Search Results for author:
Found 475 papers, 164 papers with code
On Algorithms for Sparse Multi-factor NMF
Nonnegative matrix factorization (NMF) is a popular data analysis method, the objective of which is to decompose a matrix with all nonnegative components into the product of two other nonnegative matrices.
Recipe recognition with large multimodal food dataset
We present deep experiments of recipe recognition on our dataset using visual, textual information and fusion.
Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation
Existing approaches to resource allocation for nowadays stochastic networks are challenged to meet fast convergence and tolerable delay requirements.
A multi-task learning model for malware classification with useful file access pattern from API call sequence
Based on API call sequences, semantic-aware and machine learning (ML) based malware classifiers can be built for malware detection or classification.
On Multiplicative Multitask Feature Learning
We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers.
Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer
That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.
Classification of Neurological Gait Disorders Using Multi-task Feature Learning
An advanced machine learning method, multi-task feature learning (MTFL), is used to jointly train classification models of a subject's gait in three classes, post-stroke, PD and healthy gait.
Clipper: A Low-Latency Online Prediction Serving System
In this paper, we introduce Clipper, a general-purpose low-latency prediction serving system.
Robust Learning with Kernel Mean p-Power Error Loss
Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing.
Deep Reinforcement Learning for Visual Object Tracking in Videos
In this paper we introduce a fully end-to-end approach for visual tracking in videos that learns to predict the bounding box locations of a target object at every frame.
IDK Cascades: Fast Deep Learning by Learning not to Overthink
Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions.
Non-asymptotic entanglement distillation
For isotropic states, it can be further simplified to a linear program.
Quantum Physics
A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud
In tests using outcrop point cloud data, the proposed method identified and extracted the full extent of individual fractures with high accuracy.
Predicting Users' Negative Feedbacks in Multi-Turn Human-Computer Dialogues
Besides, strategies of obtaining distance supervision data for pre-training are also discussed in this work.
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications.
SkipNet: Learning Dynamic Routing in Convolutional Networks
While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient.
Video Captioning via Hierarchical Reinforcement Learning
Video captioning is the task of automatically generating a textual description of the actions in a video.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Group Linguistic Bias Aware Neural Response Generation
For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users{'} preference on language styles, topics, etc.
Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama's voice using GAN, WaveNet and low-quality found data
Thanks to the growing availability of spoofing databases and rapid advances in using them, systems for detecting voice spoofing attacks are becoming more and more capable, and error rates close to zero are being reached for the ASVspoof2015 database.
Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation
In this paper, we take a radical approach to bridge the gap between synthetic studies and real-world practices---We propose a novel, planned-ahead hybrid reinforcement learning model that combines model-free and model-based reinforcement learning to solve a real-world vision-language navigation task.
Model-based Reinforcement Learning
reinforcement-learning
+4
Speech waveform synthesis from MFCC sequences with generative adversarial networks
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis.
A comparison of recent waveform generation and acoustic modeling methods for neural-network-based speech synthesis
Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in acoustic modeling can be overcome by using advanced machine learning approaches.
Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning
Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task.
Fast Weight Long Short-Term Memory
Associative memory using fast weights is a short-term memory mechanism that substantially improves the memory capacity and time scale of recurrent neural networks (RNNs).
No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling
Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem.
Ranked #13 on
Visual Storytelling
on VIST
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
Ranked #5 on
Multiple Object Tracking
on BDD100K test
Deep Mixture of Experts via Shallow Embedding
Larger networks generally have greater representational power at the cost of increased computational complexity.
Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video
We present Accel, a novel semantic video segmentation system that achieves high accuracy at low inference cost by combining the predictions of two network branches: (1) a reference branch that extracts high-detail features on a reference keyframe, and warps these features forward using frame-to-frame optical flow estimates, and (2) an update branch that computes features of adjustable quality on the current frame, performing a temporal update at each video frame.
S3D: Single Shot multi-Span Detector via Fully 3D Convolutional Networks
In this paper, we present a novel Single Shot multi-Span Detector for temporal activity detection in long, untrimmed videos using a simple end-to-end fully three-dimensional convolutional (Conv3D) network.
PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction
We propose a novel 3D spatial representation for data fusion and scene reconstruction.
Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesis
Generating versatile and appropriate synthetic speech requires control over the output expression separate from the spoken text.
