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Found 522 papers, 161 papers with code
Worst-Case Linear Discriminant Analysis
In this paper, we first analyze the scatter measures used in the conventional linear discriminant analysis~(LDA) model and note that the formulation is based on the average-case view.
Probabilistic Multi-Task Feature Selection
In this paper, we unify the $l_{1, 2}$ and $l_{1,\infty}$ norms by considering a family of $l_{1, q}$ norms for $1 < q\le\infty$ and study the problem of determining the most appropriate sparsity enforcing norm to use in the context of multi-task feature selection.
A Convex Formulation for Learning Task Relationships in Multi-Task Learning
In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning.
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction
Very often data we encounter in practice is a collection of matrices rather than a single matrix.
Ensemble of Distributed Learners for Online Classification of Dynamic Data Streams
We present an efficient distributed online learning scheme to classify data captured from distributed, heterogeneous, and dynamic data sources.
Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis
Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs).
Electricity Market Forecasting via Low-Rank Multi-Kernel Learning
The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure.
Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms
In this paper, different from existing methods, we propose local learning methods for multi-task classification and regression problems based on heterogeneous neighborhood which is defined on data points from all tasks.
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data
Crowdsourcing platforms offer a practical solution to the problem of affordably annotating large datasets for training supervised classifiers.
A Formal Analysis of Required Cooperation in Multi-agent Planning
Then, by dividing the problems that require cooperation (referred to as RC problems) into two classes -- problems with heterogeneous and homogeneous agents, we aim to identify all the conditions that can cause RC in these two classes.
Towards Good Practices for Action Video Encoding
High dimensional representations such as VLAD or FV have shown excellent accuracy in action recognition.
Compact Representation for Image Classification: To Choose or to Compress?
In spite of the popularity of various feature compression methods, this paper argues that feature selection is a better choice than feature compression.
Learning of Agent Capability Models with Applications in Multi-agent Planning
Thus far, there are two common representations of agent models: MDP based and action based, which are both based on action modeling.
Plan or not: Remote Human-robot Teaming with Incomplete Task Information
While information asymmetry can be desirable sometimes, it may also lead to the robot choosing improper actions that negatively influence the teaming performance.
Weakly Supervised Fine-Grained Image Categorization
Most existing works heavily rely on object / part detectors to build the correspondence between object parts by using object or object part annotations inside training images.
Exploit Bounding Box Annotations for Multi-label Object Recognition
With strong labels, our framework is able to achieve state-of-the-art results in both datasets.
Ranked #16 on
Multi-Label Classification
on PASCAL VOC 2007
Semantic Object Segmentation via Detection in Weakly Labeled Video
Semantic object segmentation in video is an important step for large-scale multimedia analysis.
3D Reconstruction in the Presence of Glasses by Acoustic and Stereo Fusion
We present a novel sensor fusion algorithm that first segments the depth map into different categories such as opaque/transparent/infinity (e. g., too far to measure) and then updates the depth map based on the segmentation outcome.
Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data
With the increasing availability of various sensor technologies, we now have access to large amounts of multi-block (also called multi-set, multi-relational, or multi-view) data that need to be jointly analyzed to explore their latent connections.
Prediction-Adaptation-Correction Recurrent Neural Networks for Low-Resource Language Speech Recognition
In this paper, we investigate the use of prediction-adaptation-correction recurrent neural networks (PAC-RNNs) for low-resource speech recognition.
Highway Long Short-Term Memory RNNs for Distant Speech Recognition
In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers.
Plan Explicability and Predictability for Robot Task Planning
Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans.
On Training Bi-directional Neural Network Language Model with Noise Contrastive Estimation
We propose to train bi-directional neural network language model(NNLM) with noise contrastive estimation(NCE).
Storm Detection by Visual Learning Using Satellite Images
In our system, we extract and summarize important visual storm evidence from satellite image sequences in the way that meteorologists interpret the images.
Recurrent Neural Network Encoder with Attention for Community Question Answering
We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks.
