Search Results for author:
Found 390 papers, 107 papers with code
All Information is Valuable: Question Matching over Full Information Transmission Network
In this process, there are two kinds of critical information that are commonly employed: the representation information of original questions and the interactive information between pairs of questions.
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages
Recent emergence of multilingual pre-training language model (mPLM) has enabled breakthroughs on various downstream cross-lingual transfer (CLT) tasks.
Joint Goal Segmentation and Goal Success Prediction on Multi-Domain Conversations
To evaluate the performance of a multi-domain goal-oriented Dialogue System (DS), it is important to understand what the users’ goals are for the conversations and whether those goals are successfully achieved.
\textrm{DuReader}_{\textrm{vis}}: A Chinese Dataset for Open-domain Document Visual Question Answering
Open-domain question answering has been used in a wide range of applications, such as web search and enterprise search, which usually takes clean texts extracted from various formats of documents (e. g., web pages, PDFs, or Word documents) as the information source.
Learning to See in the Dark with Events
A detail enhancing branch is proposed to reconstruct day light-specific features from the domain-invariant representations in a residual manner, regularized by a ranking loss.
A Coarse-to-Fine Labeling Framework for Joint Word Segmentation, POS Tagging, and Constituent Parsing
In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i. e., phrase, subphrase, word, subword).
Physics-guided Residual Learning for Probabilistic Power Flow Analysis
In addition, based on our proposed framework, we design three methods to initialize the weights of the shortcut connection layer according to the physical characteristics of AC-PF equations.
MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
This architectural improvement leads to a new diffusion-based medical image segmentation method called MedSegDiff-V2, which significantly improves the performance of MedSegDiff.
From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognition
In this work, we propose a new parameter-efficient learning framework based on neural model reprogramming for cross-lingual speech recognition, which can \textbf{re-purpose} well-trained English automatic speech recognition (ASR) models to recognize the other languages.
Super-Resolution Harmonic Retrieval of Non-Circular Signals
This paper proposes a super-resolution harmonic retrieval method for uncorrelated strictly non-circular signals, whose covariance and pseudo-covariance present Toeplitz and Hankel structures, respectively.
OA-BEV: Bringing Object Awareness to Bird's-Eye-View Representation for Multi-Camera 3D Object Detection
To this end, we propose OA-BEV, a network that can be plugged into the BEV-based 3D object detection framework to bring out the objects by incorporating object-aware pseudo-3D features and depth features.
Learning Trajectory-Word Alignments for Video-Language Tasks
Besides T2W attention, we also follow previous VDL-BERTs to set a word-to-patch (W2P) attention in the cross-modal encoder.
Adaptively Clustering Neighbor Elements for Image Captioning
We design a novel global-local Transformer named \textbf{Ada-ClustFormer} (\textbf{ACF}) to generate captions.
Dynamic Sparse Network for Time Series Classification: Learning What to "see''
The receptive field (RF), which determines the region of time series to be ``seen'' and used, is critical to improve the performance for time series classification (TSC).
Mu$^{2}$SLAM: Multitask, Multilingual Speech and Language Models
We present Mu$^{2}$SLAM, a multilingual sequence-to-sequence model pre-trained jointly on unlabeled speech, unlabeled text and supervised data spanning Automatic Speech Recognition (ASR), Automatic Speech Translation (AST) and Machine Translation (MT), in over 100 languages.
Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts
Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user's interest.
Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality
Non-convex AC optimal power flow (AC-OPF) is a fundamental optimization problem in power system analysis.
Entity Set Co-Expansion in StackOverflow
Given a few seed entities of a certain type (e. g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds.
Feature Aggregation and Propagation Network for Camouflaged Object Detection
In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection.
ExpNet: A unified network for Expert-Level Classification
In the paper, we call them expert-level classification.
TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering
In this paper, we aim to address the above limitation by leveraging the implicit knowledge stored in PrLMs and propose a two-stage prompt-based unsupervised commonsense question answering framework (TSGP).