Investigating accuracy of pitch-accent annotations in neural network-based speech synthesis and denoising effects
We investigated the impact of noisy linguistic features on the performance of a Japanese speech synthesis system based on neural network that uses WaveNet vocoder.
XL-NBT: A Cross-lingual Neural Belief Tracking Framework
Then, we pre-train a state tracker for the source language as a teacher, which is able to exploit easy-to-access parallel data.
Dynamic parameter reallocation improves trainability of deep convolutional networks
Network pruning has emerged as a powerful technique for reducing the size of deep neural networks.
Neural source-filter-based waveform model for statistical parametric speech synthesis
Neural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressive (AR) structure.
Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language
Towards end-to-end Japanese speech synthesis, we extend Tacotron to systems with self-attention to capture long-term dependencies related to pitch accents and compare their audio quality with classical pipeline systems under various conditions to show their pros and cons.
STFT spectral loss for training a neural speech waveform model
This paper proposes a new loss using short-time Fourier transform (STFT) spectra for the aim of training a high-performance neural speech waveform model that predicts raw continuous speech waveform samples directly.
Audiovisual speaker conversion: jointly and simultaneously transforming facial expression and acoustic characteristics
Transforming the facial and acoustic features together makes it possible for the converted voice and facial expressions to be highly correlated and for the generated target speaker to appear and sound natural.
Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning
Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to open vocabulary scenarios.
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments.
Ranked #2 on
Vision-Language Navigation
on Room2Room
Guided Feature Selection for Deep Visual Odometry
We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks.
MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment
In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network.
Few-shot Object Detection via Feature Reweighting
The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples.
Ranked #21 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Conditional Graph Neural Processes: A Functional Autoencoder Approach
Thus, we refer to our model as Conditional Graph Neural Process (CGNP).
Explanatory Graphs for CNNs
This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN.
Residual Attention based Network for Hand Bone Age Assessment
The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.
Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation
However, there remain two unsolved challenges: i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities.
Interpretable CNNs for Object Classification
This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part.
Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding
In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end.
HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding
Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.
When does reinforcement learning stand out in quantum control? A comparative study on state preparation
In this work, we perform a comparative study on the efficacy of three reinforcement learning algorithms: tabular Q-learning, deep Q-learning, and policy gradient, as well as two non-machine-learning methods: stochastic gradient descent and Krotov algorithms, in the problem of preparing a desired quantum state.
Quantum Physics
Parameter Efficient Training of Deep Convolutional Neural Networks by Dynamic Sparse Reparameterization
We evaluate the performance of dynamic reallocation methods in training deep convolutional networks and show that our method outperforms previous static and dynamic reparameterization methods, yielding the best accuracy for a fixed parameter budget, on par with accuracies obtained by iteratively pruning a pre-trained dense model.
Visual Query Answering by Entity-Attribute Graph Matching and Reasoning
Based on GEA and Q, we provide techniques to find matches of Q in GEA, as the answer of Qnl in Img.
DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction
The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask.
Joint training framework for text-to-speech and voice conversion using multi-source Tacotron and WaveNet
We propose using an extended model architecture of Tacotron, that is a multi-source sequence-to-sequence model with a dual attention mechanism as the shared model for both the TTS and VC tasks.
Training Multi-Speaker Neural Text-to-Speech Systems using Speaker-Imbalanced Speech Corpora
When the available data of a target speaker is insufficient to train a high quality speaker-dependent neural text-to-speech (TTS) system, we can combine data from multiple speakers and train a multi-speaker TTS model instead.
Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry
Most previous learning-based visual odometry (VO) methods take VO as a pure tracking problem.
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation
The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs.
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.
TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning
We show that TAFE-Net is highly effective in generalizing to new tasks or concepts and evaluate the TAFE-Net on a range of benchmarks in zero-shot and few-shot learning.
Ranked #1 on
Few-Shot Image Classification
on aPY - 0-Shot
ACE: Adapting to Changing Environments for Semantic Segmentation
However, neural classifiers are often extremely brittle when confronted with domain shift---changes in the input distribution that occur over time.
Maximum Correntropy Criterion with Variable Center
The kernel function in correntropy is usually restricted to the Gaussian function with center located at zero.
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech.
REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments
One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language.
Generalizable control for quantum parameter estimation through reinforcement learning
Measurement and estimation of parameters are essential for science and engineering, where one of the main quests is to find systematic schemes that can achieve high precision.