A Deep Neural Network for Chinese Zero Pronoun Resolution
This is because zero pronouns have no descriptive information, which results in difficulty in explicitly capturing their semantic similarities with antecedents.
Neural Recovery Machine for Chinese Dropped Pronoun
Dropped pronouns (DPs) are ubiquitous in pro-drop languages like Chinese, Japanese etc.
Proactive Decision Support using Automated Planning
Proactive decision support (PDS) helps in improving the decision making experience of human decision makers in human-in-the-loop planning environments.
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups
Pose estimation, tracking, and action recognition of articulated objects from depth images are important and challenging problems, which are normally considered separately.
Personalizing a Dialogue System with Transfer Reinforcement Learning
One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain.
Very Deep Convolutional Networks for End-to-End Speech Recognition
Sequence-to-sequence models have shown success in end-to-end speech recognition.
Explicablility as Minimizing Distance from Expected Behavior
In order to have effective human-AI collaboration, it is necessary to address how the AI agent's behavior is being perceived by the humans-in-the-loop.
Neural Attention for Learning to Rank Questions in Community Question Answering
In real-world data, e. g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms.
Learning to Search on Manifolds for 3D Pose Estimation of Articulated Objects
This paper focuses on the challenging problem of 3D pose estimation of a diverse spectrum of articulated objects from single depth images.
Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator
We introduce the concept of a Visual Compiler that generates a scene specific pedestrian detector and pose estimator without any pedestrian observations.
Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications
The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
When AI systems interact with humans in the loop, they are often called on to provide explanations for their plans and behavior.
Sequence-based Multimodal Apprenticeship Learning For Robot Perception and Decision Making
Apprenticeship learning has recently attracted a wide attention due to its capability of allowing robots to learn physical tasks directly from demonstrations provided by human experts.
Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors
We formulate FABL as a regression-like optimization problem with structured sparsity-inducing norms to model interrelationships of body parts and features.
Learning Latent Representations for Speech Generation and Transformation
In this paper, we apply a convolutional VAE to model the generative process of natural speech.
Causes and Corrections for Bimodal Multipath Scanning with Structured Light
Structured light illumination is an active 3-D scanning technique based on projecting/capturing a set of striped patterns and measuring the warping of the patterns as they reflect off a target object's surface.
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Structured Light Phase Measuring Profilometry Pattern Design for Binary Spatial Light Modulators
In the case of phase measuring profilometry (PMP), the projected patterns are composed of a rolling sinusoidal wave, but as a set of time-multiplexed patterns, PMP requires the target surface to remain motionless or for scanning to be performed at such high rates that any movement is small.
What Is and What Is Not a Salient Object? Learning Salient Object Detector by Ensembling Linear Exemplar Regressors
Finding what is and what is not a salient object can be helpful in developing better features and models in salient object detection (SOD).
AI Challenges in Human-Robot Cognitive Teaming
Among the many anticipated roles for robots in the future is that of being a human teammate.
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation
Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks.
SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity
We describe a method of calculating the similarity of questions in community QA.
Learning to Transfer
We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function.
Chinese Zero Pronoun Resolution with Deep Memory Network
Existing approaches for Chinese zero pronoun resolution typically utilize only syntactical and lexical features while ignoring semantic information.
Simple Recurrent Units for Highly Parallelizable Recurrence
Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations.
Ranked #32 on
Question Answering
on SQuAD1.1 dev
Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-of-vocabulary entities.
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Supervision by Fusion: Towards Unsupervised Learning of Deep Salient Object Detector
Based on this insight, we combine an intra-image fusion stream and a inter-image fusion stream in the proposed framework to generate the learning curriculum and pseudo ground-truth for supervising the training of the deep salient object detector.
Learning Graphical Models from a Distributed Stream
We study Bayesian networks, the workhorse of graphical models, and present a communication-efficient method for continuously learning and maintaining a Bayesian network model over data that is arriving as a distributed stream partitioned across multiple processors.
Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning
We propose a novel co-training framework with a distributional module and a pattern module.
Integrating User and Agent Models: A Deep Task-Oriented Dialogue System
Then the built user model is used as a leverage to train the agent model by deep reinforcement learning.
