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Found 182 papers, 70 papers with code
基于规则的双重否定识别——以“不v1不v2”为例(Double Negative Recognition Based on Rules——Taking “不v1不v2” as an Example)
“不v1不v2”是汉语中典型的双重否定结构形式之一, 它包括“不+助动词+不+v2”(不得不去)、“不+是+不v2”(不是不好)、述宾结构“不v1... 不v2”(不认为他不去)等多种双重否定结构, 情况复杂。本文以“不v1不v2”为例, 结合“元语否定”、“动词叙实性”、“否定焦点”等概念, 对“不v1不v2”进行了全面的考察, 制定了“不v1不v2”双重否定结构的识别策略。根据识别策略, 设计了双重否定自动识别程序, 并在此过程中补充了助动词表、非叙实动词表等词库。最终, 对28033句语料进行了识别, 识别正确率为97. 87%, 召回率约为93. 10%。
CNNSAT: Fast, Accurate Boolean Satisfiability using Convolutional Neural Networks
Boolean satisfiability (SAT) is one of the most well-known NP-complete problems and has been extensively studied.
NEURAL MALWARE CONTROL WITH DEEP REINFORCEMENT LEARNING
Antimalware products are a key component in detecting malware attacks, and their engines typically execute unknown programs in a sandbox prior to running them on the native operating system.
HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction
Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary.
Pseudo-Masked Language Models for Unified Language Model Pre-Training
We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).
Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation
With the help of deep learning methods, the automatic identification or segmentation of nerves can be realized, assisting doctors in completing nerve block anesthesia accurately and efficiently.
BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification
CT-based bronchial tree analysis plays an important role in the computer-aided diagnosis for respiratory diseases, as it could provide structured information for clinicians.
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization
To extend the reconstruction-based anomaly detection architecture to the localized anomalies, we propose a self-supervised learning approach through random masking and then restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly detection and localization.
GypSum: Learning Hybrid Representations for Code Summarization
Current deep learning models for code summarization generally follow the principle in neural machine translation and adopt the encoder-decoder framework, where the encoder learns the semantic representations from source code and the decoder transforms the learnt representations into human-readable text that describes the functionality of code snippets.
R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction
In this paper, we present a novel approach for FGVC, which can simultaneously make use of partial yet sufficient discriminative information in environmental cues and also compress the redundant information in class-token with respect to the target.
Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data
We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours.
Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees
Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality.
Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation
Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS), is a challenging task that has attracted much attention recently.
Ranked #17 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Semi-Supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance
We propose CodedVTR (Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers.
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well studied as other domains, such as computer vision and natural language processing.
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation
Then the tensor was mapped to a matrix which was used to mix the one-hot encoded labels of the above image patches.
ChemicalX: A Deep Learning Library for Drug Pair Scoring
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task.
Evaluating the impact of quarantine measures on COVID-19 spread
We generate counterfactual simulations to estimate effectiveness of quarantine measures.
Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.
Improving Out-of-Distribution Robustness via Selective Augmentation
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution.
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration
Our work reveals a structured shortcoming of the existing mainstream self-supervised learning methods.
Transferrable Contrastive Learning for Visual Domain Adaptation
From this point, we present a particular paradigm of self-supervised learning tailored for domain adaptation, i. e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently.
A Style and Semantic Memory Mechanism for Domain Generalization
Correspondingly, we also propose a novel "jury" mechanism, which is particularly effective in learning useful semantic feature commonalities among domains.
Ranked #15 on
Domain Generalization
on PACS
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward
These scenarios indeed correspond to the vulnerabilities of the under-test driving policies, thus are meaningful for their further improvements.
Multiway Ensemble Kalman Filter
In this work, we study the emergence of sparsity and multiway structures in second-order statistical characterizations of dynamical processes governed by partial differential equations (PDEs).
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess
The advent of machine learning models that surpass human decision-making ability in complex domains has initiated a movement towards building AI systems that interact with humans.
Imbalanced Graph Classification via Graph-of-Graph Neural Networks
Graph Neural Networks (GNNs) have achieved unprecedented success in learning graph representations to identify categorical labels of graphs.
