Search Results for author:
Found 144 papers, 62 papers with code
Target Really Matters: Target-aware Contrastive Learning and Consistency Regularization for Few-shot Stance Detection
Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient high-quality labeled data for every new target under fully-supervised learning, whereas unlabeled data can be collected easier.
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis
Then we propose a differential sentiment loss instead of the cross-entropy loss to better classify the sentiments by distinguishing the different distances between sentiments.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction
We introduce Ming-Lite-Uni, an open-source multimodal framework featuring a newly designed unified visual generator and a native multimodal autoregressive model tailored for unifying vision and language.
AdaR1: From Long-CoT to Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization
To address this, we propose a novel two-stage framework for adaptive and efficient reasoning.
A comprehensive review of remote sensing in wetland classification and mapping
Although some review articles summarized the development of this field, there is a lack of a thorough and in-depth understanding of wetland classification and mapping: (1) the scientific importance of wetlands, (2) major data, methods used in wetland classification and mapping, (3) driving factors of wetland changes, (4) current research paradigm and limitations, (5) challenges and opportunities in wetland classification and mapping under the context of technological innovation and global environmental change.
EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding
Ablation studies validate the contributions of the hierarchical GCN modules and HTSR Loss, while saliency maps reveal the significance of motor-related brain regions in decoding tasks.
A Theoretical Analysis of Analogy-Based Evolutionary Transfer Optimization
Evolutionary transfer optimization (ETO) has been gaining popularity in research over the years due to its outstanding knowledge transfer ability to address various challenges in optimization.
Robust Safety Critical Control Under Multiple State and Input Constraints: Volume Control Barrier Function Method
To further address the challenges arising from multiple CBF and input constraints, a novel Volume CBF (VCBF) is proposed by analyzing the feasible space of the quadratic programming (QP) problem.
MSCMHMST: A traffic flow prediction model based on Transformer
This study proposes a hybrid model based on Transformers, named MSCMHMST, aimed at addressing key challenges in traffic flow prediction.
MegaTTS 3: Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis
While recent zero-shot text-to-speech (TTS) models have significantly improved speech quality and expressiveness, mainstream systems still suffer from issues related to speech-text alignment modeling: 1) models without explicit speech-text alignment modeling exhibit less robustness, especially for hard sentences in practical applications; 2) predefined alignment-based models suffer from naturalness constraints of forced alignments.
CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems
Multi-modality learning has become a crucial technique for improving the performance of machine learning applications across domains such as autonomous driving, robotics, and perception systems.
Enhancing Image Matting in Real-World Scenes with Mask-Guided Iterative Refinement
Real-world image matting is essential for applications in content creation and augmented reality.
AUKT: Adaptive Uncertainty-Guided Knowledge Transfer with Conformal Prediction
Knowledge transfer between teacher and student models has proven effective across various machine learning applications.
Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation
Mainstream defenses aim to vaccinate the model such that the later harmful fine-tuning attack is less effective.
Return of the Encoder: Maximizing Parameter Efficiency for SLMs
The dominance of large decoder-only language models has overshadowed encoder-decoder architectures, despite their fundamental efficiency advantages in sequence processing.
Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA
Discovering dominant patterns and exploring dynamic behaviors especially critical state transitions and tipping points in high-dimensional time-series data are challenging tasks in study of real-world complex systems, which demand interpretable data representations to facilitate comprehension of both spatial and temporal information within the original data space.
Retrieval-Augmented Dialogue Knowledge Aggregation for Expressive Conversational Speech Synthesis
Then, we design a multi-attribute retrieval scheme to match the dialogue semantic and style vectors of the CD with the stored dialogue semantic and style vectors in the SDSSD, retrieving the most similar dialogues.
Listening and Seeing Again: Generative Error Correction for Audio-Visual Speech Recognition
Unlike traditional Automatic Speech Recognition (ASR), Audio-Visual Speech Recognition (AVSR) takes audio and visual signals simultaneously to infer the transcription.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+4
Towards Expressive Video Dubbing with Multiscale Multimodal Context Interaction
Recent research focuses on modeling multimodal context to enhance prosody expressiveness but overlooks two key issues: 1) Multiscale prosody expression attributes in the context influence the current sentence's prosody.