Quantum Physics Mesoscale and Nanoscale Physics
Neural source-filter waveform models for statistical parametric speech synthesis
Other models such as Parallel WaveNet and ClariNet bring together the benefits of AR and IAF-based models and train an IAF model by transferring the knowledge from a pre-trained AR teacher to an IAF student without any sequential transformation.
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction
In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain.
Compositional Coding for Collaborative Filtering
However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data.
Multi-Kernel Correntropy for Robust Learning
In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths.
Speaker Anonymization Using X-vector and Neural Waveform Models
One solution to mitigate these concerns involves the concealing of speaker identities before the sharing of speech data.
One-shot entanglement distillation beyond LOCC
We show in particular that the $\varepsilon$-error one-shot distillable entanglement of any pure state is the same under all sets of operations ranging from one-way LOCC to separability-preserving operations or operations preserving the set of states with positive partial transpose, and can be computed exactly as a quadratically constrained linear program.
Quantum Physics Mathematical Physics Mathematical Physics
Spatial Heterogeneity Automatic Detection and Estimation
Spatial regression is widely used for modeling the relationship between a dependent variable and explanatory covariates.
Methodology
Self-Supervised Learning for Contextualized Extractive Summarization
Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss, which does not explicitly capture the global context at the document level.
Recognizing License Plates in Real-Time
To enable real-time and accurate license plate recognition, in this work, we propose a set of techniques: 1) a contour reconstruction method along with edge-detection to quickly detect the candidate plates; 2) a simple zero-one-alternation scheme to effectively remove the fake top and bottom borders around plates to facilitate more accurate segmentation of characters on plates; 3) a set of techniques to augment the training data, incorporate SIFT features into the CNN network, and exploit transfer learning to obtain the initial parameters for more effective training; and 4) a two-phase verification procedure to determine the correct plate at low cost, a statistical filtering in the plate detection stage to quickly remove unwanted candidates, and the accurate CR results after the CR process to perform further plate verification without additional processing.
Task-Aware Feature Generation for Zero-Shot Compositional Learning
In this work, we propose a task-aware feature generation (TFG) framework for compositional learning, which generates features of novel visual concepts by transferring knowledge from previously seen concepts.
Self-Supervised Dialogue Learning
The sequential order of utterances is often meaningful in coherent dialogues, and the order changes of utterances could lead to low-quality and incoherent conversations.
Modeling the Uncertainty in Electronic Health Records: a Bayesian Deep Learning Approach
Deep learning models have exhibited superior performance in predictive tasks with the explosively increasing Electronic Health Records (EHR).
Outfit Compatibility Prediction and Diagnosis with Multi-Layered Comparison Network
Existing works about fashion outfit compatibility focus on predicting the overall compatibility of a set of fashion items with their information from different modalities.
Privacy-preserving Distributed Machine Learning via Local Randomization and ADMM Perturbation
With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks.
Local Supports Global: Deep Camera Relocalization with Sequence Enhancement
We propose to leverage the local information in image sequences to support global camera relocalization.
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry
As single-view depth estimation is an ill-posed problem, and photometric loss is incapable of discriminating distortion artifacts of warped images, the estimated depth is vague and pose is inaccurate.
Latent Part-of-Speech Sequences for Neural Machine Translation
In this work, we introduce a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics, while allowing for effective and efficient inference over the latent space.
Initial investigation of an encoder-decoder end-to-end TTS framework using marginalization of monotonic hard latent alignments
The advantages of our approach are that we can simplify many modules for the soft attention and that we can train the end-to-end TTS model using a single likelihood function.
TIGEr: Text-to-Image Grounding for Image Caption Evaluation
This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems.
Detecting Deep Neural Network Defects with Data Flow Analysis
Deep neural networks (DNNs) are shown to be promising solutions in many challenging artificial intelligence tasks.
Data Sanity Check for Deep Learning Systems via Learnt Assertions
But the statistical nature of DL makes it quite vulnerable to invalid inputs, i. e., those cases that are not considered in the training phase of a DL model.
Reject Illegal Inputs: Scaling Generative Classifiers with Supervised Deep Infomax
The supervised probabilistic constraints are equivalent to a generative classifier on high-level data representations, where class conditional log-likelihoods of samples can be evaluated.