Learning Dynamic Compact Memory Embedding for Deformable Visual Object Tracking
To address this issue, segmentation-based trackers have been proposed that employ per-pixel matching to improve the tracking performance of deformable objects effectively.
Learning Dynamic Preference Structure Embedding From Temporal Networks
Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning
In this paper, we propose a distributed formation and obstacle avoidance method based on multi-agent reinforcement learning (MARL).
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.
SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae
Spinal degeneration plagues many elders, office workers, and even the younger generations.
Meta-learning with an Adaptive Task Scheduler
In ATS, for the first time, we design a neural scheduler to decide which meta-training tasks to use next by predicting the probability being sampled for each candidate task, and train the scheduler to optimize the generalization capacity of the meta-model to unseen tasks.
Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification
Recent years have witnessed the significant success of applying graph neural networks (GNNs) in learning effective node representations for classification.
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
The same data are propagated through the graph structure to perform the same neural operation multiple times in GNNs, leading to redundant computation which accounts for 92. 4% of total operators.
Learning Efficient Multi-Agent Cooperative Visual Exploration
In this paper, we extend the state-of-the-art single-agent visual navigation method, Active Neural SLAM (ANS), to the multi-agent setting by introducing a novel RL-based planning module, Multi-agent Spatial Planner (MSP). MSP leverages a transformer-based architecture, Spatial-TeamFormer, which effectively captures spatial relations and intra-agent interactions via hierarchical spatial self-attentions.
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.
BoolNet: Streamlining Binary Neural Networks Using Binary Feature Maps
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts, often based on specialized model designs using additional 32-bit components.
Efficient Context-Aware Network for Abdominal Multi-organ Segmentation
For the context block, we propose strip pooling module to capture anisotropic and long-range contextual information, which exists in abdominal scene.
Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models
To reduce the computational cost without sacrificing inferential accuracy, we propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and surrogate model parameters.
DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN
In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.
Tree Decomposed Graph Neural Network
Nevertheless, iterative propagation restricts the information of higher-layer neighborhoods to be transported through and fused with the lower-layer neighborhoods', which unavoidably results in feature smoothing between neighborhoods in different layers and can thus compromise the performance, especially on heterophily networks.
Ranked #10 on
Node Classification
on Cornell
A Low Rank Promoting Prior for Unsupervised Contrastive Learning
In this paper, we construct a novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC.
Chase: A Large-Scale and Pragmatic Chinese Dataset for Cross-Database Context-Dependent Text-to-SQL
However, we identify two biases in existing datasets for XDTS: (1) a high proportion of context-independent questions and (2) a high proportion of easy SQL queries.
MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain Adaptation
Due to the increasing concerns for data privacy, source-free unsupervised domain adaptation attracts more and more research attention, where only a trained source model is assumed to be available, while the labeled source data remain private.
United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks
Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification.
Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification
To learn the direct influence among output nodes in a graph, we propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field.
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months.
BoolNet: Minimizing The Energy Consumption of Binary Neural Networks
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts.
An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction
In this paper, we introduce a novel multi-step spoken language understanding system based on adversarial learning that can leverage the multiround user's feedback to update slot values.
A Coarse to Fine Question Answering System based on Reinforcement Learning
In this paper, we present a coarse to fine question answering (CFQA) system based on reinforcement learning which can efficiently processes documents with different lengths by choosing appropriate actions.
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model
We propose a new graphical model inference procedure, called SG-PALM, for learning conditional dependency structure of high-dimensional tensor-variate data.
Learning Robust Recommenders through Cross-Model Agreement
A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.
Integrated Communication and Navigation for Ultra-Dense LEO Satellite Networks: Vision, Challenges and Solutions
Next generation beyond 5G networks are expected to provide both Terabits per second data rate communication services and centimeter-level accuracy localization services in an efficient, seamless and cost-effective manner.
Domain-Specific Suppression for Adaptive Object Detection
The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one.
Unsupervised Remote Sensing Super-Resolution via Migration Image Prior
Despite their success, however, low/high spatial resolution pairs are usually difficult to obtain in satellites with a high temporal resolution, making such approaches in SR impractical to use.
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer
We firstly pre-train a transformer with sequences in a reverse direction to predict prior items.