Intra- and Inter-modal Context Interaction Modeling for Conversational Speech Synthesis
In the inference phase, we take MDH and adopt trained interaction modules to fully infer the speech prosody of the target utterance's text content.
Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech
The multi-modal aims to take both the RGB and Depth spaces of the spatial image to learn more comprehensive spatial information, and the multi-scale seeks to model the local and global spatial knowledge simultaneously.
Analyst Reports and Stock Performance: Evidence from the Chinese Market
This article applies natural language processing (NLP) to extract and quantify textual information to predict stock performance.
Voltage Support Capability Analysis of Grid-Forming Inverters with Current-Limiting Control Under Asymmetrical Grid Faults
It is discovered that matching the phase angle of the virtual impedance, emulated by the CLC, with that of the composed impedance from the capacitor to the fault location can maximize the voltage support capability of GFM inverters under asymmetrical grid faults.
Flexible Coded Distributed Convolution Computing for Enhanced Fault Tolerance and Numerical Stability in Distributed CNNs
Deploying Convolutional Neural Networks (CNNs) on resource-constrained devices necessitates efficient management of computational resources, often via distributed systems susceptible to latency from straggler nodes.
FairDgcl: Fairness-aware Recommendation with Dynamic Graph Contrastive Learning
Specifically, we propose FairDgcl, a dynamic graph adversarial contrastive learning framework aiming at improving fairness in recommender system.
Vision-Language Navigation with Energy-Based Policy
Consequently, ENP learns to globally align with the expert policy by maximizing the likelihood of the actions and modeling the dynamics of the navigation states in a collaborative manner.
Multi-Source Spatial Knowledge Understanding for Immersive Visual Text-to-Speech
Previous works focus on the RGB modality for global environmental modeling, overlooking the potential of multi-source spatial knowledge like depth, speaker position, and environmental semantics.
Emphasis Rendering for Conversational Text-to-Speech with Multi-modal Multi-scale Context Modeling
In this paper, we propose a novel Emphasis Rendering scheme for the CTTS model, termed ER-CTTS, that includes two main components: 1) we simultaneously take into account textual and acoustic contexts, with both global and local semantic modeling to understand the conversation context comprehensively; 2) we deeply integrate multi-modal and multi-scale context to learn the influence of context on the emphasis expression of the current utterance.
FluentEditor2: Text-based Speech Editing by Modeling Multi-Scale Acoustic and Prosody Consistency
To maintain speech fluency, we propose a new fluency speech editing scheme based on our previous \textit{FluentEditor} model, termed \textit{\textbf{FluentEditor2}}, by modeling the multi-scale acoustic and prosody consistency training criterion in TSE training.
Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehicles
Trajectory prediction describes the motions of surrounding moving obstacles for an autonomous vehicle; it plays a crucial role in enabling timely decision-making, such as collision avoidance and trajectory replanning.
Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape
The prediction accuracy and adaptation speed results show the effectiveness of PLBA in preference learning and MRS behavior adaption.
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition
However, such representations are not robust and struggle to generalize across diverse acquisition scenarios.
Medical Report Generation Is A Multi-label Classification Problem
However, in this paper, we propose a novel perspective: rethinking medical report generation as a multi-label classification problem.
MCDubber: Multimodal Context-Aware Expressive Video Dubbing
Automatic Video Dubbing (AVD) aims to take the given script and generate speech that aligns with lip motion and prosody expressiveness.
Generative Expressive Conversational Speech Synthesis
After that, the expressive conversational speech is synthesized by the conversation-enriched VITS to deliver feedback to the user. Furthermore, we propose a large-scale Natural CSS Dataset called NCSSD, that includes both naturally recorded conversational speech in improvised styles and dialogues extracted from TV shows.
Navigation Instruction Generation with BEV Perception and Large Language Models
To address these challenges, we propose BEVInstructor, which incorporates Bird's Eye View (BEV) features into Multi-Modal Large Language Models (MLLMs) for instruction generation.
Exploiting Scale-Variant Attention for Segmenting Small Medical Objects
Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment.
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients
Compared to the solutions based on centralized data centers, updating large models in the Internet of Things (IoT) faces challenges in coordinating knowledge from distributed clients by using their private and heterogeneous data.
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various application scenarios.
