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Found 137 papers, 60 papers with code
Unsupervised Dependency Graph Network
We introduce a new model, the Unsupervised Dependency Graph Network (UDGN), that can induce dependency structures from raw corpora and the masked language modeling task.
Event Detection with Dual Relational Graph Attention Networks
To this end, we propose a simple yet effective model named DualGAT (Dual Relational Graph Attention Networks), which exploits the complementary nature of syntactic and semantic relations to alleviate the problem.
CodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild
Existing relation extraction (RE) methods typically focus on extracting relational facts between entity pairs within single sentences or documents.
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation
Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection.
Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach
In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models.
Methodology for Capacity Credit Evaluation of Physical and Virtual Energy Storage in Decarbonized Power System
Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization.
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system.
Contrastive Learning with Consistent Representations
Contrastive learning demonstrates great promise for representation learning.
Restricted Orthogonal Gradient Projection for Continual Learning
The basic idea is to adopt a restricted orthogonal constraint allowing parameters optimized in the direction oblique to the whole frozen space to facilitate forward knowledge transfer while consolidating previous knowledge.
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning
Federated Learning has become a widely-used framework which allows learning a global model on decentralized local datasets under the condition of protecting local data privacy.
Prompt Gating: A Parameter Efficient Tuning Method for Zero-Shot Multi-Source Translation
Multi-source translation (MST), which typically receives multiple source sentences of the same meaning in different languages, has been shown superior to single-source translation.
Continually Learning from Existing Models: Knowledge Accumulation for Neural Machine Translation
Although continually extending an existing NMT model to new domains or languages has attracted intensive interest in recent years, the equally valuable problem of continually improving a given NMT model in its domain by leveraging knowledge from an unlimited number of existing NMT models is not explored yet.
Synaptic Dynamics Realize First-order Adaptive Learning and Weight Symmetry
Gradient-based first-order adaptive optimization methods such as the Adam optimizer are prevalent in training artificial networks, achieving the state-of-the-art results.
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction
It contains 103, 193 event coreference chains, 1, 216, 217 temporal relations, 57, 992 causal relations, and 15, 841 subevent relations, which is larger than existing datasets of all the ERE tasks by at least an order of magnitude.
iSmallNet: Densely Nested Network with Label Decoupling for Infrared Small Target Detection
First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information.
GeoGCN: Geometric Dual-domain Graph Convolution Network for Point Cloud Denoising
We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD).
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models
Even though the large-scale language models have achieved excellent performances, they suffer from various adversarial attacks.
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models
We then design a Model Uncertainty--aware Knowledge Integration (MUKI) framework to recover the golden supervision for the student.
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Warning: this paper contains model outputs exhibiting offensiveness and biases.
LF-SLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents
We collect the PALVIO dataset using a Panoramic Annular Lens (PAL) system with an entire FoV of 360\deg x(40\deg-120\deg) and IMU sensor to address the lack of panoramic SLAM datasets.
Manual-Guided Dialogue for Flexible Conversational Agents
Modeling these subtasks is consistent with the human agent's behavior patterns.
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset
To our knowledge, RSOD is the first quantitatively evaluated and graded snowy OD dataset.
Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LT$^2$LR-Net)
While low-rank matrix prior has been exploited in dynamic MR image reconstruction and has obtained satisfying performance, tensor low-rank models have recently emerged as powerful alternative representations for three-dimensional dynamic MR datasets.
A Template-based Method for Constrained Neural Machine Translation
In this work, we propose a template-based method that can yield results with high translation quality and match accuracy and the inference speed of our method is comparable with unconstrained NMT models.
A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation
We employ the Hebbian rule operating in local compartments to update synaptic weights and achieve supervised learning in a biologically plausible manner.
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation
Existing reference-free metrics have obvious limitations for evaluating controlled text generation models.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
ELLE: Efficient Lifelong Pre-training for Emerging Data
We experiment ELLE with streaming data from 5 domains on BERT and GPT.
OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation
Light field disparity estimation is an essential task in computer vision with various applications.