Membership Inference Attacks on Knowledge Graphs
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.
Adversarial Robustness under Long-Tailed Distribution
We then perform a systematic study on existing long-tailed recognition methods in conjunction with the adversarial training framework.
ExKaldi-RT: A Real-Time Automatic Speech Recognition Extension Toolkit of Kaldi
This paper describes the ExKaldi-RT online automatic speech recognition (ASR) toolkit that is implemented based on the Kaldi ASR toolkit and Python language.
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web
Then we learn entity embeddings through this new type of triples.
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness
It provides high scalability because the paths within an EIO network exponentially increase with the network depth.
Model-Free Learning of Safe yet Effective Controllers
We study the problem of learning safe control policies that are also effective; i. e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task, and the discounted reward capturing the (classic) control performance.
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning
In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL.
Track to Detect and Segment: An Online Multi-Object Tracker
Most online multi-object trackers perform object detection stand-alone in a neural net without any input from tracking.
Ranked #1 on
Online Multi-Object Tracking
on MOT16
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
Aspect sentiment triplet extraction (ASTE), which aims to identify aspects from review sentences along with their corresponding opinion expressions and sentiments, is an emerging task in fine-grained opinion mining.
Aspect Sentiment Triplet Extraction
Machine Reading Comprehension
+1
Learning-Based Vulnerability Analysis of Cyber-Physical Systems
This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS).
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization
We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings.
CogDL: A Toolkit for Deep Learning on Graphs
Deep learning on graphs has attracted tremendous attention from the graph learning community in recent years.
Provable Boolean Interaction Recovery from Tree Ensemble obtained via Random Forests
Iterative Random Forests (iRF) use a tree ensemble from iteratively modified RF to obtain predictive and stable non-linear or Boolean interactions of features.
Statistics Theory Statistics Theory
Stability of scar states in 2D PXP model against random disorders
Recently a class of quantum systems exhibiting weak ergodicity breaking has attracted much attention.
Disordered Systems and Neural Networks Statistical Mechanics
Dissecting Energy Budget of a Gamma-Ray Burst Fireball
The jet composition and radiative efficiency of GRBs are poorly constrained from the data.
High Energy Astrophysical Phenomena
WheaCha: A Method for Explaining the Predictions of Code Summarization Models
The last decade has witnessed a rapid advance in machine learning models.
Learning Optimal Strategies for Temporal Tasks in Stochastic Games
By deriving distinct rewards and discount factors from the acceptance condition of the DPA, we reduce the maximization of the worst-case probability of satisfying the LTL specification into the maximization of a discounted reward objective in the product game; this allows for the use of model-free RL algorithms to learn an optimal controller strategy.
Enabling Lower-Power Charge-Domain Nonvolatile In-Memory Computing with Ferroelectric FETs
Compute-in-memory (CiM) is a promising approach to alleviating the memory wall problem for domain-specific applications.
Emerging Technologies
Machine Learning for Electronic Design Automation: A Survey
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing.
Explore the Potential of CNN Low Bit Training
Through this dynamic precision framework, we can reduce the bit-width of convolution, which is the most computational cost, while keeping the training process close to the full precision floating-point training.
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms
We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games.
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach
In this paper, considering scenarios with capacity budget, we aim to discover adversarially robust architecture at targeted capacities.
TRACE: Early Detection of Chronic Kidney Disease Onset with Transformer-Enhanced Feature Embedding
The early detection of CKD faces challenges of insufficient medical histories of positive patients and complicated risk factors.
Learning to Adapt to Evolving Domains
(2) Since the target data arrive online, the agent should also maintain competence on previous target domains, i. e. to adapt without forgetting.
aw_nas: A Modularized and Extensible NAS framework
Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner.
BARS: Joint Search of Cell Topology and Layout for Accurate and Efficient Binary ARchitectures
We also design the micro-level search space to strengthen the information flow for BNN.
Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes
Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of vision-based robots.
A Learning-Based Tune-Free Control Framework for Large Scale Autonomous Driving System Deployment
This paper presents the design of a tune-free (human-out-of-the-loop parameter tuning) control framework, aiming at accelerating large scale autonomous driving system deployed on various vehicles and driving environments.