Image Matters: Visually modeling user behaviors using Advanced Model Server
So we propose to model user preference jointly with user behavior ID features and behavior images.
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text.
Ranked #2 on
Speech Synthesis
on North American English
Training RNNs as Fast as CNNs
Common recurrent neural network architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations.
Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning
Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases.
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.
LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text
By modeling content information as local memories, LCMR attentively learns what to exploit with the guidance of user-item interaction.
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa.
Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment
Also, they depend on either common slots or slot entropy, which are not available when the source and target slots are totally disjoint and no database is available to calculate the slot entropy.
Expert Finding in Community Question Answering: A Review
Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boosting the performance.
Parameter Transfer Unit for Deep Neural Networks
Unfortunately, the transferability is usually defined as discrete states and it differs with domains and network architectures.
Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification
Existing cross-domain sentiment classification meth- ods cannot automatically capture non-pivots, i. e., the domain- specific sentiment words, and pivots, i. e., the domain-shared sentiment words, simultaneously.
Integrating Local Context and Global Cohesiveness for Open Information Extraction
However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions.
Variable-fidelity expected improvement method for efficient global optimization of expensive functions
The efficient global optimization method (EGO) based on kriging surrogate model and expected improvement (EI) has received much attention for optimization of high-fidelity, expensive functions.
Image Co-segmentation via Multi-scale Local Shape Transfer
Image co-segmentation is a challenging task in computer vision that aims to segment all pixels of the objects from a predefined semantic category.
Learning to Multitask
Based on such training set, L2MT first uses a proposed layerwise graph neural network to learn task embeddings for all the tasks in a multitask problem and then learns an estimation function to estimate the relative test error based on task embeddings and the representation of the multitask model based on a unified formulation.
Deep Reinforcement Learning for Chinese Zero pronoun Resolution
In this study, we show how to integrate local and global decision-making by exploiting deep reinforcement learning models.
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Clone a voice in 5 seconds to generate arbitrary speech in real-time
Transfer Learning via Learning to Transfer
In transfer learning, what and how to transfer are two primary issues to be addressed, as different transfer learning algorithms applied between a source and a target domain result in different knowledge transferred and thereby the performance improvement in the target domain.
Back-Translation-Style Data Augmentation for End-to-End ASR
In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Zero Pronoun Resolution with Attention-based Neural Network
Recent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents.
Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis
We demonstrate that the proposed framework enables Tacotron to generate intelligible speech using less than half an hour of paired training data.
Human activity recognition based on time series analysis using U-Net
Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method.
Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
Embeddings can be learned from both the structural and attribute information of entities, and the results of structure embedding and attribute embedding are combined to get accurate alignments.
Ranked #5 on
Entity Alignment
on YAGO-WIKI50K
Top-K Influential Nodes in Social Networks: A Game Perspective
In this paper, we study influence maximization from a game perspective.
Hierarchical Generative Modeling for Controllable Speech Synthesis
This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training data, such as speaking style, accent, background noise, and recording conditions.
Cycle-consistency training for end-to-end speech recognition
To solve this problem, this work presents a loss that is based on the speech encoder state sequence instead of the raw speech signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Modeling and Predicting Popularity Dynamics via Deep Learning Attention Mechanism
Here we propose a deep learning attention mechanism to model the process through which individual items gain their popularity.
Modeling and Predicting Citation Count via Recurrent Neural Network with Long Short-Term Memory
The rapid evolution of scientific research has been creating a huge volume of publications every year.
Digital Libraries Physics and Society
RGB-D SLAM in Dynamic Environments Using Point Correlations
This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups.
Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction
After that, the visual saliency knowledge encoded in the most representative paths is selected and aggregated to improve the capability of MM-Net in predicting spatial saliency in aerial scenarios.
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification
Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT).
Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes
We present two end-to-end models: Audio-to-Byte (A2B) and Byte-to-Audio (B2A), for multilingual speech recognition and synthesis.
Complementary Segmentation of Primary Video Objects with Reversible Flows
Segmenting primary objects in a video is an important yet challenging problem in computer vision, as it exhibits various levels of foreground/background ambiguities.