Exploring the Correlation between Likelihood of Flow-based Generative Models and Image Semantics
In this paper, we explore the correlation between flows' likelihood and image semantics.
Generalized Natural Language Grounded Navigation via Environment-agnostic Multitask Learning
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e. g., following natural language instructions or dialog.
Multi-modal Deep Analysis for Multimedia
To address the two scientific problems, we investigate them from the following aspects: 1) multi-modal correlational representation: multi-modal fusion of data across different modalities, and 2) multi-modal data and knowledge fusion: multi-modal fusion of data with domain knowledge.
Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings
While speaker adaptation for end-to-end speech synthesis using speaker embeddings can produce good speaker similarity for speakers seen during training, there remains a gap for zero-shot adaptation to unseen speakers.
Audio and Speech Processing
Cross-Channel Intragroup Sparsity Neural Network
Modern deep neural networks rely on overparameterization to achieve state-of-the-art generalization.
Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention
Despite detection of suicidal ideation on social media has made great progress in recent years, people's implicitly and anti-real contrarily expressed posts still remain as an obstacle, constraining the detectors to acquire higher satisfactory performance.
Transferring neural speech waveform synthesizers to musical instrument sounds generation
Recent neural waveform synthesizers such as WaveNet, WaveGlow, and the neural-source-filter (NSF) model have shown good performance in speech synthesis despite their different methods of waveform generation.
Effect of choice of probability distribution, randomness, and search methods for alignment modeling in sequence-to-sequence text-to-speech synthesis using hard alignment
Sequence-to-sequence text-to-speech (TTS) is dominated by soft-attention-based methods.
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.
Transformation of low-quality device-recorded speech to high-quality speech using improved SEGAN model
Nowadays vast amounts of speech data are recorded from low-quality recorder devices such as smartphones, tablets, laptops, and medium-quality microphones.
Sound Audio and Speech Processing
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation
Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e. g., television) using only visual observations.
Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning
In this paper, we propose a novel deep learning model called Task Adaptive Activation Network (TAAN) that can automatically learn the optimal network architecture for MTL.
Domain-Aware Dynamic Networks
In this work, we propose a method to improve the model capacity without increasing inference-time complexity.
Speaker detection in the wild: Lessons learned from JSALT 2019
This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios.
Audio and Speech Processing Sound
"How do urban incidents affect traffic speed?" A Deep Graph Convolutional Network for Incident-driven Traffic Speed Prediction
Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate urban traffic incident, spatio-temporal, periodic and context features for traffic speed prediction.
Theme-Matters: Fashion Compatibility Learning via Theme Attention
This model associates themes with the pairwise compatibility with attention, and thus compute the outfit-wise compatibility.
Fully Convolutional Graph Neural Networks using Bipartite Graph Convolutions
Graph neural networks have been adopted in numerous applications ranging from learning relational representations to modeling data on irregular domains such as point clouds, social graphs, and molecular structures.
Deep Learning for Learning Graph Representations
Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years.
Reject Illegal Inputs with Generative Classifier Derived from Any Discriminative Classifier
Experiments on illegal inputs, including adversarial examples, samples with common corruptions, and out-of-distribution~(OOD) samples show that allowed to reject a portion of test samples, SDIM-\emph{logit} significantly improves the performance on the left test sets.
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network
In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images.
Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
Large-scale knowledge graphs (KGs) are shown to become more important in current information systems.
Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA
To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting
We firstly represent only face regions using the latent variable as the domain knowledge and combine it with the non-face parts textures to generate high-quality face images with plausible contents.
Category-wise Attack: Transferable Adversarial Examples for Anchor Free Object Detection
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbations can completely change the classification results.
2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data
Such performance gain is more pronounced with transfer learning or in the case of limited training data.
Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients
In our practical implementation of EWSG, the non-uniform subsampling is performed efficiently via a Metropolis-Hastings chain on the data index, which is coupled to the MCMC algorithm.
Frustratingly Simple Few-Shot Object Detection
Such a simple approach outperforms the meta-learning methods by roughly 2~20 points on current benchmarks and sometimes even doubles the accuracy of the prior methods.
Ranked #17 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation
We design a regularisation technique to regulate the domain adaptation.
Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning
In addition, we conduct additional experiments to show the effectiveness of SALL from the aspects of reliability and interpretability in the context of medical imaging application.