Face Hallucination via Split-Attention in Split-Attention Network
However, most of them fail to take into account the overall facial profile and fine texture details simultaneously, resulting in reduced naturalness and fidelity of the reconstructed face, and further impairing the performance of downstream tasks (e. g., face detection, facial recognition).
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?
Task-agnostic forms of data augmentation have proven widely effective in computer vision, even on pretrained models.
Joint Contrastive Learning with Infinite Possibilities
This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling.
A Surgery of the Neural Architecture Evaluators
A major challenge in NAS is to conduct a fast and accurate evaluation of neural architectures.
A Survey of FPGA-Based Robotic Computing
On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios.
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
In this article, we describe our data collection procedure and propose evaluation metrics for multilabel classification of urban sound tags.
Contrastively-reinforced Attention Convolutional Neural Network for Fine-grained Image Recognition
The evaluation information is backpropagated and forces the classification stream to improve its awareness of visual attention, which helps classification.
Ranked #17 on
Fine-Grained Image Classification
on Stanford Cars
Joint Visual and Wireless Signal Feature based Approach for High-Precision Indoor Localization
The existing localization systems for indoor applications basically rely on wireless signal.
Shift Equivariance in Object Detection
Robustness to small image translations is a highly desirable property for object detectors.
Evaluating Efficient Performance Estimators of Neural Architectures
Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS).
Probabilistic Conformance for Cyber-Physical Systems
In system analysis, conformance indicates that two systems simultaneously satisfy the same set of specifications of interest; thus, the results from analyzing one system automatically transfer to the other, or one system can safely replace the other in practice.
Physical Adversarial Attack on Vehicle Detector in the Carla Simulator
In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild.
Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
We present a new loss function called Distribution-Balanced Loss for the multi-label recognition problems that exhibit long-tailed class distributions.
ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing
Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids.
Ranked #1 on
Protein Secondary Structure Prediction
on CB513
Dimensionality Reduction
Protein Secondary Structure Prediction
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification
Some works in the optimization of deep neural networks have shown that a better arrangement of training data can make the classifier converge faster and perform better.
GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks
GE-SpMM performs SpMM-like operation on sparse matrices represented in the most common Compressed Sparse Row (CSR) format, so it can be embedded in GNN frameworks with no preprocessing overheads and support general GNN algorithms.
Distributed, Parallel, and Cluster Computing
Monitoring Depression Trend on Twitter during the COVID-19 Pandemic
The COVID-19 pandemic has severely affected people's daily lives and caused tremendous economic loss worldwide.
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction
The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously.
2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection
A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons.
1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation
We proposed a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet, using object key-points to encode the 3D attributes, and to learn an end-to-end point cloud object detection without the need of hand-engineering or learning the anchors.
1st Place Solutions for Waymo Open Dataset Challenges -- 2D and 3D Tracking
This technical report presents the online and real-time 2D and 3D multi-object tracking (MOT) algorithms that reached the 1st places on both Waymo Open Dataset 2D tracking and 3D tracking challenges.
AFDet: Anchor Free One Stage 3D Object Detection
High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving.
Deep Learning for Stable Monotone Dynamical Systems
Monotone systems, originating from real-world (e. g., biological or chemical) applications, are a class of dynamical systems that preserves a partial order of system states over time.
Learning a Unified Sample Weighting Network for Object Detection
To this end, we devise a general loss function to cover most region-based object detectors with various sampling strategies, and then based on it we propose a unified sample weighting network to predict a sample's task weights.
Exploring the Potential of Low-bit Training of Convolutional Neural Networks
For training a variety of models on CIFAR-10, using 1-bit mantissa and 2-bit exponent is adequate to keep the accuracy loss within $1\%$.
Precisely Predicting Acute Kidney Injury with Convolutional Neural Network Based on Electronic Health Record Data
Compared with the existing AKI predictors, the predictor in this work greatly improves the precision of early prediction of AKI by using the Convolutional Neural Network architecture and a more concise input vector.
A Complex KBQA System using Multiple Reasoning Paths
Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding.
Learning Semantic Program Embeddings with Graph Interval Neural Network
We have also created a neural bug detector based on GINN to catch null pointer deference bugs in Java code.