Ranked #2 on
Speech Recognition
on AISHELL-1
(using extra training data)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking Dataset
Together with the release of the dataset, we also develop an Emotion and Intent Interaction (EI$^2$) network as a reference system by modeling the deep correlation between emotion and intent in the multimodal conversation.
Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge
Speech Self-Supervised Learning (SSL) has demonstrated considerable efficacy in various downstream tasks.
Reliable Object Tracking by Multimodal Hybrid Feature Extraction and Transformer-Based Fusion
In this study, we propose a novel multimodal hybrid tracker (MMHT) that utilizes frame-event-based data for reliable single object tracking.
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
However, this process may lead to inaccurate annotations, such as ignoring non-majority or non-candidate labels.
EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs).
Infrared Small Target Detection with Scale and Location Sensitivity
In this paper, we focus on boosting detection performance with a more effective loss but a simpler model structure.
Volumetric Environment Representation for Vision-Language Navigation
To achieve a comprehensive 3D representation with fine-grained details, we introduce a Volumetric Environment Representation (VER), which voxelizes the physical world into structured 3D cells.
Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types
Notably, our model, trained solely on data from a transparent glass bowl containing granular cereals, showcases generalization ability when tested zero-shot on other bowl configurations with different types of food.
Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
To validate our analysis, we empirically evaluate the learning performance and convergence efficiency of the risk-neutral and risk-sensitive REINFORCE algorithms in multiple environments: CartPole, MiniGrid, and Robot Navigation.
Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning
Deception detection has attracted increasing attention due to its importance in real-world scenarios.
Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives
Foundation models have indeed made a profound impact on various fields, emerging as pivotal components that significantly shape the capabilities of intelligent systems.
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
Large Model based Sequential Keyframe Extraction for Video Summarization
Keyframe extraction aims to sum up a video's semantics with the minimum number of its frames.
Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling
Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting.
Prompt Based Tri-Channel Graph Convolution Neural Network for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments.
Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios
This approach includes two pivotal components: firstly, a noise scheduler that adjusts the type and level of noise in the data to emulate various realistic incomplete situations.
Emotion-Aware Prosodic Phrasing for Expressive Text-to-Speech
Prosodic phrasing is crucial to the naturalness and intelligibility of end-to-end Text-to-Speech (TTS).
FluentEditor: Text-based Speech Editing by Considering Acoustic and Prosody Consistency
Text-based speech editing (TSE) techniques are designed to enable users to edit the output audio by modifying the input text transcript instead of the audio itself.
Explainable AI for tool wear prediction in turning
After the training process, the Shapley criterion was used to explain the predictions of the trained ML classifier.
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices.
Bird's-Eye-View Scene Graph for Vision-Language Navigation
Vision-language navigation (VLN), which entails an agent to navigate 3D environments following human instructions, has shown great advances.
Understanding the Application of Utility Theory in Robotics and Artificial Intelligence: A Survey
As a unifying concept in economics, game theory, and operations research, even in the Robotics and AI field, the utility is used to evaluate the level of individual needs, preferences, and interests.
Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction
To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.
Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
In this paper, we propose a novel ADD model, termed as M2S-ADD, that attempts to discover audio authenticity cues during the mono-to-stereo conversion process.
Distributed TD(0) with Almost No Communication
We provide a new non-asymptotic analysis of distributed temporal difference learning with linear function approximation.
PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning
Personalized news recommender systems help users quickly find content of their interests from the sea of information.
Neural Partial Differential Equations with Functional Convolution
We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs.
Towards Interpretable Federated Learning
Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data.
TegFormer: Topic-to-Essay Generation with Good Topic Coverage and High Text Coherence
Creating an essay based on a few given topics is a challenging NLP task.
MnTTS2: An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset
Text-to-Speech (TTS) synthesis for low-resource languages is an attractive research issue in academia and industry nowadays.
Quantifying syntax similarity with a polynomial representation of dependency trees
We introduce a graph polynomial that distinguishes tree structures to represent dependency grammar and a measure based on the polynomial representation to quantify syntax similarity.
Coverage-centric Coreset Selection for High Pruning Rates
We then propose a novel one-shot coreset selection method, Coverage-centric Coreset Selection (CCS), that jointly considers overall data coverage upon a distribution as well as the importance of each example.
Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities
Multimodal emotion recognition leverages complementary information across modalities to gain performance.
FCTalker: Fine and Coarse Grained Context Modeling for Expressive Conversational Speech Synthesis
Therefore, we propose a novel expressive conversational TTS model, termed as FCTalker, that learn the fine and coarse grained context dependency at the same time during speech generation.
Explicit Intensity Control for Accented Text-to-speech
Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1).
A Deep Investigation of RNN and Self-attention for the Cyrillic-Traditional Mongolian Bidirectional Conversion
To answer this question, this paper investigates the utility of these two powerful techniques for CTMBC task combined with agglutinative characteristics of Mongolian language.
A Spatial-channel-temporal-fused Attention for Spiking Neural Networks
Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing.
MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline
This paper introduces a high-quality open-source text-to-speech (TTS) synthesis dataset for Mongolian, a low-resource language spoken by over 10 million people worldwide.
Controllable Accented Text-to-Speech Synthesis
Accented TTS synthesis is challenging as L2 is different from L1 in both in terms of phonetic rendering and prosody pattern.
Communication-efficient Distributed Learning for Large Batch Optimization
In this paper, we propose new gradient compression methods for large batch optimization, JointSpar and its variant JointSpar-LARS with layerwise adaptive learning rates, that jointly reduce both the computation and the communication cost.
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System
This curriculum-driven noise introduction aids the memory-driven decoder by supplying more diverse and representative training data, enhancing the decoder's ability to model seasonal tendencies and dependencies in the time-series data.
Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning
In this paper, we propose a data-driven deep learning model, i. e. StrengthNet, to improve the generalization of emotion strength assessment for seen and unseen speech.
Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers
Sparsely activated transformers, such as Mixture of Experts (MoE), have received great interest due to their outrageous scaling capability which enables dramatical increases in model size without significant increases in computational cost.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Transformer with Memory Replay
Transformers achieve state-of-the-art performance for natural language processing tasks by pre-training on large-scale text corpora.
Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention
Visual features of skin lesions vary significantly because the images are collected from patients with different lesion colours and morphologies by using dissimilar imaging equipment.
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge
Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation.
Ranked #1 on
Causal Emotion Entailment
on RECCON
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoising
Furthermore, an overlapped inference mechanism is introduced to effectively eliminate the boundary artifacts that are common for encoder-decoder-based denoising models.
Federated Graph Neural Networks: Overview, Techniques and Challenges
This has led to the rapid development of the emerging research field of federated graph neural networks (FedGNNs).
Multi-Stage Deep Transfer Learning for EmIoT-enabled Human-Computer Interaction
Human–computer interaction has been studied to achieve speech interaction with IoT devices.
Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection
In the second stage, augmented source and target data with pseudo labels are adopted to perform the self-training for prediction consistency.
PP-MSVSR: Multi-Stage Video Super-Resolution
Different from the Single Image Super-Resolution(SISR) task, the key for Video Super-Resolution(VSR) task is to make full use of complementary information across frames to reconstruct the high-resolution sequence.
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study
Accurate organ at risk (OAR) segmentation is critical to reduce the radiotherapy post-treatment complications.
SSMF: Shifting Seasonal Matrix Factorization
In general, given a data stream of events with seasonal patterns that innovate over time, how can we effectively and efficiently forecast future events?
StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis
The emotion strength of synthesized speech can be controlled flexibly using a strength descriptor, which is obtained by an emotion attribute ranking function.
VisualTTS: TTS with Accurate Lip-Speech Synchronization for Automatic Voice Over
The proposed VisualTTS adopts two novel mechanisms that are 1) textual-visual attention, and 2) visual fusion strategy during acoustic decoding, which both contribute to forming accurate alignment between the input text content and lip motion in input lip sequence.
Decoupling Speaker-Independent Emotions for Voice Conversion Via Source-Filter Networks
We note that the decoupling of emotional features from other speech information (such as speaker, content, etc.)
FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting
On the contrary, the soft composition operates by stitching different patches into a whole feature map where pixels in overlapping regions are summed up.
Ranked #3 on
Video Inpainting
on DAVIS
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark
Researchers have explored advanced methods from computer vision and natural language processing to incorporate medical domain knowledge for the generation of readable medical reports.