Leveraging per Image-Token Consistency for Vision-Language Pre-training
(2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations.
Disentangling Task Relations for Few-shot Text Classification via Self-Supervised Hierarchical Task Clustering
However, most prior works assume that all the tasks are sampled from a single data source, which cannot adapt to real-world scenarios where tasks are heterogeneous and lie in different distributions.
Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease
Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants.
TLP: A Deep Learning-based Cost Model for Tensor Program Tuning
Instead of extracting features from the tensor program itself, TLP extracts features from the schedule primitives.
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set.
A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition
We propose a quantum kernel learning (QKL) framework to address the inherent data sparsity issues often encountered in training large-scare acoustic models in low-resource scenarios.
Max Markov Chain
In this paper, we introduce Max Markov Chain (MMC), a novel representation for a useful subset of High-order Markov Chains (HMCs) with sparse correlations among the states.
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff.
Modular Hybrid Autoregressive Transducer
In this work, we propose a modular hybrid autoregressive transducer (MHAT) that has structurally separated label and blank decoders to predict label and blank distributions, respectively, along with a shared acoustic encoder.
Accelerating RNN-T Training and Inference Using CTC guidance
We propose a novel method to accelerate training and inference process of recurrent neural network transducer (RNN-T) based on the guidance from a co-trained connectionist temporal classification (CTC) model.
Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation
Adapting a neural text-to-speech (TTS) model to a target speaker typically involves fine-tuning most if not all of the parameters of a pretrained multi-speaker backbone model.
Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention
Audio captioning is the task of generating captions that describe the content of audio clips.
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models.
Personalized Dialogue Generation with Persona-Adaptive Attention
Persona-based dialogue systems aim to generate consistent responses based on historical context and predefined persona.
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR
First, we show that by combining speech representations with byte-level text representations and use of language embeddings, we can dramatically reduce the Character Error Rate (CER) on languages with no supervised speech from 64. 8\% to 30. 8\%, a relative reduction of 53\%.
Improving generalizability of distilled self-supervised speech processing models under distorted settings
Self-supervised learned (SSL) speech pre-trained models perform well across various speech processing tasks.
JOIST: A Joint Speech and Text Streaming Model For ASR
In addition, we explore JOIST using a streaming E2E model with an order of magnitude more data, which are also novelties compared to previous works.
Comparison of Soft and Hard Target RNN-T Distillation for Large-scale ASR
Knowledge distillation is an effective machine learning technique to transfer knowledge from a teacher model to a smaller student model, especially with unlabeled data.
GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction
In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
On Clustering Trend in Language Evolution Based on Dynamical Behaviors of Multi-Agent Model
Computer model has been extensively adopted to overcome the time limitation of language evolution by transforming language theory into physical modeling mechanism, which helps to explore the general laws of the evolution.
Fault Detection Scheme for Grid-Forming Inverters in Islanded Droop-Controlled AC Microgrids
An observer-based fault detection scheme for grid-forming inverters operating in islanded droop-controlled AC microgrids is proposed.
Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition
The critical goal of gait recognition is to acquire the inter-frame walking habit representation from the gait sequences.
Real-Time Power System Event Detection: A Novel Instance Selection Approach
Instance selection is a vital technique for energy big data analytics.
How Good Is Neural Combinatorial Optimization?
Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts.
Discrete Linear Canonical Transform on Graphs
In this paper, we propose and design the definition of the discrete linear canonical transform on graphs (GLCT), which is an extension of the discrete linear canonical transform (DLCT), just as the graph Fourier transform (GFT) is an extension of the discrete Fourier transform (DFT).
Online Beam Learning with Interference Nulling for Millimeter Wave MIMO Systems
Furthermore, a hardware proof-of-concept prototype based on mmWave phased arrays is built and used to implement and evaluate the developed online beam learning solutions in realistic scenarios.
Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems
Cell-free massive multiple-input-multiple-output is promising to meet the stringent quality-of-experience (QoE) requirements of railway wireless communications by coordinating many successional access points (APs) to serve the onboard users coherently.
Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification
By taking advantage of the local manifold structure, a Laplacian graph is constructed from the superpixels to ensure that a typical pixel should be similar to its neighbors within the same superpixel.
An Adaptive Repeated-Intersection-Reduction Local Search for the Maximum Independent Set Problem
The maximum independent set (MIS) problem, a classical NP-hard problem with extensive applications in various areas, aims to find the largest set of vertices with no edge among them.
See What You See: Self-supervised Cross-modal Retrieval of Visual Stimuli from Brain Activity
Recent studies demonstrate the use of a two-stage supervised framework to generate images that depict human perception to visual stimuli from EEG, referring to EEG-visual reconstruction.
CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems
In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases.
An Efficient Person Clustering Algorithm for Open Checkout-free Groceries
Then, to ensure that the method adapts to the dynamic and unseen person flow, we propose Graph Convolutional Network (GCN) with a simple Nearest Neighbor (NN) strategy to accurately cluster the instances of CSG.
A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesis
In human speech, the attitude of a speaker cannot be fully expressed only by the textual content.
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support images.
Ranked #1 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders
Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community.
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
In this study, we propose a novel Deformer module along with a multi-scale framework for the deformable image registration task.
Structured Light with Redundancy Codes
We propose three applications using our redundancy codes: (1) Self error-correction for SL imaging under strong ambient light, (2) Error detection for adaptive reconstruction under global illumination, and (3) Interference filtering with device-specific projection sequence encoding, especially for event camera-based SL and light curtain devices.
Soft Retargeting Network for Click Through Rate Prediction
In this paper, we focus on a new type of user interest, i. e., user retargeting interest.
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech
We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of Speech benchmark.
Co-optimization of Battery Routing and Load Restoration for Microgrids with Mobile Energy Storage Systems
Mobile energy storage systems (MESS) offer great operational flexibility to enhance the resiliency of distribution systems in an emergency condition.
Heterformer: A Transformer Architecture for Node Representation Learning on Heterogeneous Text-Rich Networks
We study node representation learning on heterogeneous text-rich networks, where nodes and edges are multi-typed and some types of nodes are associated with text information.
Transferable Physical Attack against Object Detection with Separable Attention
Transferable adversarial attack is always in the spotlight since deep learning models have been demonstrated to be vulnerable to adversarial samples.
Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation
Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently.
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds
Discovering latent topics from text corpora has been studied for decades.
Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus
With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses.
Variation-cognizant Probabilistic Power Flow Analysis via Multi-task Learning
With an increasing high penetration of solar photovoltaic generation in electric power grids, voltage phasors and branch power flows experience more severe fluctuations.
Differentially Private Load Restoration for Microgrids with Distributed Energy Storage
Distributed energy storage systems (ESSs) can be efficiently leveraged for load restoration (LR) for a microgrid (MG) in island mode.
Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis
Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies.
Adversarial Filtering Modeling on Long-term User Behavior Sequences for Click-Through Rate Prediction
Rich user behavior information is of great importance for capturing and understanding user interest in click-through rate (CTR) prediction.
MAESTRO: Matched Speech Text Representations through Modality Matching
Self-supervised learning from speech signals aims to learn the latent structure inherent in the signal, while self-supervised learning from text attempts to capture lexical information.
Unsupervised Data Selection via Discrete Speech Representation for ASR
Self-supervised learning of speech representations has achieved impressive results in improving automatic speech recognition (ASR).
Modern Views of Machine Learning for Precision Psychiatry
In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice.
Improving Distortion Robustness of Self-supervised Speech Processing Tasks with Domain Adaptation
Speech distortions are a long-standing problem that degrades the performance of supervisely trained speech processing models.
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.
AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection
This paper proposed a novel anomaly detection (AD) approach of High-speed Train images based on convolutional neural networks and the Vision Transformer.
LibMTL: A Python Library for Multi-Task Learning
This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi-Task Learning (MTL).
OneLabeler: A Flexible System for Building Data Labeling Tools
In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios.
Contrastive Graph Learning for Population-based fMRI Classification
Nonetheless, existing contrastive methods generate resemblant pairs only on pixel-level features of 3D medical images, while the functional connectivity that reveals critical cognitive information is under-explored.
Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation
End-to-end speech-to-speech translation (S2ST) without relying on intermediate text representations is a rapidly emerging frontier of research.
XTREME-S: Evaluating Cross-lingual Speech Representations
Covering 102 languages from 10+ language families, 3 different domains and 4 task families, XTREME-S aims to simplify multilingual speech representation evaluation, as well as catalyze research in "universal" speech representation learning.
Image Steganography based on Style Transfer
Image steganography is the art and science of using images as cover for covert communications.
Ask2Mask: Guided Data Selection for Masked Speech Modeling
They treat all unsupervised speech samples with equal weight, which hinders learning as not all samples have relevant information to learn meaningful representations.
Path-Aware Graph Attention for HD Maps in Motion Prediction
The success of motion prediction for autonomous driving relies on integration of information from the HD maps.
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning
Although using bounds as surrogate functions to design safe RL algorithms have appeared in some existing works, we develop them at least three aspects: (i) We provide a rigorous theoretical analysis to extend the surrogate functions to generalized advantage estimator (GAE).
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text Classification
Large-scale multi-label text classification (LMTC) aims to associate a document with its relevant labels from a large candidate set.
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural language processing tasks: Unidirectional PLMs (e. g., GPT) are well known for their superior text generation capabilities; bidirectional PLMs (e. g., BERT) have been the prominent choice for natural language understanding (NLU) tasks.
Ranked #4 on
Zero-Shot Text Classification
on AG News
Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations
Interestingly, there have not been standard approaches to deploy PLMs for topic discovery as better alternatives to topic models.
Self-supervised Learning with Random-projection Quantizer for Speech Recognition
In particular the quantizer projects speech inputs with a randomly initialized matrix, and does a nearest-neighbor lookup in a randomly-initialized codebook.
mSLAM: Massively multilingual joint pre-training for speech and text
We present mSLAM, a multilingual Speech and LAnguage Model that learns cross-lingual cross-modal representations of speech and text by pre-training jointly on large amounts of unlabeled speech and text in multiple languages.
Ranked #1 on
Spoken language identification
on Fleurs
(using extra training data)
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training
In this work, we propose LEMON, a general framework for language-based environment manipulation tasks.
Domain Adaptation via Bidirectional Cross-Attention Transformer
Specifically, in BCAT, we design a weight-sharing quadruple-branch transformer with a bidirectional cross-attention mechanism to learn domain-invariant feature representations.
AutoMine: An Unmanned Mine Dataset
The main contributions of the AutoMine dataset are as follows: 1. The first autonomous driving dataset for perception and localization in mine scenarios.
Balanced and Hierarchical Relation Learning for One-Shot Object Detection
In this paper, we introduce the balanced and hierarchical learning for our detector.
JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering
Specifically, JointLK performs joint reasoning between LM and GNN through a novel dense bidirectional attention module, in which each question token attends on KG nodes and each KG node attends on question tokens, and the two modal representations fuse and update mutually by multi-step interactions.
Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing
Moreover, we propose a simple constrained Viterbi procedure to ensure the legality of the output graph according to the constraints of the SRL structure.
Multiple Interest and Fine Granularity Network for User Modeling
User modeling plays a fundamental role in industrial recommender systems, either in the matching stage and the ranking stage, in terms of both the customer experience and business revenue.
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides
Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.
Effective Meta-Regularization by Kernelized Proximal Regularization
We study the problem of meta-learning, which has proved to be advantageous to accelerate learning new tasks with a few samples.
VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion
However, this approach often suffers from the mismatch between the resolution of point clouds and RGB images, leading to sub-optimal performance.
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning
Multi-Task Learning (MTL) has achieved success in various fields.
Deep Safe Multi-Task Learning
We theoretically study the safeness of both learning strategies in the DSMTL model to show that the proposed methods can achieve some versions of safe multi-task learning.
Joint Unsupervised and Supervised Training for Multilingual ASR
Our average WER of all languages outperforms average monolingual baseline by 33. 3%, and the state-of-the-art 2-stage XLSR by 32%.
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation
Real-time location inference of social media users is the fundamental of some spatial applications such as localized search and event detection.
MotifClass: Weakly Supervised Text Classification with Higher-order Metadata Information
We study the problem of weakly supervised text classification, which aims to classify text documents into a set of pre-defined categories with category surface names only and without any annotated training document provided.
Load Restoration in Islanded Microgrids: Formulation and Solution Strategies
In this paper, we consider the problem of load restoration in a microgrid (MG) that is islanded from the upstream DS because of an extreme weather event.
Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis
Advancing lithium-ion batteries (LIBs) in both design and usage is key to promoting electrification in the coming decades to mitigate human-caused climate change.
Transient Synchronization Stability Analysis of Wind Farms with MMC-HVDC Integration Under Offshore AC Grid Fault
The MMC-HVDC connected offshore wind farms (OWFs) could suffer short circuit fault (SCF), whereas their transient stability is not well analysed.
SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training
We build a single encoder with the BERT objective on unlabeled text together with the w2v-BERT objective on unlabeled speech.
Dynamic Feature Alignment for Semi-supervised Domain Adaptation
Most research on domain adaptation has focused on the purely unsupervised setting, where no labeled examples in the target domain are available.
Region Semantically Aligned Network for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes.
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.
Ranked #1 on
Semantic Role Labeling
on OntoNotes
Multi-View Self-Attention Based Transformer for Speaker Recognition
In this work, we propose a novel multi-view self-attention mechanism and present an empirical study of different Transformer variants with or without the proposed attention mechanism for speaker recognition.
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers
Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishing real from synthetic speech.
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition
As end-to-end automatic speech recognition (ASR) models reach promising performance, various downstream tasks rely on good confidence estimators for these systems.
Sim2Real for Soft Robotic Fish via Differentiable Simulation
Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots.
Multi-Subspace Structured Meta-Learning
We propose a MUlti-Subspace structured Meta-Learning (MUSML) algorithm to learn the subspace bases.
Comparison of Object Detection Algorithms Using Video and Thermal Images Collected from a UAS Platform: An Application of Drones in Traffic Management
The results show that a background subtraction-based method can achieve good detection performance on RGB images (F1 scores around 0. 9 for most cases), and a more varied performance is seen on thermal images with different azimuth angles.
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio.
A Simple Self-calibration Method for The Internal Time Synchronization of MEMS LiDAR
This paper proposes a simple self-calibration method for the internal time synchronization of MEMS(Micro-electromechanical systems) LiDAR during research and development.
Multi-Task Learning in Natural Language Processing: An Overview
Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP).
Generating Active Explicable Plans in Human-Robot Teaming
In our experimental evaluation, we verify that our approach generates more efficient explicable plans while successfully capturing the dynamic belief change of the human teammate.
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification
Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table.
Domain Adaptation by Maximizing Population Correlation with Neural Architecture Search
Base on the PC function, we propose a new method called Domain Adaptation by Maximizing Population Correlation (DAMPC) to learn a domain-invariant feature representation for DA.
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training
We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base.
Injecting Text in Self-Supervised Speech Pretraining
The proposed method, tts4pretrain complements the power of contrastive learning in self-supervision with linguistic/lexical representations derived from synthesized speech, effectively learning from untranscribed speech and unspoken text.
Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach
Then, a decoder with hierarchical temporal attention enables a similar day selection, which re-evaluates the importance of historical information at each time step.
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems
This paper deals with the event and intrusion detection problem by leveraging a stream data mining classifier (Hoeffding adaptive tree) with semi-supervised learning techniques to distinguish cyber-attacks from regular system perturbations accurately.
Mitigating Greenhouse Gas Emissions Through Generative Adversarial Networks Based Wildfire Prediction
Over the past decade, the number of wildfire has increased significantly around the world, especially in the State of California.
Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model
Deep reinforcement learning (DRL) algorithms have proven effective in robot navigation, especially in unknown environments, through directly mapping perception inputs into robot control commands.
W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training
In particular, when compared to published models such as conformer-based wav2vec~2. 0 and HuBERT, our model shows~5\% to~10\% relative WER reduction on the test-clean and test-other subsets.
Ranked #1 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
Personalized Image Semantic Segmentation
In this paper, we address the problem of personalized image segmentation.
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting
As cameras are increasingly deployed in new application domains such as autonomous driving, performing 3D object detection on monocular images becomes an important task for visual scene understanding.
Multi-Modal 3D Object Detection in Autonomous Driving: a Survey
In this survey, we first introduce the background of popular sensors for autonomous cars, including their common data representations as well as object detection networks developed for each type of sensor data.
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection
Cross-domain weakly supervised object detection aims to adapt object-level knowledge from a fully labeled source domain dataset (i. e. with object bounding boxes) to train object detectors for target domains that are weakly labeled (i. e. with image-level tags).
Ranked #4 on
Weakly Supervised Object Detection
on Clipart1k
Sparse Multi-Path Corrections in Fringe Projection Profilometry
Three-dimensional scanning by means of structured light illumination is an active imaging technique involving projecting and capturing a series of striped patterns and then using the observed warping of stripes to reconstruct the target object's surface through triangulating each pixel in the camera to a unique projector coordinate corresponding to a particular feature in the projected patterns.
WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis
The model takes an input phoneme sequence, and through an iterative refinement process, generates an audio waveform.
FedNILM: Applying Federated Learning to NILM Applications at the Edge
Non-intrusive load monitoring (NILM) helps disaggregate the household's main electricity consumption to energy usages of individual appliances, thus greatly cutting down the cost in fine-grained household load monitoring.
Rethinking Training from Scratch for Object Detection
The ImageNet pre-training initialization is the de-facto standard for object detection.
More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances.
Large-Signal Grid-Synchronization Stability Analysis of PLL-based VSCs Using Lyapunov's Direct Method
Grid-synchronization stability (GSS) is an emerging stability issue of grid-tied voltage source converters (VSCs), which can be provoked by severe grid voltage sags.
Scaling End-to-End Models for Large-Scale Multilingual ASR
Building ASR models across many languages is a challenging multi-task learning problem due to large variations and heavily unbalanced data.
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction
Confidence scores are very useful for downstream applications of automatic speech recognition (ASR) systems.
Deep Latent Emotion Network for Multi-Task Learning
DLEN is deployed on a real-world multi-task feed recommendation scenario of Tencent QQ-Small-World with a dataset containing over a billion samples, and it exhibits a significant performance advantage over the SOTA MTL model in offline evaluation, together with a considerable increase by 3. 02% in view-count and 2. 63% in user stay-time in production.
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash.
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
In our framework, the server evaluates devices' value of training based on their training loss.
Pushing the Limits of Non-Autoregressive Speech Recognition
We combine recent advancements in end-to-end speech recognition to non-autoregressive automatic speech recognition.
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models
To attack RNN-T, we find prepending perturbation is more effective than the additive perturbation, and can mislead the models to predict the same short target on utterances of arbitrary length.