Curating a COVID-19 data repository and forecasting county-level death counts in the United States
We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead.
Energy-Aware DNN Graph Optimization
Unlike existing work in deep neural network (DNN) graphs optimization for inference performance, we explore DNN graph optimization for energy awareness and savings for power- and resource-constrained machine learning devices.
A Comprehensive Survey of Grammar Error Correction
More importantly, we discuss four kinds of basic approaches, including statistical machine translation based approach, neural machine translation based approach, classification based approach and language model based approach, six commonly applied performance boosting techniques for GEC systems and two data augmentation methods.
In the Eyes of the Beholder: Analyzing Social Media Use of Neutral and Controversial Terms for COVID-19
To model the substantive difference of tweets with controversial terms and those with non-controversial terms, we apply topic modeling and LIWC-based sentiment analysis.
The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic
Recently, the pandemic of the novel Coronavirus Disease-2019 (COVID-19) has presented governments with ultimate challenges.
Adversarial Training for Large Neural Language Models
In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning.
Ranked #3 on
Natural Language Inference
on ANLI test
(using extra training data)
Robust Beamforming Design for Intelligent Reflecting Surface Aided Cognitive Radio Systems with Imperfect Cascaded CSI
Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's minimum transmit power and feasibility rate of the optimization problems.
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation
In budgeted pruning, how to distribute the resources across layers (i. e., sparsity allocation) is the key problem.
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor.
Differential Privacy for Sequential Algorithms
Using the two examples: sequential probability ratio test and sequential empirical risk minimization, we show that the number of steps such algorithms execute before termination can jeopardize the differential privacy of the input data in a similar fashion as their outputs, and it is impossible to use the usual Laplace mechanism to achieve standard differentially private in these examples.
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning
Probabilistic Computation Tree Logic (PCTL) is frequently used to formally specify control objectives such as probabilistic reachability and safety.
Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud
Currently, the majority of FPGA-based DNN accelerators in the cloud run in a time-division multiplexing way for multiple users sharing a single FPGA, and require re-compilation with $\sim$100 s overhead.
Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks
In this paper, we introduce a novel gating mechanism to deep neural networks.
FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture
By inspecting the discovered architectures, we find that the operation primitives, the weight quantization range, the capacity of the model, and the connection pattern have influences on the fault resilience capability of NN models.
UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training
We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).
Ranked #3 on
Question Generation
on SQuAD1.1
(using extra training data)
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models.
Application of Pre-training Models in Named Entity Recognition
Then, we apply these pre-training models to a NER task by fine-tuning, and compare the effects of the different model architecture and pre-training tasks on the NER task.
The Sylvester Graphical Lasso (SyGlasso)
We apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects.
SQLFlow: A Bridge between SQL and Machine Learning
Previous database systems extended their SQL dialect to support ML.
Communication Lower Bound in Convolution Accelerators
Evaluations based on the 65nm technology demonstrate that the proposed architecture nearly reaches the theoretical minimum communication in a three-level memory hierarchy and it is computation dominant.
Distributed, Parallel, and Cluster Computing Hardware Architecture
Review of Learning-based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps between Self-driving and Traffic Congestion
Some suggestions towards congestion mitigation for future mMP studies are proposed: i) enrich data collection to facilitate the congestion learning, ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and iii) integrate domain knowledge from the traditional car following (CF) theory to improve the string stability of mMP.
Non-native Speaker Verification for Spoken Language Assessment
These systems are explored for non-native spoken English data in this paper.
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning
We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP).
Single Training Dimension Selection for Word Embedding with PCA
In this paper, we present a fast and reliable method based on PCA to select the number of dimensions for word embeddings.
New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning
With the development of artificial intelligence algorithms like deep learning models and the successful applications in many different fields, further similar trails of deep learning technology have been made in cyber security area.
Learning a Static Bug Finder from Data
In a cross-project prediction task, three neural bug detectors we instantiate from NeurSA are effective in catching null pointer dereference, array index out of bound and class cast bugs in unseen code.