Vector-Decomposed Disentanglement for Domain-Invariant Object Detection
Secondly, domain-specific representations are introduced as the differences between the input and domain-invariant representations.
Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization
It not only makes use of the relationships among the sentences, but also the relationships between the given query and the sentences.
Spatiotemporal information conversion machine for time-series prediction
Making predictions in a robust way is a difficult task only based on the observed data of a nonlinear system.
Emotional Voice Conversion: Theory, Databases and ESD
In this paper, we first provide a review of the state-of-the-art emotional voice conversion research, and the existing emotional speech databases.
Distributed TD(0) with Almost No Communication
In the global state model, we show that the convergence rate of our distributed one-shot averaging method matches the known convergence rate of TD(0).
Decoupled Spatial-Temporal Transformer for Video Inpainting
Seamless combination of these two novel designs forms a better spatial-temporal attention scheme and our proposed model achieves better performance than state-of-the-art video inpainting approaches with significant boosted efficiency.
Isconna: Streaming Anomaly Detection with Frequency and Patterns
Isconna does not actively explore or maintain pattern snippets; it instead measures the consecutive presence and absence of edge records.
Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability
To our best knowledge, this is the first study of reinforcement learning in emotional text-to-speech synthesis.
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network
Conditional generative adversarial networks (cGANs) target at synthesizing diverse images given the input conditions and latent codes, but unfortunately, they usually suffer from the issue of mode collapse.
How flux feeding causes eruptions of solar magnetic flux ropes with the hyperbolic flux tube configuration?
But it is unclear how flux feeding influences coronal flux ropes that are wrapped by hyperbolic flux tubes (HFT), and whether it is able to cause the flux-rope eruption.
Solar and Stellar Astrophysics
A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing
To address the first problem, a physics-informed, data-driven model (PIM), which instead of directly using machine setting parameters to predict porosity levels of printed parts, it first interprets machine settings into physical effects, such as laser energy density and laser radiation pressure.
BIG-bench Machine Learning
Physics-informed machine learning
Non-equilibrium Flux Rope Formation by Confined Flares Preceding a Solar Coronal Mass Ejection
We present evidence that a magnetic flux rope was formed before a coronal mass ejection (CME) and its associated long-duration flare during a pair of preceding confined eruptions and associated impulsive flares in a compound event in NOAA Active Region 12371.
Solar and Stellar Astrophysics
Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset
Emotional voice conversion aims to transform emotional prosody in speech while preserving the linguistic content and speaker identity.
Temporal Difference Learning as Gradient Splitting
Temporal difference learning with linear function approximation is a popular method to obtain a low-dimensional approximation of the value function of a policy in a Markov Decision Process.
Adam with Bandit Sampling for Deep Learning
In this paper, we propose a generalization of Adam, called Adambs, that allows us to also adapt to different training examples based on their importance in the model's convergence.
GraphSpeech: Syntax-Aware Graph Attention Network For Neural Speech Synthesis
Attention-based end-to-end text-to-speech synthesis (TTS) is superior to conventional statistical methods in many ways.
Real-Time Anomaly Detection in Edge Streams
Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory?
Modeling Prosodic Phrasing with Multi-Task Learning in Tacotron-based TTS
We propose a multi-task learning scheme for Tacotron training, that optimizes the system to predict both Mel spectrum and phrase breaks.
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion
We study alternating minimization for matrix completion in the simplest possible setting: completing a rank-one matrix from a revealed subset of the entries.
DEFM: Delay E mbedding based Forecast Machine for Time Series Forecasting by Spatiotemporal Information Transformation
Making accurate forecasts for a complex system is a challenge in various practical applications.
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching
Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias.
Keyphrase Prediction With Pre-trained Language Model
Considering the different characteristics of extractive and generative methods, we propose to divide the keyphrase prediction into two subtasks, i. e., present keyphrase extraction (PKE) and absent keyphrase generation (AKG), to fully exploit their respective advantages.
An Attention Transfer Model for Human-Assisted Failure Avoidance in Robot Manipulations
With the attention transfer, a robot understands \textit{what} and \textit{where} human concerns are to identify and correct abnormal manipulations.
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming
Due to heterogeneous robots inside a team and the resilient capabilities of robots, it is challenging to perform a task with an optimal balance between reasonable task allocations and maximum utilization of robot capability.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
Understanding and Optimizing Packed Neural Network Training for Hyper-Parameter Tuning
As neural networks are increasingly employed in machine learning practice, how to efficiently share limited training resources among a diverse set of model training tasks becomes a crucial issue.
WaveTTS: Tacotron-based TTS with Joint Time-Frequency Domain Loss
To address this problem, we propose a new training scheme for Tacotron-based TTS, referred to as WaveTTS, that has 2 loss functions: 1) time-domain loss, denoted as the waveform loss, that measures the distortion between the natural and generated waveform; and 2) frequency-domain loss, that measures the Mel-scale acoustic feature loss between the natural and generated acoustic features.
Teacher-Student Training for Robust Tacotron-based TTS
We first train a Tacotron2-based TTS model by always providing natural speech frames to the decoder, that serves as a teacher model.
Regularized Non-negative Spectral Embedding for Clustering
Spectral Clustering is a popular technique to split data points into groups, especially for complex datasets.
Ranking and Sampling in Open-Domain Question Answering
Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs.
Joint Lifelong Topic Model and Manifold Ranking for Document Summarization
The JTMMR model can improve the effect of the manifold ranking method by using the better semantic feature.
Conditional Adversarial Generative Flow for Controllable Image Synthesis
Flow-based generative models show great potential in image synthesis due to its reversible pipeline and exact log-likelihood target, yet it suffers from weak ability for conditional image synthesis, especially for multi-label or unaware conditions.
Benchmarking Time Series Databases with IoTDB-Benchmark for IoT Scenarios
With the goal of establishing a standard of evaluating TSDB systems, we present the IoTDB-Benchmark framework, specifically designed for TSDB and IoT application scenarios.
Databases
A Bandit Approach to Maximum Inner Product Search
There has been substantial research on sub-linear time approximate algorithms for Maximum Inner Product Search (MIPS).
A LSTM Approach with Sub-Word Embeddings for Mongolian Phrase Break Prediction
In this paper, we first utilize the word embedding that focuses on sub-word units to the Mongolian Phrase Break (PB) prediction task by using Long-Short-Term-Memory (LSTM) model.
Discrete Factorization Machines for Fast Feature-based Recommendation
In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation.
Phase Conductor on Multi-layered Attentions for Machine Comprehension
Attention models have been intensively studied to improve NLP tasks such as machine comprehension via both question-aware passage attention model and self-matching attention model.
Ranked #31 on
Question Answering
on SQuAD1.1 dev
Untangling Blockchain: A Data Processing View of Blockchain Systems
Blockchain technologies are gaining massive momentum in the last few years.
Databases Cryptography and Security
BLOCKBENCH: A Framework for Analyzing Private Blockchains
However, there is a clear lack of a systematic framework with which different systems can be analyzed and compared against each other.
Databases Cryptography and Security Distributed, Parallel, and Cluster Computing
Structural Embedding of Syntactic Trees for Machine Comprehension
Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees.
Ranked #40 on
Question Answering
on SQuAD1.1 dev
A Review of Methodologies for Natural-Language-Facilitated Human-Robot Cooperation
However, a thorough review, that can reveal latest methodologies to use NL to facilitate human-robot cooperation, is missing.
Systems of natural-language-facilitated human-robot cooperation: A review
Natural-language-facilitated human-robot cooperation (NLC), in which natural language (NL) is used to share knowledge between a human and a robot for conducting intuitive human-robot cooperation (HRC), is continuously developing in the recent decade.
Generating machine-executable plans from end-user's natural-language instructions
To address this NL-based human-machine communication problem and enable the machines to appropriately execute tasks by following the end-user's NL instructions, we developed a Machine-Executable-Plan-Generation (exePlan) method.
Simultaneous Low-rank Component and Graph Estimation for High-dimensional Graph Signals: Application to Brain Imaging
We propose an algorithm to uncover the intrinsic low-rank component of a high-dimensional, graph-smooth and grossly-corrupted dataset, under the situations that the underlying graph is unknown.
Feature Selection Based on Term Frequency and T-Test for Text Categorization
Much work has been done on feature selection.