FLEET: Butterfly Estimation from a Bipartite Graph Stream
We consider space-efficient single-pass estimation of the number of butterflies, a fundamental bipartite graph motif, from a massive bipartite graph stream where each edge represents a connection between entities in two different partitions.
Data Structures and Algorithms
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks
This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks.
Selectivity or Invariance: Boundary-aware Salient Object Detection
In this network, the feature selectivity at boundaries is enhanced by incorporating a boundary localization stream, while the feature invariance at interiors is guaranteed with a complex interior perception stream.
The height problem in first passage percolation
We consider the first passage percolation model in Z2 with a distribution F for 0 < F (0) < pc.
Probability
Interactive Plan Explicability in Human-Robot Teaming
Being aware of the human teammates' expectation leads to robot behaviors that better align with human expectation, thus facilitating more efficient and potentially safer teams.
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods.
Progressive Explanation Generation for Human-robot Teaming
A progressive explanation improves understanding by limiting the cognitive effort required at each step of making the explanation.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Integrating neural networks into the blind deblurring framework to compete with the end-to-end learning-based methods
Recently, end-to-end learning-based methods based on deep neural network (DNN) have been proven effective for blind deblurring.
HLT@SUDA at SemEval 2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery.
Online Explanation Generation for Human-Robot Teaming
The new explanation generation methods are based on a model reconciliation setting introduced in our prior work.
LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
This paper introduces a new speech corpus called "LibriTTS" designed for text-to-speech use.
Sound Audio and Speech Processing
Image Quality Assessment for Omnidirectional Cross-reference Stitching
Along with the development of virtual reality (VR), omnidirectional images play an important role in producing multimedia content with immersive experience.
Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study
For making the classifications reasonable, the visualization of CNN representations is furthermore used to identify cells between melanoma and nevi.
A Light Dual-Task Neural Network for Haze Removal
In LDTNet, the haze-free image and the transmission map are produced simultaneously.
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.
Ranked #1 on
Speech Recognition
on Hub5'00 SwitchBoard
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots
In this paper, we consider a first step to bridge a gap in coordinating task planning robots.
A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices.
HLT@SUDA at SemEval-2019 Task 1: UCCA Graph Parsing as Constituent Tree Parsing
The key idea is to convert a UCCA semantic graph into a constituent tree, in which extra labels are deliberately designed to mark remote edges and discontinuous nodes for future recovery.
Ranked #1 on
UCCA Parsing
on SemEval 2019 Task 1
Causes and Corrections for Bimodal Multi-Path Scanning With Structured Light
Structured light illumination is an active 3D scanning technique based on projecting/capturing a set of striped patterns and measuring the warping of the patterns as they reflect off a target object's surface.
Structure-Preserving Stereoscopic View Synthesis With Multi-Scale Adversarial Correlation Matching
This paper addresses stereoscopic view synthesis from a single image.
Evidence for $Z_{c}^{\pm}$ decays into the $ρ^{\pm} η_{c}$ final state
We study $e^{+}e^{-}$ collisions with a $\pi^{+}\pi^{-}\pi^{0}\eta_{c}$ final state using data samples collected with the BESIII detector at center-of-mass energies $\sqrt{s}=4. 226$, $4. 258$, $4. 358$, $4. 416$, and $4. 600$ GeV.
High Energy Physics - Experiment
Federated Hierarchical Hybrid Networks for Clickbait Detection
Online media outlets adopt clickbait techniques to lure readers to click on articles in a bid to expand their reach and subsequently increase revenue through ad monetization.
Brain Network Construction and Classification Toolbox (BrainNetClass)
The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods.
Expected Sarsa($λ$) with Control Variate for Variance Reduction
In this paper, for reducing the variance, we introduce control variate technique to $\mathtt{Expected}$ $\mathtt{Sarsa}$($\lambda$) and propose a tabular $\mathtt{ES}$($\lambda$)-$\mathtt{CV}$ algorithm.
Policy Optimization with Stochastic Mirror Descent
Improving sample efficiency has been a longstanding goal in reinforcement learning.
Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning
We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages.
Discriminative Topic Mining via Category-Name Guided Text Embedding
We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.
Multi-Spectral Visual Odometry without Explicit Stereo Matching
Moreover, the proposed method can also provide a metric 3D reconstruction in semi-dense density with multi-spectral information, which is not available from existing multi-spectral methods.
Heterogeneous Domain Adaptation via Soft Transfer Network
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain.
Gradient Q$(σ, λ)$: A Unified Algorithm with Function Approximation for Reinforcement Learning
To address above problem, we propose a GQ$(\sigma,\lambda)$ that extends tabular Q$(\sigma,\lambda)$ with linear function approximation.
Functional Annotation of Human Cognitive States using Graph Convolution Networks
In this project, we applied graph convolutional networks (GCN) to decode brain activity over short time windows in a task fMRI dataset, i. e. associate a given window of fMRI time series with the task used.
Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.
Speech Recognition with Augmented Synthesized Speech
Recent success of the Tacotron speech synthesis architecture and its variants in producing natural sounding multi-speaker synthesized speech has raised the exciting possibility of replacing expensive, manually transcribed, domain-specific, human speech that is used to train speech recognizers.
Knowledge Distillation from Internal Representations
In this paper, we propose to distill the internal representations of a large model such as BERT into a simplified version of it.
End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds
In this paper, we aim to synergize the birds-eye view and the perspective view and propose a novel end-to-end multi-view fusion (MVF) algorithm, which can effectively learn to utilize the complementary information from both.
Deep Learning for Massive MIMO with 1-Bit ADCs: When More Antennas Need Fewer Pilots
This leads to the interesting, and \textit{counter-intuitive}, observation that when more antennas are employed by the massive MIMO base station, our proposed deep learning approach achieves better channel estimation performance, for the same pilot sequence length.
Information Theory Signal Processing Information Theory
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
With the massive number of repositories available, there is a pressing need for topic-based search.
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Solving Optimization Problems through Fully Convolutional Networks: an Application to the Travelling Salesman Problem
Based on samples of a 10 city TSP, a fully convolutional network (FCN) is used to learn the mapping from a feasible region to an optimal solution.
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
A comparison of end-to-end models for long-form speech recognition
In this paper, we both investigate and improve the performance of end-to-end models on long-form transcription.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring
Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters.
FeCaffe: FPGA-enabled Caffe with OpenCL for Deep Learning Training and Inference on Intel Stratix 10
In this paper, we firstly propose the FeCaffe, i. e. FPGA-enabled Caffe, a hierarchical software and hardware design methodology based on the Caffe to enable FPGA to support mainline deep learning development features, e. g. training and inference with Caffe.
Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models
In this paper, we propose to use pre-trained features from end-to-end ASR models to solve speech sentiment analysis as a down-stream task.
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
SpecAugment on Large Scale Datasets
Recently, SpecAugment, an augmentation scheme for automatic speech recognition that acts directly on the spectrogram of input utterances, has shown to be highly effective in enhancing the performance of end-to-end networks on public datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis
This paper proposes a hierarchical, fine-grained and interpretable latent variable model for prosody based on the Tacotron 2 text-to-speech model.
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior
Recent neural text-to-speech (TTS) models with fine-grained latent features enable precise control of the prosody of synthesized speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing
Towards pushing deep learning on devices, we present MDLdroid, a novel decentralized mobile deep learning framework to enable resource-aware on-device collaborative learning for personal mobile sensing applications.
Deep Multi-Task Augmented Feature Learning via Hierarchical Graph Neural Network
The proposed feature augmentation strategy can be used in many deep multi-task learning models.
Deep Multi-Task Learning via Generalized Tensor Trace Norm
The GTTN is defined as a convex combination of matrix trace norms of all possible tensor flattenings and hence it can discover all the possible low-rank structures.
A Simple General Approach to Balance Task Difficulty in Multi-Task Learning
In multi-task learning, difficulty levels of different tasks are varying.
High-Order Paired-ASPP Networks for Semantic Segmenation
The network first introduces a High-Order Representation module to extract the contextual high-order information from all stages of the backbone.
CODAR: A Contextual Duration-Aware Qubit Mapping for Various NISQ Devices
Quantum computing devices in the NISQ era share common features and challenges like limited connectivity between qubits.
Quantum Physics
Learning Beam Codebooks with Neural Networks: Towards Environment-Aware mmWave MIMO
This leads to high beam training overhead and loss in the achievable beamforming gains.
Information Theory Signal Processing Information Theory
Self-supervised Image Enhancement Network: Training with Low Light Images Only
We introduce a constraint that the maximum channel of the reflectance conforms to the maximum channel of the low light image and its entropy should be largest in our model to achieve self-supervised learning.
Defense-PointNet: Protecting PointNet Against Adversarial Attacks
Despite remarkable performance across a broad range of tasks, neural networks have been shown to be vulnerable to adversarial attacks.
Joint 2D-3D Breast Cancer Classification
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females.
2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice.
Attention-guided Chained Context Aggregation for Semantic Segmentation
The way features propagate in Fully Convolutional Networks is of momentous importance to capture multi-scale contexts for obtaining precise segmentation masks.
Ranked #23 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
Unsupervised Domain Adaptation for Mammogram Image Classification: A Promising Tool for Model Generalization
Generalization is one of the key challenges in the clinical validation and application of deep learning models to medical images.
Is POS Tagging Necessary or Even Helpful for Neural Dependency Parsing?
In the pre deep learning era, part-of-speech tags have been considered as indispensable ingredients for feature engineering in dependency parsing.
Fisher Deep Domain Adaptation
Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance.
CF2-Net: Coarse-to-Fine Fusion Convolutional Network for Breast Ultrasound Image Segmentation
Breast ultrasound (BUS) image segmentation plays a crucial role in a computer-aided diagnosis system, which is regarded as a useful tool to help increase the accuracy of breast cancer diagnosis.
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.
Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark
Since there has already been a broad body of HNE algorithms, as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy.
Learning Event-Based Motion Deblurring
Recovering sharp video sequence from a motion-blurred image is highly ill-posed due to the significant loss of motion information in the blurring process.
Ranked #28 on
Image Deblurring
on GoPro
(using extra training data)
Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming
Given this sequential process, a formulation based on goal-based MDP for generating progressive explanations is presented.
Learning an Adaptive Model for Extreme Low-light Raw Image Processing
Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene.
Partially-Typed NER Datasets Integration: Connecting Practice to Theory
While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them.
A Large Scale Speech Sentiment Corpus
We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium.
Minimally Supervised Categorization of Text with Metadata
Then, based on the same generative process, we synthesize training samples to address the bottleneck of label scarcity.
Efficient Second-Order TreeCRF for Neural Dependency Parsing
Experiments and analysis on 27 datasets from 13 languages clearly show that techniques developed before the DL era, such as structural learning (global TreeCRF loss) and high-order modeling are still useful, and can further boost parsing performance over the state-of-the-art biaffine parser, especially for partially annotated training data.
Ranked #1 on
Dependency Parsing
on CoNLL-2009
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
We demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate (WER) of 2. 1%/4. 6% without external language model (LM), 1. 9%/4. 1% with LM and 2. 9%/7. 0% with only 10M parameters on the clean/noisy LibriSpeech test sets.
Ranked #12 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
On a long-form YouTube test set, when the nonstreaming RNN-T model is trained with shorter segments of data, the proposed combination improves word error rate (WER) from 22. 3% to 14. 8%; when the streaming RNN-T model trained on short Search queries, the proposed techniques improve WER on the YouTube set from 67. 0% to 25. 3%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Conformer: Convolution-augmented Transformer for Speech Recognition
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs).
Ranked #12 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improved Noisy Student Training for Automatic Speech Recognition
Noisy student training is an iterative self-training method that leverages augmentation to improve network performance.