A Debiased MDI Feature Importance Measure for Random Forests
Based on the original definition of MDI by Breiman et al. for a single tree, we derive a tight non-asymptotic bound on the expected bias of MDI importance of noisy features, showing that deep trees have higher (expected) feature selection bias than shallow ones.
ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation
The whole network has nearly symmetric architecture, which is mainly composed of a series of factorized convolution unit (FCU) and its parallel counterparts (PFCU).
Ranked #25 on
Real-Time Semantic Segmentation
on Cityscapes test
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution shift.
Contribution of Radio Halos to the Foreground for SKA EoR Experiments
The overwhelming foreground contamination is one of the primary impediments to probing the EoR through measuring the redshifted 21 cm signal.
Cosmology and Nongalactic Astrophysics
Unified Language Model Pre-training for Natural Language Understanding and Generation
This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.
Ranked #2 on
Generative Question Answering
on CoQA
(using extra training data)
LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation
Ranked #26 on
Real-Time Semantic Segmentation
on Cityscapes test
Transferrable Prototypical Networks for Unsupervised Domain Adaptation
Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar.
Generalizable control for quantum parameter estimation through reinforcement learning
Measurement and estimation of parameters are essential for science and engineering, where one of the main quests is to find systematic schemes that can achieve high precision.
Quantum Physics Mesoscale and Nanoscale Physics
Unique Sharp Local Minimum in $\ell_1$-minimization Complete Dictionary Learning
First, we obtain a necessary and sufficient norm condition for the reference dictionary $D^*$ to be a sharp local minimum of the expected $\ell_1$ objective function.
DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators
On the Xilinx ZU2 @330 MHz and ZU9 @330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by na\"ive implementations without optimizations, and the throughput is further improved up to 1. 26x by leveraging heterogeneous optimizations in DNNVM.
A New Concept of Deep Reinforcement Learning based Augmented General Sequence Tagging System
In this paper, a new deep reinforcement learning based augmented general sequence tagging system is proposed.
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
The most effective algorithms are based on the structures of sequence to sequence models (or "encoder-decoder" models), and generate the intents and semantic tags either using separate models or a joint model.
Ranked #1 on
Slot Filling
on ATIS
Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks
These errors are not accounted for by the standard confidence estimation schemes and are hard to rectify in the upstream and downstream processing.
A Neural Transition-based Model for Nested Mention Recognition
It is common that entity mentions can contain other mentions recursively.
Ranked #6 on
Nested Named Entity Recognition
on NNE
User Information Augmented Semantic Frame Parsing using Coarse-to-Fine Neural Networks
The results show that our approach leverages such simple user information to outperform state-of-the-art approaches by 0. 25% for intent detection and 0. 31% for slot filling using standard training data.
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System
In this paper, a new deep reinforcement learning based augmented general tagging system is proposed.
Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems
This proposal introduces a Dialogue Challenge for building end-to-end task-completion dialogue systems, with the goal of encouraging the dialogue research community to collaborate and benchmark on standard datasets and unified experimental environment.
Nonparametric Topic Modeling with Neural Inference
Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner.
A Deeply-Recursive Convolutional Network for Crowd Counting
The estimation of crowd count in images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning.
Hu-Fu: Hardware and Software Collaborative Attack Framework against Neural Networks
So, in this paper, we propose a hardware-software collaborative attack framework to inject hidden neural network Trojans, which works as a back-door without requiring manipulating input images and is flexible for different scenarios.
A Counter-Forensic Method for CNN-Based Camera Model Identification
We examine in this paper the problem of identifying the camera model or type that was used to take an image and that can be spoofed.
Automated Big Traffic Analytics for Cyber Security
Network traffic analytics technology is a cornerstone for cyber security systems.
Cryptography and Security
PoTrojan: powerful neural-level trojan designs in deep learning models
With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life.
Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription
As the combination approaches become more complicated the difference between the phonetic and graphemic systems further decreases.
A Bayesian Nonparametric Topic Model with Variational Auto-Encoders
On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner.
A Survey of FPGA Based Neural Network Accelerator
Various FPGA based accelerator designs have been proposed with software and hardware optimization techniques to achieve high speed and energy efficiency.
Hardware Architecture
Deep Gradient Compression Reduce the Communication Bandwidth For distributed Traning
Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure.
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections.