Search Results for author:
Found 368 papers, 147 papers with code
Sparse K-Means with $\ell_{\infty}/\ell_0$ Penalty for High-Dimensional Data Clustering
Nevertheless, this framework has two serious drawbacks: One is that the solution of the framework unavoidably involves a considerable portion of redundant noise features in many situations, and the other is that the framework neither offers intuitive explanations on why this framework can select relevant features nor leads to any theoretical guarantee for feature selection consistency.
Improving the Speed of Response of Learning Algorithms Using Multiple Models
During the past two decades, the first author has worked on numerous methods for improving the stability, robustness, and performance of adaptive systems using multiple models and the other authors have collaborated with him on some of them.
Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning
We find that Chinese, Japanese and Koreans do exhibit substantial differences in certain attributes, such as bangs, smiling, and bushy eyebrows.
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Evaluated on the LSTM for speech recognition benchmark, ESE is 43x and 3x faster than Core i7 5930k CPU and Pascal Titan X GPU implementations.
Fast Change Point Detection on Dynamic Social Networks
In this work we devise an effective and efficient three-step-approach for detecting change points in dynamic networks under the snapshot model.
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Since memory reference is more than two orders of magnitude more expensive than arithmetic operations, the regularity of sparse structure leads to more efficient hardware design.
Hidden Talents of the Variational Autoencoder
Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying distribution.
A Deep Learning Approach for Blind Drift Calibration of Sensor Networks
Temporal drift of sensory data is a severe problem impacting the data quality of wireless sensor networks (WSNs).
Steklov Spectral Geometry for Extrinsic Shape Analysis
We propose using the Dirichlet-to-Neumann operator as an extrinsic alternative to the Laplacian for spectral geometry processing and shape analysis.
Graphics
Future Word Contexts in Neural Network Language Models
Instead of using a recurrent unit to capture the complete future word contexts, a feedforward unit is used to model a finite number of succeeding, future, words.
LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions
We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information.
Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems
This paper proposes Telepath, a vision-based bionic recommender system model, which understands users from such perspective.
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.
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.
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
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.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Automated Big Traffic Analytics for Cyber Security
Network traffic analytics technology is a cornerstone for cyber security systems.
Cryptography and Security
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.
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 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.
Nonparametric Topic Modeling with Neural Inference
Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner.
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.
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.
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 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
Nested Mention Recognition
Nested Named Entity Recognition
+1
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 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
Intent Detection
on ATIS
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.
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.
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.
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
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.
LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation
LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation
Ranked #29 on
Real-Time Semantic Segmentation
on Cityscapes test
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)
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
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.
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 #28 on
Real-Time Semantic Segmentation
on Cityscapes test
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.
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.
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.
Multi-view Deep Subspace Clustering Networks
Dnet learns view-specific self-representation matrices, whereas Unet learns a common self-representation matrix for all views.
Ranked #1 on
Multi-view Subspace Clustering
on ORL
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.
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).
Non-native Speaker Verification for Spoken Language Assessment
These systems are explored for non-native spoken English data in this paper.
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.
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
SQLFlow: A Bridge between SQL and Machine Learning
Previous database systems extended their SQL dialect to support ML.
The Sylvester Graphical Lasso (SyGlasso)
We apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects.
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 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.
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 #4 on
Question Generation
on SQuAD1.1
(using extra training data)
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.
Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks
In this paper, we introduce a novel gating mechanism to deep neural networks.
Optical Flow Estimation
Video-Based Person Re-Identification
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.
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.
Differentially Private Algorithms for Statistical Verification of Cyber-Physical Systems
We also show that the standard exponential mechanism that randomizes the output of an algorithm to achieve differential privacy fails to do so in the context of sequential algorithms.
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.
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.
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.
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 #6 on
Natural Language Inference
on ANLI test
(using extra training data)
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.
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.
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.
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.
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.
A Complex KBQA System using Multiple Reasoning Paths
Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding.
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.
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 $1\%$.
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.
Learning Monotone Dynamics by Neural Networks
For monotonicity constraints, we propose to use nonnegative neural networks and batch normalization.
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.
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.
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.
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.
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
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.
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 CASP12
Dimensionality Reduction
Protein Secondary Structure Prediction
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.
Ranked #7 on
Long-tail Learning
on VOC-MLT
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.
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.
Evaluating Efficient Performance Estimators of Neural Architectures
Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS).
Shift Equivariance in Object Detection
Robustness to small image translations is a highly desirable property for object detectors.
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.
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 #25 on
Fine-Grained Image Classification
on Stanford Cars
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.
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.
A Surgery of the Neural Architecture Evaluators
A major challenge in NAS is to conduct a fast and accurate evaluation of neural architectures.
Joint Contrastive Learning with Infinite Possibilities
This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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 enables the use of model-free RL algorithms to learn an optimal controller strategy.