A Simple but Effective Pluggable Entity Lookup Table for Pre-trained Language Models
Pre-trained language models (PLMs) cannot well recall rich factual knowledge of entities exhibited in large-scale corpora, especially those rare entities.
PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding
PanoFlow achieves state-of-the-art performance on the public OmniFlowNet and the established FlowScape benchmarks.
LF-VIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras with Negative Plane
To tackle this issue, we propose LF-VIO, a real-time VIO framework for cameras with extremely large FoV.
Binary Neural Networks as a general-propose compute paradigm for on-device computer vision
For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and establish a similar degree of general applicability in vision tasks.
Toward a More Populous Online Platform: The Economic Impacts of Compensated Reviews
In this paper, we study the effect of compensated reviews on non-compensated reviews by utilizing online reviews on 1, 240 auto shipping companies over a ten-year period from a transportation website.
Model Uncertainty-Aware Knowledge Amalgamation for Pre-Trained Language Models
As many fine-tuned pre-trained language models~(PLMs) with promising performance are generously released, investigating better ways to reuse these models is vital as it can greatly reduce the retraining computational cost and the potential environmental side-effects.
BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks
Our experiments show that the proposed framework demonstrates learning accuracy comparable to BP-based rules and may provide new insights on how learning is orchestrated in biological systems.
On Transferability of Prompt Tuning for Natural Language Processing
To explore whether we can improve PT via prompt transfer, we empirically investigate the transferability of soft prompts across different downstream tasks and PLMs in this work.
FedGraph: Federated Graph Learning with Intelligent Sampling
In this paper, we propose FedGraph for federated graph learning among multiple computing clients, each of which holds a subgraph.
Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer.
Knowledge Base Question Answering
Knowledge Graph Embedding
+1
Fixed-Time Convergent Distributed Observer Design of Linear Systems: A Kernel-Based Approach
The robust distributed state estimation for a class of continuous-time linear time-invariant systems is achieved by a novel kernel-based distributed observer, which, for the first time, ensures fixed-time convergence properties.
Exploring Universal Intrinsic Task Subspace via Prompt Tuning
In the experiments, we study diverse few-shot NLP tasks and surprisingly find that in a 250-dimensional subspace found with 100 tasks, by only tuning 250 free parameters, we can recover 97% and 83% of the full prompt tuning performance for 100 seen tasks (using different training data) and 20 unseen tasks, respectively, showing great generalization ability of the found intrinsic task subspace.
RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models
Motivated by this observation, we construct a word-based robustness-aware perturbation to distinguish poisoned samples from clean samples to defend against the backdoor attacks on natural language processing (NLP) models.
Topology-Imbalance Learning for Semi-Supervised Node Classification
The class imbalance problem, as an important issue in learning node representations, has drawn increasing attention from the community.
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts
In this work, we study the computational patterns of FFNs and observe that most inputs only activate a tiny ratio of neurons of FFNs.
Training Deep Spiking Neural Networks with Bio-plausible Learning Rules
There exists a marked cleavage between the biological plausible approaches and the practical backpropagation-based approaches on how to train a deep spiking neural network (DSNN) with better performance.
AutoNF: Automated Architecture Optimization of Normalizing Flows Using a Mixture Distribution Formulation
Although various flow models based on different transformations have been proposed, there still lacks a quantitative analysis of performance-cost trade-offs between different flows as well as a systematic way of constructing the best flow architecture.
Dynamic Knowledge Distillation for Pre-trained Language Models
Knowledge distillation~(KD) has been proved effective for compressing large-scale pre-trained language models.
GoG: Relation-aware Graph-over-Graph Network for Visual Dialog
Specifically, GoG consists of three sequential graphs: 1) H-Graph, which aims to capture coreference relations among dialog history; 2) History-aware Q-Graph, which aims to fully understand the question through capturing dependency relations between words based on coreference resolution on the dialog history; and 3) Question-aware I-Graph, which aims to capture the relations between objects in an image based on fully question representation.
Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation
Visual dialogue is a challenging task since it needs to answer a series of coherent questions on the basis of understanding the visual environment.
Packed Levitated Marker for Entity and Relation Extraction
In particular, we propose a neighborhood-oriented packing strategy, which considers the neighbor spans integrally to better model the entity boundary information.
Ranked #1 on
Named Entity Recognition (NER)
on Few-NERD (SUP)
WeChat Neural Machine Translation Systems for WMT21
This paper introduces WeChat AI's participation in WMT 2021 shared news translation task on English->Chinese, English->Japanese, Japanese->English and English->German.
Composing Recurrent Spiking Neural Networks using Locally-Recurrent Motifs and Risk-Mitigating Architectural Optimization
The small size of the motifs and sparse inter-motif connectivity leads to an RSNN architecture scalable to large network sizes.
Rethinking Stealthiness of Backdoor Attack against NLP Models
In this work, we point out a potential problem of current backdoor attacking research: its evaluation ignores the stealthiness of backdoor attacks, and most of existing backdoor attacking methods are not stealthy either to system deployers or to system users.
H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks
Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits their application in many practical tasks.
Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks
While backpropagation (BP) has been applied to spiking neural networks (SNNs) achieving encouraging results, a key challenge involved is to backpropagate a continuous-valued loss over layers of spiking neurons exhibiting discontinuous all-or-none firing activities.
Demonstration of Panda: A Weakly Supervised Entity Matching System
Panda's IDE includes many novel features purpose-built for EM, such as smart data sampling, a builtin library of EM utility functions, automatically generated LFs, visual debugging of LFs, and finally, an EM-specific labeling model.
Sample Recycling Method -- A New Approach to Efficient Nested Monte Carlo Simulations
The nesting of such stochastic modeling can be computationally challenging.
Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition
Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse.
Fully Hyperbolic Neural Networks
Hyperbolic neural networks have shown great potential for modeling complex data.
CLEVE: Contrastive Pre-training for Event Extraction
Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning.
Knowledge Inheritance for Pre-trained Language Models
Specifically, we introduce a pre-training framework named "knowledge inheritance" (KI) and explore how could knowledge distillation serve as auxiliary supervision during pre-training to efficiently learn larger PLMs.
Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction
Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale auto-labeled data.
CSS-LM: A Contrastive Framework for Semi-supervised Fine-tuning of Pre-trained Language Models
We then perform contrastive semi-supervised learning on both the retrieved unlabeled and original labeled instances to help PLMs capture crucial task-related semantic features.
Relief and Stimulus in A Cross-sector Multi-product Scarce Resource Supply Chain Network
In the era of a growing population, systemic changes to the world, and the rising risk of crises, humanity has been facing an unprecedented challenge of resource scarcity.
Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films
We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).
Mesoscale and Nanoscale Physics
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning
Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks.
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade
On the other hand, the exiting decisions made by internal classifiers are unreliable, leading to wrongly emitted early predictions.
Topological Hall Effect in a Topological Insulator Interfaced with a Magnetic Insulator
A topological insulator (TI) interfaced with a magnetic insulator (MI) may host an anomalous Hall effect (AHE), a quantum AHE, and a topological Hall effect (THE).
Materials Science Mesoscale and Nanoscale Physics Applied Physics
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification
Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC).
Neural Gibbs Sampling for Joint Event Argument Extraction
Existing EAE methods either extract each event argument roles independently or sequentially, which cannot adequately model the joint probability distribution among event arguments and their roles.
Charge density wave and weak Kondo effect in a Dirac semimetal CeSbTe
Using angle-resolved photoemission spectroscopy (ARPES) and low-energy electron diffraction (LEED), together with density-functional theory (DFT) calculation, we report the formation of charge density wave (CDW) and its interplay with the Kondo effect and topological states in CeSbTe.
Strongly Correlated Electrons Materials Science
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose.
Using simulation to incorporate dynamic criteria into multiple criteria decision-making
In this paper, we present a case study demonstrating how dynamic and uncertain criteria can be incorporated into a multicriteria analysis with the help of discrete event simulation.
DisenE: Disentangling Knowledge Graph Embeddings
Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently.