An adaptive neuro-fuzzy model for attitude estimation and 2 control a 3 DOF system
To evaluate the performance of the AN-FIS controller in closed-loop simulation, an ANFIS observer is used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor and data gyro data.
How fine can fine-tuning be? Learning efficient language models
Given a language model pre-trained on massive unlabeled text corpora, only very light supervised fine-tuning is needed to learn a task: the number of fine-tuning steps is typically five orders of magnitude lower than the total parameter count.
Introducing the VoicePrivacy Initiative
The VoicePrivacy initiative aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges.
Self-Supervised Deep Visual Odometry with Online Adaptation
In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner.
Variational quantum Gibbs state preparation with a truncated Taylor series
By performing numerical experiments, we show that shallow parameterized circuits with only one additional qubit can be trained to prepare the Ising chain and spin chain Gibbs states with a fidelity higher than 95%.
Design Choices for X-vector Based Speaker Anonymization
The recently proposed x-vector based anonymization scheme converts any input voice into that of a random pseudo-speaker.
Investigation of learning abilities on linguistic features in sequence-to-sequence text-to-speech synthesis
Our experiments suggest that a) a neural sequence-to-sequence TTS system should have a sufficient number of model parameters to produce high quality speech, b) it should also use a powerful encoder when it takes characters as inputs, and c) the encoder still has a room for improvement and needs to have an improved architecture to learn supra-segmental features more appropriately.
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
Neural Architecture Search (NAS) is just such a revolutionary algorithm, and the related research work is complicated and rich.
More Practical and Adaptive Algorithms for Online Quantum State Learning
This regret bound depends only on the maximum rank $M$ of measurements rather than the number of qubits, which takes advantage of low-rank measurements.
Variational Quantum Singular Value Decomposition
In this work, we propose a variational quantum algorithm for singular value decomposition (VQSVD).
Elliptic Blowup Equations for 6d SCFTs. IV: Matters
Given the recent geometrical classification of 6d $(1, 0)$ SCFTs, a major question is how to compute for this large class their elliptic genera.
High Energy Physics - Theory Mathematical Physics Mathematical Physics
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs
Graph Neural Networks (GNNs) are emerging machine learning models on graphs.
Learning Continuous-Time Dynamics by Stochastic Differential Networks
Learning continuous-time stochastic dynamics is a fundamental and essential problem in modeling sporadic time series, whose observations are irregular and sparse in both time and dimension.
Cooling-Aware Resource Allocation and Load Management for Mobile Edge Computing Systems
Driven by explosive computation demands of Internet of Things (IoT), mobile edge computing (MEC) provides a promising technique to enhance the computation capability for mobile users.
The curious case of developmental BERTology: On sparsity, transfer learning, generalization and the brain
In this essay, we explore a point of intersection between deep learning and neuroscience, through the lens of large language models, transfer learning and network compression.
Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals
Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs.
Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation.
A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph
To solve the missing map problem, which is an issue in many applications , after the tracking is lost, based on monocular visual SLAM, we present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps via the corresponding undirected connected graph.
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards
The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning.
Shortcuts to Adiabaticity for the Quantum Rabi Model: Efficient Generation of Giant Entangled Cat States via Parametric Amplification
We propose a method for the fast generation of nonclassical ground states of the Rabi model in the ultrastrong and deep-strong coupling regimes via the shortcuts-to-adiabatic (STA) dynamics.
Quantum Physics
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs
Results: The area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0. 99 for the semi-supervised model and 0. 87 for the pre-trained models.
Learning Tuple Compatibility for Conditional OutfitRecommendation
Our contributions include: 1) Designing a Mixed Category Attention Net (MCAN) which integrates both fine-grained and coarse category information into recommendation and learns the compatibility among fashion tuples.
Cultural Vocal Bursts Intensity Prediction
Recommendation Systems
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
Disentangled Self-Supervision in Sequential Recommenders
There exist two challenges: i) reconstructing a future sequence containing many behaviors is exponentially harder than reconstructing a single next behavior, which can lead to difficulty in convergence, and ii) the sequence of all future behaviors can involve many intentions, not all of which may be predictable from the sequence of earlier behaviors.
Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation
In this work, we propose a general framework for unsupervised image-to-image translation across multiple domains, which can translate images from domain X to any a domain without requiring direct training between the two domains involved in image translation.
Learning by Minimizing the Sum of Ranked Range
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output.
A Unified Approach to Interpreting and Boosting Adversarial Transferability
We discover and prove the negative correlation between the adversarial transferability and the interaction inside adversarial perturbations.
Holistic Combination of Structural and Textual Code Information for Context based API Recommendation
APIRec-CST is a deep learning model that combines the API usage with the text information in the source code based on an API Context Graph Network and a Code Token Network that simultaneously learn structural and textual features for API recommendation.
End-to-End Text-to-Speech using Latent Duration based on VQ-VAE
Explicit duration modeling is a key to achieving robust and efficient alignment in text-to-speech synthesis (TTS).
SWIPENET: Object detection in noisy underwater images
Moreover, inspired by the human education process that drives the learning from easy to hard concepts, we here propose the CMA training paradigm that first trains a clean detector which is free from the influence of noisy data.
A Survey on Curriculum Learning
We discuss works on curriculum learning within a general CL framework, elaborating on how to design a manually predefined curriculum or an automatic curriculum.
Fast Local Attack: Generating Local Adversarial Examples for Object Detectors
The deep neural network is vulnerable to adversarial examples.
Pretraining Strategies, Waveform Model Choice, and Acoustic Configurations for Multi-Speaker End-to-End Speech Synthesis
We explore pretraining strategies including choice of base corpus with the aim of choosing the best strategy for zero-shot multi-speaker end-to-end synthesis.
Attention Aware Cost Volume Pyramid Based Multi-view Stereo Network for 3D Reconstruction
This strategy estimates the depth map at coarsest level, while the depth maps at finer levels are considered as the upsampled depth map from previous level with pixel-wise depth residual.
Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling
We propose the use of a modified cycle generative adversarial network (CycleGAN) model to bridge the gap between regular and UWF fundus and generate additional UWF fundus images for training.
Sentence Matching with Syntax- and Semantics-Aware BERT
Sentence matching aims to identify the special relationship between two sentences, and plays a key role in many natural language processing tasks.
Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach
This paper proposes a cooperative strategy of connected and automated vehicles (CAVs) longitudinal control for partially connected and automated traffic environment based on deep reinforcement learning (DRL) algorithm, which enhances the string stability of mixed traffic, car following efficiency, and energy efficiency.
Variational Quantum Algorithms for Trace Distance and Fidelity Estimation
A novel variational algorithm for trace distance estimation is then derived from this technique, with the assistance of a single ancillary qubit.
Quantum Physics Information Theory Mathematical Physics Information Theory Mathematical Physics Optimization and Control
VSQL: Variational Shadow Quantum Learning for Classification
Classification of quantum data is essential for quantum machine learning and near-term quantum technologies.
Noise-Assisted Quantum Autoencoder
Based on this understanding, we present a noise-assisted quantum autoencoder algorithm to go beyond the limitations, our model can achieve high recovering fidelity for general input states.
Quantum Physics
Joint Optimization of Trajectory, Propulsion and Thrust Powers for Covert UAV-on-UAV Video Tracking and Surveillance
Autonomous tracking of suspicious unmanned aerial vehicles (UAVs) by legitimate monitoring UAVs (or monitors) can be crucial to public safety and security.
Generalizable control for multiparameter quantum metrology
Here we study the generalizability of optimal control, namely, optimal controls that can be systematically updated across a range of parameters with minimal cost.
Quantum Physics
Detecting and quantifying entanglement on near-term quantum devices
Firstly, it decomposes a positive map into a combination of quantum operations implementable on near-term quantum devices.
Quantum Physics Strongly Correlated Electrons
Intragroup sparsity for efficient inference
This work studies intragroup sparsity, a fine-grained structural constraint on network weight parameters.
TkML-AP: Adversarial Attacks to Top-k Multi-Label Learning
Top-k multi-label learning, which returns the top-k predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine.
Towards A Unified Understanding and Improving of Adversarial Transferability
We discover and prove the negative correlation between the adversarial transferability and the interaction inside adversarial perturbations.
A Marching Cube Algorithm Based on Edge Growth
When only the main contour of the 3D model needs to be organized, the algorithm performs well.
3D Reconstruction
Graphics
Multimodal Gait Recognition for Neurodegenerative Diseases
In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses.
Instance-Aware Predictive Navigation in Multi-Agent Environments
To decide the action at each step, we seek the action sequence that can lead to safe future states based on the prediction module outputs by repeatedly sampling likely action sequences.