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 Models of Code
Attribution methods have emerged as a popular approach to interpreting model predictions based on the relevance of input features.
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
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
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
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.
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).
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
+2
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
Instance Segmentation
on nuScenes
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.
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.
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.
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web
Then we learn entity embeddings through this new type of triples.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Membership Inference Attacks on Knowledge Graphs
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 #25 on
Node Classification
on Cora
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.
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.
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.
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.
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.
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.
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.
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.
SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae
Spinal degeneration plagues many elders, office workers, and even the younger generations.
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.
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).
Model Predictive Control
Multi-agent Reinforcement Learning
+2
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.
Imbalanced Graph Classification via Graph-of-Graph Neural Networks
To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G$^2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from stochastic augmentations of graphs.
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).
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.
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 #37 on
Domain Generalization
on PACS
Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration
Our work reveals a structured shortcoming of the existing mainstream self-supervised learning methods.
LAR-SR: A Local Autoregressive Model for Image Super-Resolution
Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output.
Improving Out-of-Distribution Robustness via Selective Augmentation
Machine learning algorithms typically assume that training and test examples are drawn from the same distribution.
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.
Evaluating the impact of quarantine measures on COVID-19 spread
We generate counterfactual simulations to estimate effectiveness of quarantine measures.
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.
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.
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE.
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.
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 #34 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
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.
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.
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.
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.
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.
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.
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.
Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies
The current work on reinforcement learning (RL) from demonstrations often assumes the demonstrations are samples from an optimal policy, an unrealistic assumption in practice.
Differentiable Invariant Causal Discovery
This paper proposes Differentiable Invariant Causal Discovery (DICD), utilizing the multi-environment information based on a differentiable framework to avoid learning spurious edges and wrong causal directions.
Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage
Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation.
Spatial-temporal Concept based Explanation of 3D ConvNets
Recent studies have achieved outstanding success in explaining 2D image recognition ConvNets.
LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network
This technical report presents the 1st place winning solution for the Waymo Open Dataset 3D semantic segmentation challenge 2022.
On Structural Explanation of Bias in Graph Neural Networks
The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios.
Multi-Granularity Regularized Re-Balancing for Class Incremental Learning
Re-balancing methods are used to alleviate the influence of data imbalance; however, we empirically discover that they would under-fit new classes.
Collaboration-Aware Graph Convolutional Network for Recommender Systems
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect.
Class-Specific Semantic Reconstruction for Open Set Recognition
In this study, we propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of AE and prototype learning.
Dual Vision Transformer
Dual-ViT is henceforth able to reduce the computational complexity without compromising much accuracy.
Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition
The research and applications of multimodal emotion recognition have become increasingly popular recently.
Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning
Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL).
CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS
Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum).
BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition
However, due to the inconsistent frequency of human activities, the amount of data for each activity in the human activity dataset is imbalanced.
Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios
Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy.
Implementation of improved RGBD 3D target detection model based on FPGA heterogeneous computing architecture
In order to solve the problems of low model accuracy, poor computing power, poor parallel ability and excessive power consumption in the deployment of RGBD based 3 D target detection model at the embedded end, this paper first proposes an improved RGBD 3 D target detection model based on ENet semantic segmentation model, which takes ENet as the semantic segmentation network, RGB image and depth information are fused to realize 3 D target detection. Secondly, in order to apply the model at the edge, this paper constructs a lightweight network and cuts the network in the down-sampling stage of ENet model. Finally, this paper uses Xilinx ZCU104 as the hardware development kit, which takes FPGA as the auxiliary parallel operation unit and ARM as the main operation unit. It is a heterogeneous computing architecture with the ability to deal with complex operations. The architecture uses FPGA to accelerate the depth model in parallel, which improves the operation speed and reduces the power consumption. The test results of the model on ZCU104 are compared with other hardware. The results show that while ensuring the accuracy, the power consumption of the heterogeneous computing architecture used in this paper is 93% lowerthan that of Intel Xeon e5-2620 v4 CPU, the speed is 12 times higher, and the speed is more than 180 times higher than that of ARM Cortex-A53 commonly used at the edge.
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning
Multi-view learning has progressed rapidly in recent years.
CenterFormer: Center-based Transformer for 3D Object Detection
It then uses the feature of the center candidate as the query embedding in the transformer.
Ranked #2 on
3D Object Detection
on waymo cyclist
One-shot Network Pruning at Initialization with Discriminative Image Patches
Recently, there is a growing belief that data is unnecessary in OPaI.
Infrared: A Meta Bug Detector
The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors.
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception
LidarMultiNet is extensively tested on both Waymo Open Dataset and nuScenes dataset, demonstrating for the first time that major LiDAR perception tasks can be unified in a single strong network that is trained end-to-end and achieves state-of-the-art performance.