Ranked #5 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Attention: to Better Stand on the Shoulders of Giants
In the progress of science, the previously discovered knowledge principally inspires new scientific ideas, and citation is a reasonably good reflection of this cumulative nature of scientific research.
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
Then, we use a modality-specific network to extract implicit and high-level features from different MR scans.
Distant Transfer Learning via Deep Random Walk
Transfer learning, which is to improve the learning performance in the target domain by leveraging useful knowledge from the source domain, often requires that those two domains are very close, which limits its application scope.
Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images
Diagnosis from chest CT images is a promising direction.
Neural Networks Based Beam Codebooks: Learning mmWave Massive MIMO Beams that Adapt to Deployment and Hardware
To overcome these limitations, this paper develops an efficient online machine learning framework that learns how to adapt the codebook beam patterns to the specific deployment, surrounding environment, user distribution, and hardware characteristics.
Offline Handwritten Chinese Text Recognition with Convolutional Neural Networks
Deep learning based methods have been dominating the text recognition tasks in different and multilingual scenarios.
A Multi-spectral Dataset for Evaluating Motion Estimation Systems
The multi-spectral images, including both color and thermal images in full sensor resolution (640 x 480), are obtained from a standard and a long-wave infrared camera at 32Hz with hardware-synchronization.
Allocation of Multi-Robot Tasks with Task Variants
We referred to this new problem as the multi-robot task allocation problem with task variants.
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since topic correlations are ubiquitous in massive text corpora.
Ranked #1 on
Topic Models
on Arxiv HEP-TH citation graph
Deep Image Clustering with Category-Style Representation
In this paper, we propose a novel deep image clustering framework to learn a category-style latent representation in which the category information is disentangled from image style and can be directly used as the cluster assignment.
A Study on Evaluation Standard for Automatic Crack Detection Regard the Random Fractal
In CovEval, a different matching process based on the idea of covering box matching is adopted for this issue.
COMET: Convolutional Dimension Interaction for Collaborative Filtering
In this paper, we propose a novel representation learning-based model called COMET (COnvolutional diMEnsion inTeraction), which simultaneously models the high-order interaction patterns among historical interactions and embedding dimensions.
Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network
In reality, however, it is not uncommon to obtain samples from multiple heterogeneous domains.
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
Our study is based on UC Toutiao (a news feed service integrated with the UC Browser App, serving hundreds of millions of users daily), where the source domain is the news and the target domain is the ad.
Fast and Accurate Neural CRF Constituency Parsing
Estimating probability distribution is one of the core issues in the NLP field.
Ranked #1 on
Constituency Parsing
on CTB7
Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks
The network takes low light images as input and the enhanced V channel as condition, then it can re-enhance the contrast and brightness of the low light image and at the same time reduce noise and color distortion.
An End-to-End Attack on Text-based CAPTCHAs Based on Cycle-Consistent Generative Adversarial Network
Firstly, we train CAPTCHA synthesizers based on the cycle-GAN to generate some fake samples.
WaveGrad: Estimating Gradients for Waveform Generation
This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density.
Oceanic non-Kolmogorov optical turbulence and spherical wave propagation
In particular, for natural water turbulence several models for the spatial power spectra have been developed based on the classic, Kolmogorov postulates.
Atmospheric and Oceanic Physics Optics
Boosting Retailer Revenue by Generated Optimized Combined Multiple Digital Marketing Campaigns
Secondly, based on the sub-modular optimization theory and the DMC pool by DMCNet, the generated combined multiple DMCs are ranked with respect to their revenue generation strength then the top three ranked campaigns are returned to the sellers' back-end management system, so that retailers can set combined multiple DMCs for their online shops just in one-shot.
Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification
Recent works have shown that deep neural networks can achieve super-human performance in a wide range of image classification tasks in the medical imaging domain.
Cross-Modal Alignment with Mixture Experts Neural Network for Intral-City Retail Recommendation
At top of the MoE layer, we deploy a transformer layer for each task as task tower to learn task-specific information.
Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging
A key challenge in training neural networks for a given medical imaging task is often the difficulty of obtaining a sufficient number of manually labeled examples.