The dynamic energy balance in earthquakes expressed by fault surface morphology
The fault surface morphology is the direct result of the microscopic processes near the crack tip or on the frictional interface.
Geophysics
A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric
All the results demonstrate that the re-organized dataset splits and new metric can better monitor the progress in TSGV.
Practical distributed quantum information processing with LOCCNet
Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks.
Explicit Perturbations to the Stabilizer $τ= {\rm i}$ of Modular $A^\prime_5$ Symmetry and Leptonic CP Violation
In a class of neutrino mass models with modular flavor symmetries, it has been observed that CP symmetry is preserved at the fixed point (or stabilizer) of the modulus parameter $\tau = {\rm i}$, whereas significant CP violation emerges within the neighbourhood of this stabilizer.
High Energy Physics - Phenomenology
ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech
The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV).
MetaDelta: A Meta-Learning System for Few-shot Image Classification
Meta-learning aims at learning quickly on novel tasks with limited data by transferring generic experience learned from previous tasks.
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
In this paper, we systematically discuss and define the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from wrong diagnosis record.
Automated Machine Learning on Graphs: A Survey
Machine learning on graphs has been extensively studied in both academic and industry.
A Hybrid Quantum-Classical Hamiltonian Learning Algorithm
Hamiltonian learning is crucial to the certification of quantum devices and quantum simulators.
Infrastructure Assisted Constrained Connected Automated Vehicle Trajectory Optimization on Curved Roads: A Spatial Formulation on a Curvilinear Coordinate
This paper presents an infrastructure assisted constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads.
A Unified Game-Theoretic Interpretation of Adversarial Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection
A great deal of recent research effort on speech spoofing countermeasures has been invested into back-end neural networks and training criteria.
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different?
Online Learning of a Probabilistic and Adaptive Scene Representation
Constructing and maintaining a consistent scene model on-the-fly is the core task for online spatial perception, interpretation, and action.
Attention Back-end for Automatic Speaker Verification with Multiple Enrollment Utterances
Probabilistic linear discriminant analysis (PLDA) or cosine similarity have been widely used in traditional speaker verification systems as back-end techniques to measure pairwise similarities.
Ranked #1 on
Speaker Verification
on CN-CELEB
Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting
In this paper, we propose a multi-scale context aggregation network (MSCANet) based on single-column encoder-decoder architecture for crowd counting, which consists of an encoder based on a dense context-aware module (DCAM) and a hierarchical attention-guided decoder.
An Initial Investigation for Detecting Partially Spoofed Audio
By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, which will likely degrade the performance of countermeasures trained with entirely spoofed utterances.
AutoGL: A Library for Automated Graph Learning
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
Robust Object Detection via Instance-Level Temporal Cycle Confusion
Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition
For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however with more than 30+ conditions are very rare from the global patient cohorts, which results in a typical long-tailed learning problem for deep learning-based screening models.
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge
We investigate the theoretical connections between graph signal processing and graph embedding models and formulate the graph embedding model as a general graph signal process with a corresponding graph filter.
A Multi-Level Attention Model for Evidence-Based Fact Checking
Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources.
Imperceptible Adversarial Examples for Fake Image Detection
Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society.
Transferable Adversarial Examples for Anchor Free Object Detection
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result.
Visual Question Rewriting for Increasing Response Rate
Offline experiments and mechanical Turk based evaluations show that it is possible to rewrite bland questions in a more detailed and attractive way to increase the response rate, and images can be helpful.
Sum of Ranked Range Loss for Supervised Learning
A combination loss of AoRR and TKML is proposed as a new learning objective for improving the robustness of multi-label learning in the face of outliers in sample and labels alike.
DETReg: Unsupervised Pretraining with Region Priors for Object Detection
Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture.
Ranked #1 on
Few-Shot Object Detection
on COCO 2017
Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing
Whether it be for results summarization, or the analysis of classifier fusion, some means to compare different classifiers can often provide illuminating insight into their behaviour, (dis)similarity or complementarity.
Medical Matting: A New Perspective on Medical Segmentation with Uncertainty
None of them investigate the influence of the ambiguous nature of the lesion itself. Inspired by image matting, this paper introduces alpha matte as a soft mask to represent uncertain areas in medical scenes and accordingly puts forward a new uncertainty quantification method to fill the gap of uncertainty research for lesion structure.