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network
We present SpeechStew, a speech recognition model that is trained on a combination of various publicly available speech recognition datasets: AMI, Broadcast News, Common Voice, LibriSpeech, Switchboard/Fisher, Tedlium, and Wall Street Journal.
Ranked #1 on
Speech Recognition
on CHiME-6 eval
Using Simulation to Aid the Design and Optimization of Intelligent User Interfaces for Quality Assurance Processes in Machine Learning
Because QA4ML users have to view a non-trivial amount of data and perform many input actions to correct errors made by the ML model, an optimally-designed user interface (UI) can reduce the cost of interactions significantly.
On the limits of algorithmic prediction across the globe
We tested how well predictive models of human behavior trained in a developed country generalize to people in less developed countries by modeling global variation in 200 predictors of academic achievement on nationally representative student data for 65 countries.
PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS
This paper introduces PnG BERT, a new encoder model for neural TTS.
Residual Energy-Based Models for End-to-End Speech Recognition
End-to-end models with auto-regressive decoders have shown impressive results for automatic speech recognition (ASR).
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene Segmentation
Current scene segmentation methods suffer from cumbersome model structures and high computational complexity, impeding their applications to real-world scenarios that require real-time processing.
Learning Word-Level Confidence For Subword End-to-End ASR
We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR).
Deciphering Star Cluster Evolution by Shape Morphology
A significant negative correlation between the overall ellipticities and masses is also detected for the sample clusters with log(age/year) $\geq$ 8, suggesting that the overall shapes of the clusters are possibly influenced by the number of members and masses, in addition to the external forces and the surrounding environment.
Astrophysics of Galaxies Solar and Stellar Astrophysics
Self-supervised Low Light Image Enhancement and Denoising
Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed.
Fronthaul Compression and Passive Beamforming Design for Intelligent Reflecting Surface-aided Cloud Radio Access Networks
This letter studies a cloud radio access network (C-RAN) with multiple intelligent reflecting surfaces (IRS) deployed between users and remote radio heads (RRH).
Echo State Speech Recognition
We propose automatic speech recognition (ASR) models inspired by echo state network (ESN), in which a subset of recurrent neural networks (RNN) layers in the models are randomly initialized and untrained.
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
Employing large antenna arrays is a key characteristic of millimeter wave (mmWave) and terahertz communication systems.
MATCH: Metadata-Aware Text Classification in A Large Hierarchy
Multi-label text classification refers to the problem of assigning each given document its most relevant labels from the label set.
Multi-Objective Meta Learning
Empirically, we show the effectiveness of the proposed MOML framework in several meta learning problems, including few-shot learning, neural architecture search, domain adaptation, and multi-task learning.
Phase discontinuities induced scintillation enhancement: coherent vortex beams propagating through weak oceanic turbulence
Under the impact of an infinitely extended edge phase dislocation, optical vortices (screw phase dislocations) induce scintillation enhancement.
Optics
Joint Transmit Precoding and Reflect Beamforming Design for IRS-Assisted MIMO Cognitive Radio Systems
Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs).
Information Theory Signal Processing Information Theory
Photoproduction $γp \to K^+Λ(1520)$ in an effective Lagrangian approach
In addition to the $t$-channel $K$ and $K^\ast$ exchanges, the $u$-channel $\Lambda$ exchange, the $s$-channel nucleon exchange, and the interaction current, a minimal number of nucleon resonances in the $s$ channel are introduced in constructing the reaction amplitudes to describe the data.
High Energy Physics - Phenomenology Nuclear Theory
Model-based cellular kinetic analysis of SARS-CoV-2 infection: different immune response modes and treatment strategies
Integrating theoretical results with clinical COVID-19 patients' data, we classified the COVID-19 development processes into three typical modes of immune responses, correlated with the clinical classification of mild & moderate, severe and critical patients.
Generative Adversarial U-Net for Domain-free Medical Image Augmentation
The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing.