The CASE Dataset of Candidate Spaces for Advert Implantation
With the advent of faster internet services and growth of multimedia content, we observe a massive growth in the number of online videos.
ADNet: A Deep Network for Detecting Adverts
Online video advertising gives content providers the ability to deliver compelling content, reach a growing audience, and generate additional revenue from online media.
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers.
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.
Zero-Shot Transfer VQA Dataset
Therefore, toaccelerate this research, we propose a newzero-shot transfer VQA(ZST-VQA)dataset by reorganizing the existing VQA v1. 0 dataset in the way that duringtraining, some words appear only in one module (i. e. questions) but not in theother (i. e. answers).
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SoTA methods.
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment.
Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos
Then the whole scene is decomposed into moving foreground and static background by compar- ing the estimated optical flow and rigid flow derived from the depth and ego-motion.
Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking
The separation of the task requires to define a hand-crafted training goal in affinity learning stage and a hand-crafted cost function of data association stage, which prevents the tracking goals from learning directly from the feature.
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding
The four types of information, i. e. 2D flow, camera pose, segment mask and depth maps, are integrated into a differentiable holistic 3D motion parser (HMP), where per-pixel 3D motion for rigid background and moving objects are recovered.
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering
In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks.
Ranked #1 on
Paraphrase Identification
on 2017_test set
Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents
Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning.
Character-based Neural Networks for Sentence Pair Modeling
Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference.
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map
The uniqueness of our design is a sensor fusion scheme which integrates camera videos, motion sensors (GPS/IMU), and a 3D semantic map in order to achieve robustness and efficiency of the system.
Scaling Nakamoto Consensus to Thousands of Transactions per Second
The Conflux consensus protocol represents relationships between blocks as a direct acyclic graph and achieves consensus on a total order of the blocks.
Distributed, Parallel, and Cluster Computing
An Annotated Corpus for Machine Reading of Instructions in Wet Lab Protocols
We make our annotated Wet Lab Protocol Corpus available to the research community.
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game
Building intelligent agents that can communicate with and learn from humans in natural language is of great value.
LEGO: Learning Edge with Geometry all at Once by Watching Videos
In our framework, the predicted depths, normals and edges are forced to be consistent all the time.
Interactive Grounded Language Acquisition and Generalization in a 2D World
We build a virtual agent for learning language in a 2D maze-like world.
ICFVR 2017: 3rd International Competition on Finger Vein Recognition
The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets.
Occlusion Aware Unsupervised Learning of Optical Flow
Especially on KITTI dataset where abundant unlabeled samples exist, our unsupervised method outperforms its counterpart trained with supervised learning.
Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency
Learning to reconstruct depths in a single image by watching unlabeled videos via deep convolutional network (DCN) is attracting significant attention in recent years.
DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting
Several pioneering approaches have been proposed based on traffic observations of the target location as well as its adjacent regions, but they obtain somewhat limited accuracy due to lack of mining road topology.
From Shakespeare to Twitter: What are Language Styles all about?
As natural language processing research is growing and largely driven by the availability of data, we expanded research from news and small-scale dialog corpora to web and social media.
A Continuously Growing Dataset of Sentential Paraphrases
The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work.
Listen, Interact and Talk: Learning to Speak via Interaction
One of the long-term goals of artificial intelligence is to build an agent that can communicate intelligently with human in natural language.
Unsupervised Learning Layers for Video Analysis
This paper presents two unsupervised learning layers (UL layers) for label-free video analysis: one for fully connected layers, and the other for convolutional ones.
Dynamic Computational Time for Visual Attention
We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM).
A Deep Compositional Framework for Human-like Language Acquisition in Virtual Environment
We believe that our results provide some preliminary insights on how to train an agent with similar abilities in a 3D environment.
Discovering Conversational Dependencies between Messages in Dialogs
We investigate the task of inferring conversational dependencies between messages in one-on-one online chat, which has become one of the most popular forms of customer service.
Results of the WNUT16 Named Entity Recognition Shared Task
This paper presents the results of the Twitter Named Entity Recognition shared task associated with W-NUT 2016: a named entity tagging task with 10 teams participating.
Automatically Building Face Datasets of New Domains from Weakly Labeled Data with Pretrained Models
In this paper, we propose a method to automatically and incrementally construct datasets from massive weakly labeled data of the target domain which are readily available on the Internet under the help of a pretrained face model.
Deep Joint Face Hallucination and Recognition
However, we observe that directly feeding the hallucinated facial images into recog- nition models can even degrade the recognition performance despite the much better visualization quality.
Neural Machine Translation with Pivot Languages
While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for resource-scarce language pairs.
TweeTime: A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter
We describe TweeTIME, a temporal tagger for recognizing and normalizing time expressions in Twitter.
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
While question answering (QA) with neural network, i. e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system.
Semi-Supervised Learning for Neural Machine Translation
While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation.
Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation
On the WMT'14 English-to-French task, we achieve BLEU=37. 7 with a single attention model, which outperforms the corresponding single shallow model by 6. 2 BLEU points.
Ranked #37 on
Machine Translation
on WMT2014 English-French
CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
We propose CFO, a Conditional Focused neural-network-based approach to answering factoid questions with knowledge bases.
CNN-RNN: A Unified Framework for Multi-label Image Classification
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image.
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
In modern large-scale machine learning applications, the training data are often partitioned and stored on multiple machines.
Optimizing Statistical Machine Translation for Text Simplification
Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus.
Ranked #7 on
Text Simplification
on TurkCorpus
Look and Think Twice: Capturing Top-Down Visual Attention With Feedback Convolutional Neural Networks
While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to remember that the human visual contex contains generally more feedback connections than foward connections.
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics.
Attention to Scale: Scale-aware Semantic Image Segmentation
We adapt a state-of-the-art semantic image segmentation model, which we jointly train with multi-scale input images and the attention model.
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks
The sentence generator produces one simple short sentence that describes a specific short video interval.
Bidirectional LSTM-CRF Models for Sequence Tagging
It can also use sentence level tag information thanks to a CRF layer.
Ranked #1 on
Named Entity Recognition
on FindVehicle
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task.
Problems in Current Text Simplification Research: New Data Can Help
Simple Wikipedia has dominated simplification research in the past 5 years.
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions.
Explain Images with Multimodal Recurrent Neural Networks
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images.
Identification of multiple damage in beams based on robust curvature mode shapes
Multiple damage identification in beams using curvature mode shape has become a research focus of increasing interest during the last few years.
Extracting Lexically Divergent Paraphrases from Twitter
We present MultiP (Multi-instance Learning Paraphrase Model), a new model suited to identify paraphrases within the short messages on Twitter.
Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent
Polyak and Juditsky (1992) showed that asymptotically the test performance of the simple average of the parameters obtained by stochastic gradient descent (SGD) is as good as that of the parameters which minimize the empirical cost.
Deep Learning with Kernel Regularization for Visual Recognition
In this paper we focus on training deep neural networks for visual recognition tasks.