Skip-Connected Self-Recurrent Spiking Neural Networks with Joint Intrinsic Parameter and Synaptic Weight Training
Moreover, we propose a new backpropagation (BP) method called backpropagated intrinsic plasticity (BIP) to further boost the performance of ScSr-SNNs by training intrinsic model parameters.
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection
As conventional answer selection (AS) methods generally match the question with each candidate answer independently, they suffer from the lack of matching information between the question and the candidate.
Disentangle-based Continual Graph Representation Learning
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data.
Learning from Context or Names? An Empirical Study on Neural Relation Extraction
We find that (i) while context is the main source to support the predictions, RE models also heavily rely on the information from entity mentions, most of which is type information, and (ii) existing datasets may leak shallow heuristics via entity mentions and thus contribute to the high performance on RE benchmarks.
Ranked #24 on
Relation Extraction
on TACRED
WeChat Neural Machine Translation Systems for WMT20
We participate in the WMT 2020 shared news translation task on Chinese to English.
CokeBERT: Contextual Knowledge Selection and Embedding towards Enhanced Pre-Trained Language Models
In this paper, we propose a novel framework named Coke to dynamically select contextual knowledge and embed knowledge context according to textual context for PLMs, which can avoid the effect of redundant and ambiguous knowledge in KGs that cannot match the input text.
Detector tilt considerations in high-energy Bragg coherent diffraction imaging: a simulation study
This paper addresses three-dimensional signal distortion and image reconstruction issues in x-ray Bragg coherent diffraction imaging (BCDI) in the event of a non-trivial, non-orthogonal orientation of the area detector with respect to the diffracted beam.
Instrumentation and Detectors Image and Video Processing
Continual Relation Learning via Episodic Memory Activation and Reconsolidation
Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations.
Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation
By dynamically determining segment length and deleting repetitive segments, RecoverSAT is capable of recovering from repetitive and missing token errors.
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry.
Making Robots Draw A Vivid Portrait In Two Minutes
The whole portrait drawing robotic system is named AiSketcher.
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions
Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data.
MAVEN: A Massive General Domain Event Detection Dataset
Most existing datasets exhibit the following issues that limit further development of ED: (1) Data scarcity.
Coreferential Reasoning Learning for Language Representation
Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of downstream NLP tasks with appropriate fine-tuning.
Ranked #30 on
Relation Extraction
on DocRED
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
Relational facts are an important component of human knowledge, which are hidden in vast amounts of text.
Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
Spiking neural networks (SNNs) are well suited for spatio-temporal learning and implementations on energy-efficient event-driven neuromorphic processors.
Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient
Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given Spatio-temporal gradient maps.
DMRM: A Dual-channel Multi-hop Reasoning Model for Visual Dialog
Visual Dialog is a vision-language task that requires an AI agent to engage in a conversation with humans grounded in an image.
AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization
The point process is a solid framework to model sequential data, such as videos, by exploring the underlying relevance.
Implement Liquid Democracy on Ethereum: A Fast Algorithm for Realtime Self-tally Voting System
We study the liquid democracy problem, where each voter can either directly vote to a candidate or delegate his voting power to a proxy.
Cryptography and Security
HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label.
Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information
Non-autoregressive neural machine translation (NAT) generates each target word in parallel and has achieved promising inference acceleration.
Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency.
HMEAE: Hierarchical Modular Event Argument Extraction
Existing event extraction methods classify each argument role independently, ignoring the conceptual correlations between different argument roles.
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
We present FewRel 2. 0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances?
NumNet: Machine Reading Comprehension with Numerical Reasoning
Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human's reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems.
Ranked #9 on
Question Answering
on DROP Test
Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators using Time Compression Supporting Multiple Spike Codes
Using spiking speech and image recognition datasets, we demonstrate the feasibility of supporting large time compression ratios of up to 16x, delivering up to 15. 93x, 13. 88x, and 86. 21x improvements in throughput, energy dissipation, the tradeoffs between hardware area, runtime, energy, and classification accuracy, respectively based on different spike codes on a Xilinx Zynq-7000 FPGA.
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
Graph Neural Networks (GNNs) have achieved promising performance on a wide range of graph-based tasks.
Ranked #49 on
Node Classification
on Cora
Quantifying and Correlating Rhythm Formants in Speech
Consequently, only the LF LTS of the absolute speech signal is used in the empirical analysis.
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
However, the practical application of RSNNs is severely limited by challenges in training.
FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks
Given the results of MTN, we adopt an occlusion-aware Re-ID feature strategy in the pose tracking module, where pose information is utilized to infer the occlusion state to make better use of Re-ID feature.
General approaches for shear-correcting coordinate transformations in Bragg coherent diffraction imaging: Part 2
To deal with this, the currently favored approach (detailed in Part I) is to perform the entire inversion in conjugate non-orthogonal real and Fourier space frames, and to transform the 3D sample image into an orthogonal frame as a post-processing step for result analysis.
Instrumentation and Detectors Signal Processing
General approaches for shear-correcting coordinate transformations in Bragg coherent diffraction imaging: Part 1
Part II builds upon the geometric theory developed in Part I with the formalism to correct the shear distortions directly on an orthogonal grid within the phase retrieval algorithm itself, allowing more physically realistic constraints to be applied.
Instrumentation and Detectors
Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation
We propose a novel model to separate the generation into two stages: key fact prediction and surface realization.
Towards Fine-grained Text Sentiment Transfer
In this paper, we focus on the task of fine-grained text sentiment transfer (FGST).
Global Adversarial Attacks for Assessing Deep Learning Robustness
It has been shown that deep neural networks (DNNs) may be vulnerable to adversarial attacks, raising the concern on their robustness particularly for safety-critical applications.
SAVIOR: Towards Bug-Driven Hybrid Testing
Unlike the existing hybrid testing tools, SAVIOR prioritizes the concolic execution of the seeds that are likely to uncover more vulnerabilities.
Software Engineering
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs.
Ranked #58 on
Relation Extraction
on DocRED
Exploiting Offset-guided Network for Pose Estimation and Tracking
Human pose estimation has witnessed a significant advance thanks to the development of deep learning.
State-aware Re-identification Feature for Multi-target Multi-camera Tracking
Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories from videos captured by a set of cameras.
Adversarial Training for Weakly Supervised Event Detection
Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled data.
A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style.
Ranked #1 on
Unsupervised Text Style Transfer
on GYAFC
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks
Data quality affects machine learning (ML) model performances, and data scientists spend considerable amount of time on data cleaning before model training.
Option Comparison Network for Multiple-choice Reading Comprehension
However, humans typically compare the options at multiple-granularity level before reading the article in detail to make reasoning more efficient.
Ranked #2 on
Question Answering
on RACE
Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning
Sensor fusion has wide applications in many domains including health care and autonomous systems.
Evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support: three decades development course and future prospect
Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening.
Efficient Two-Step Adversarial Defense for Deep Neural Networks
However, the computational cost of the adversarial training with PGD and other multi-step adversarial examples is much higher than that of the adversarial training with other simpler attack techniques.
Optimized Gated Deep Learning Architectures for Sensor Fusion
Sensor fusion is a key technology that integrates various sensory inputs to allow for robust decision making in many applications such as autonomous driving and robot control.
Hierarchical Relation Extraction with Coarse-to-Fine Grained Attention
In this paper, we aim to incorporate the hierarchical information of relations for distantly supervised relation extraction and propose a novel hierarchical attention scheme.
Image Captioning based on Deep Reinforcement Learning
However, in this paper, we propose a novel architecture for image captioning with deep reinforcement learning to optimize image captioning tasks.
Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks
We evaluate the proposed HM2-BP algorithm by training deep fully connected and convolutional SNNs based on the static MNIST [14] and dynamic neuromorphic N-MNIST [26].
Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks
In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE.
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.
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
Clinical Information Extraction via Convolutional Neural Network
We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pathology reports.
Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding
Neural network based approaches for sentence relation modeling automatically generate hidden matching features from raw sentence pairs.
