Search Results for author:
Found 528 papers, 161 papers with code
Correlative Multi-Label Multi-Instance Image Annotation
The cross-level label coherence en-codes the consistency between the labels at the image leveland the labels at the region level.
Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering.
Ranked #1 on
Image Clustering
on Coil-20
(Accuracy metric)
Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis
Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene.
Supervised hashing with latent factor models
Very recently, supervised hashing methods, which try to preserve the semantic structure constructed from the semantic labels of the training points, have exhibited higher accuracy than unsupervised methods.
Conditional Restricted Boltzmann Machines for Cold Start Recommendations
Restricted Boltzman Machines (RBMs) have been successfully used in recommender systems.
Weakly Supervised Semantic Segmentation for Social Images
Image semantic segmentation is the task of partitioning image into several regions based on semantic concepts.
Exploring Metaphorical Senses and Word Representations for Identifying Metonyms
A metonym is a word with a figurative meaning, similar to a metaphor.
Recognizing Extended Spatiotemporal Expressions by Actively Trained Average Perceptron Ensembles
We collected and annotated data set by querying commercial web searches API with such spatiotemporal expressions as were missed by state-of-the- art parsers.
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks.
Structured Memory for Neural Turing Machines
Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning.
Empirical Study on Deep Learning Models for Question Answering
In this paper we explore deep learning models with memory component or attention mechanism for question answering task.
Staleness-aware Async-SGD for Distributed Deep Learning
Deep neural networks have been shown to achieve state-of-the-art performance in several machine learning tasks.
A Spatio-Temporal Appearance Representation for Viceo-Based Pedestrian Re-Identification
Pedestrian re-identification is a difficult problem due to the large variations in a person's appearance caused by different poses and viewpoints, illumination changes, and occlusions.
Multiple Granularity Descriptors for Fine-Grained Categorization
Fine-grained categorization, which aims to distinguish subordinate-level categories such as bird species or dog breeds, is an extremely challenging task.
Model-based Deep Hand Pose Estimation
For the first time, we show that embedding such a non-linear generative process in deep learning is feasible for hand pose estimation.
A Machine Learning Nowcasting Method based on Real-time Reanalysis Data
Despite marked progress over the past several decades, convective storm nowcasting remains a challenge because most nowcasting systems are based on linear extrapolation of radar reflectivity without much consideration for other meteorological fields.
Deep Kinematic Pose Regression
In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation.
Ranked #307 on
3D Human Pose Estimation
on Human3.6M
Integrating Topic Models and Latent Factors for Recommendation
In this work, we consider the problem of hotel recommendation for travel planning services by integrating the location information and the user's preference for recommendation.
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions.
Ranked #49 on
Question Answering
on SQuAD1.1 dev
GaDei: On Scale-up Training As A Service For Deep Learning
By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e. g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers.
PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning
To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately.
Application of Multi-channel 3D-cube Successive Convolution Network for Convective Storm Nowcasting
This paper describes the first attempt to nowcast storm initiation, growth, and advection simultaneously under a deep learning framework using multi-source meteorological data.
Learning Compact Appearance Representation for Video-based Person Re-Identification
This paper presents a novel approach for video-based person re-identification using multiple Convolutional Neural Networks (CNNs).
Ergodic SDEs on submanifolds and related numerical sampling schemes
By Birkhoff's ergodic theorem, one approach to estimate the mean value is to compute the time average along an infinitely long trajectory of an ergodic diffusion process on the level set whose invariant measure is {\mu}.
Probability 60J60, 53C17
Low-rank Label Propagation for Semi-supervised Learning with 100 Millions Samples
The success of semi-supervised learning crucially relies on the scalability to a huge amount of unlabelled data that are needed to capture the underlying manifold structure for better classification.
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
On network configurations with low bandwidth or high latency, D-PSGD can be up to one order of magnitude faster than its well-optimized centralized counterparts.
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition
In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task spaces), e. g., such base deep CNNs are trained to recognize different subsets of tens of thousands of atomic object classes.
Binarized Mode Seeking for Scalable Visual Pattern Discovery
Second, we further extend bMS to a more general form, namely contrastive binary mean shift (cbMS), which maximizes the contrastive density in binary space, for finding informative patterns that are both frequent and discriminative for the dataset.
Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition
Our LMM model can provide an end-to-end approach for jointly learning: (a) the deep networks to extract more discriminative deep features for image and object class representation; (b) the tree classifier for recognizing large numbers of object classes hierarchically; and (c) the visual hierarchy adaptation for achieving more accurate indexing of large numbers of object classes hierarchically.
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
Second, we propose a novel method that jointly trains the Ranker along with an answer-generation Reader model, based on reinforcement learning.
Ranked #4 on
Open-Domain Question Answering
on Quasar
Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
Learning to remember long sequences remains a challenging task for recurrent neural networks.
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Can we design an algorithm that is robust in a heterogeneous environment, while being communication efficient and maintaining the best-possible convergence rate?
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer.
Ranked #1 on
Open-Domain Question Answering
on Quasar
Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering
Existing VQA methods mainly adopt the visual attention mechanism to associate the input question with corresponding image regions for effective question answering.
DeepSkeleton: Skeleton Map for 3D Human Pose Regression
For the first time, we show that training regression network from skeleton map alone is capable of meeting the performance of state-of-theart 3D human pose estimation works.
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach.
Ranked #36 on
Action Recognition
on UCF101
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training
Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100 of TeraOps/s of computational capacity) is expected to be severely communication constrained.
Deep Boosting of Diverse Experts
In this paper, a deep boosting algorithm is developed to learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs (base experts) with diverse capabilities, e. g., these base deep CNNs are sequentially trained to recognize a set of object classes in an easy-to-hard way according to their learning complexities.
Fake Colorized Image Detection
Based on our observations, i. e., potential traces in the hue, saturation, dark and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram based Fake Colorized Image Detection (FCID-HIST) and Feature Encoding based Fake Colorized Image Detection (FCID-FE).
Multimedia
SEE: Syntax-aware Entity Embedding for Neural Relation Extraction
First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU.
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.
Chinese Named Entity Recognition
named-entity-recognition
+2
Knowledge Base Relation Detection via Multi-View Matching
Relation detection is a core component for Knowledge Base Question Answering (KBQA).
Reconstruction Network for Video Captioning
Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstruction network (RecNet) with a novel encoder-decoder-reconstructor architecture, which leverages both the forward (video to sentence) and backward (sentence to video) flows for video captioning.
Boosting up Scene Text Detectors with Guided CNN
Deep CNNs have achieved great success in text detection.
Saikosaponins with similar structures but different mechanisms lead to combined hepatotoxicity
Previous studies have revealed that saikosaponins are the major types of components that contribute to the hepatotoxicity of Radix Bupleuri.
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering
To better utilize semantic knowledge in images, we propose a novel framework to learn visual relation facts for VQA.
Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis.
Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation
Prior relevant studies recommend treatments either use supervised learning (e. g. matching the indicator signal which denotes doctor prescriptions), or reinforcement learning (e. g. maximizing evaluation signal which indicates cumulative reward from survival rates).
Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining
Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks.
Fast Video Shot Transition Localization with Deep Structured Models
In order to train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos.
Ranked #3 on
Camera shot boundary detection
on ClipShots
(using extra training data)
Temporal Sequence Distillation: Towards Few-Frame Action Recognition in Videos
Secondly, TSD significantly reduces the computations to run video action recognition with compressed frames on the cloud, while maintaining high recognition accuracies.
Heated-Up Softmax Embedding
Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples.
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding
This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly.
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
We propose a population-based Evolutionary Stochastic Gradient Descent (ESGD) framework for optimizing deep neural networks.
Solving Pictorial Jigsaw Puzzle by Stigmergy-inspired Internet-based Human Collective Intelligence
And through feedback, each player is provided with personalized feedback information based on the current COG and the player's exploration result, in order to accelerate his/her puzzle-solving process.
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition
Video action recognition, a critical problem in video understanding, has been gaining increasing attention.
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Reinforcement learning agents need exploratory behaviors to escape from local optima.
Dynamic Graph Representation Learning via Self-Attention Networks
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization.
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.
Learning Efficient Detector with Semi-supervised Adaptive Distillation
ADL enlarges the distillation loss for hard-to-learn and hard-to-mimic samples and reduces distillation loss for the dominant easy samples, enabling distillation to work on the single-stage detector first time, even if the student and the teacher are identical.
Edge-Semantic Learning Strategy for Layout Estimation in Indoor Environment
More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image.
MSR: Multi-Scale Shape Regression for Scene Text Detection
State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes.
VrR-VG: Refocusing Visually-Relevant Relationships
Relationships encode the interactions among individual instances, and play a critical role in deep visual scene understanding.
Hierarchical Photo-Scene Encoder for Album Storytelling
In this paper, we propose a novel model with a hierarchical photo-scene encoder and a reconstructor for the task of album storytelling.
Ranked #5 on
Image-guided Story Ending Generation
on VIST-E
Learning chemical reaction networks from trajectory data
We develop a data-driven method to learn chemical reaction networks from trajectory data.
Optimization and Control 92C42, 62M86
From Dark Matter to Galaxies with Convolutional Networks
In combination with current and upcoming data from cosmological observations, our method has the potential to answer fundamental questions about our Universe with the highest accuracy.
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
This paper presents a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images.
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Here, the challenge is to learn accurate "few-shot" models for classes existing at the tail of the class distribution, for which little data is available.
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.
Unsupervised Person Image Generation with Semantic Parsing Transformation
Firstly, a semantic generative network is proposed to transform between semantic parsing maps, in order to simplify the non-rigid deformation learning.
Distributed Deep Learning Strategies For Automatic Speech Recognition
We show that we can train the LSTM model using ADPSGD in 14 hours with 16 NVIDIA P100 GPUs to reach a 7. 6% WER on the Hub5- 2000 Switchboard (SWB) test set and a 13. 1% WER on the CallHome (CH) test set.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Novel Hybrid Sequential Model for Review-based Rating Prediction
Nowadays, the online interactions between users and items become diverse, and may include textual reviews as well as numerical ratings.
Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks
Selfie and cartoon are two popular artistic forms that are widely presented in our daily life.
Learning to Decompose Compound Questions with Reinforcement Learning
Our model consists of two parts: (i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and (ii) three independent simple-question answerers that classify the corresponding relations for each simple question.
Anti-Confusing: Region-Aware Network for Human Pose Estimation
In this work, we propose a novel framework named Region-Aware Network (RANet), which learns the ability of anti-confusing in case of heavy occlusion, nearby person and symmetric appearance, for human pose estimation.
Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesis
Early and accurate congestion estimation is of great benefit to guide the optimization in HLS and improve the efficiency of implementation.
Accelerated Target Updates for Q-learning
This paper studies accelerations in Q-learning algorithms.
Predictive Ensemble Learning with Application to Scene Text Detection
Taking scene text detection as the application, where no suitable ensemble learning strategy exists, PEL can significantly improve the performance, compared to either individual state-of-the-art models, or the fusion of multiple models by non-maximum suppression.
Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning
Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstruction network (RecNet) in a novel encoder-decoder-reconstructor architecture, which leverages both forward (video to sentence) and backward (sentence to video) flows for video captioning.
Knowledge-Aware Deep Dual Networks for Text-Based Mortality Prediction
To address the above issues, we propose novel Knowledge-aware Deep Dual Networks (K-DDN) for the text-based mortality prediction task.
A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition
On commonly used public SWB-300 and SWB-2000 ASR datasets, ADPSGD can converge with a batch size 3X as large as the one used in SSGD, thus enable training at a much larger scale.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition
In automatic speech recognition (ASR), wideband (WB) and narrowband (NB) speech signals with different sampling rates typically use separate acoustic models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-Granular Text Encoding for Self-Explaining Categorization
Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence.
Latency Minimization for Multiuser Computation Offloading in Fog-Radio Access Networks
This paper considers computation offloading in fog-radio access networks (F-RAN), where multiple user equipments (UEs) offload their computation tasks to the F-RAN through a number of fog nodes.
Hard-Aware Fashion Attribute Classification
For "hard" attributes with insufficient training data, Deact brings more stable synthetic samples for training and further improve the performance.
Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation
To tackle the unsupervised domain adaptation problem, we explore the possibilities to generate high-quality labels as proxy labels to supervise the training on target data.
Road Context-aware Intrusion Detection System for Autonomous Cars
With such ongoing road context, RAIDS validates corresponding frames observed on the in-vehicle network.
Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning
However, the human annotation is expensive, while human-crafted patterns suffer from semantic drift and distant supervision samples are usually noisy.
Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network
In this paper, we propose to guide the video caption generation with Part-of-Speech (POS) information, based on a gated fusion of multiple representations of input videos.
Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation
In our framework, a spatial constraint module is designed to fit reasonable scaling and spatial layout of object pairs with considering relationship between them.
Long-distance distribution of atom-photon entanglement at telecom wavelength
Here we report on the generation and observation of entanglement between a Rb-87 atom and a photon at telecom wavelength over 20 km optical fiber.
Quantum Physics
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples.
Learning Actions from Human Demonstration Video for Robotic Manipulation
However, the general video captioning methods focus more on the understanding of the full frame, lacking of consideration on the specific object of interests in robotic manipulations.
AI Matrix: A Deep Learning Benchmark for Alibaba Data Centers
Alibaba has China's largest e-commerce platform.
CAN ALTQ LEARN FASTER: EXPERIMENTS AND THEORY
Differently from the popular Deep Q-Network (DQN) learning, Alternating Q-learning (AltQ) does not fully fit a target Q-function at each iteration, and is generally known to be unstable and inefficient.
A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting
Very short-term convective storm forecasting, termed nowcasting, has long been an important issue and has attracted substantial interest.
Hybrid Zero Dynamics Inspired Feedback Control Policy Design for 3D Bipedal Locomotion using Reinforcement Learning
This paper presents a novel model-free reinforcement learning (RL) framework to design feedback control policies for 3D bipedal walking.
Learning Robust Representations with Graph Denoising Policy Network
Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction.
Constrained Non-Affine Alignment of Embeddings
Embeddings are already essential tools for large language models and image analysis, and their use is being extended to many other research domains.
From Dark Matter to Galaxies with Convolutional Neural Networks
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations.
History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms
In this paper, we propose a novel scheme, which eliminates backtracking line search but still exploits the information along optimization path by adapting the batch size via history stochastic gradients.
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection
Differing from vanilla prototypical networks simply computing event prototypes by averaging, which only consume event mentions once, our model is more robust and is capable of distilling contextual information from event mentions for multiple times due to the multi-hop mechanism of DMNs.
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Despite recent progress on decentralized algorithms for training deep neural networks, it remains unclear whether it is possible to train GANs in a decentralized manner.
SubCharacter Chinese-English Neural Machine Translation with Wubi encoding
However, when the translation task involves Chinese, semantic granularity remains at the word and character level, so there is still need more fine-grained translation model of Chinese.
Relation Adversarial Network for Low Resource Knowledge Graph Completion
Specifically, the framework takes advantage of a relation discriminator to distinguish between samples from different relations, and help learn relation-invariant features more transferable from source relations to target relations.
Language Model-Driven Unsupervised Neural Machine Translation
We present the detailed mathematical construction of our method.
Convolutional Neural Network for Convective Storm Nowcasting Using 3D Doppler Weather Radar Data
On the first layer of CNN, a cross-channel 3D convolution was proposed to fuse 3D raw data.
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures.
Ranked #29 on
Entity Alignment
on DBP15k zh-en
Furnishing Your Room by What You See: An End-to-End Furniture Set Retrieval Framework with Rich Annotated Benchmark Dataset
However, few works focus on the understanding of furniture within the scenes and a large-scale dataset is also lacked to advance the field.
SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection
In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection.
Learning Efficient Video Representation with Video Shuffle Networks
To this end, we propose the video shuffle, a parameter-free plug-in component that efficiently reallocates the inputs of 2D convolution so that its receptive field can be extended to the temporal dimension.
Progressive Retinex: Mutually Reinforced Illumination-Noise Perception Network for Low Light Image Enhancement
Contrast enhancement and noise removal are coupled problems for low-light image enhancement.
Improving Neural Relation Extraction with Positive and Unlabeled Learning
We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning.
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks
Reducing the numerical precision of data and computation is extremely effective in accelerating deep learning training workloads.
AutoBlock: A Hands-off Blocking Framework for Entity Matching
Entity matching seeks to identify data records over one or multiple data sources that refer to the same real-world entity.
Attentive Representation Learning with Adversarial Training for Short Text Clustering
Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model.
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization
In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure.
Zooming into Face Forensics: A Pixel-level Analysis
The stunning progress in face manipulation methods has made it possible to synthesize realistic fake face images, which poses potential threats to our society.
Down to the Last Detail: Virtual Try-on with Detail Carving
However, existing methods can hardly preserve the details in clothing texture and facial identity (face, hair) while fitting novel clothes and poses onto a person.
Vision and Language: from Visual Perception to Content Creation
The real-world deployment or services of vision and language are elaborated as well.
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets
Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \emph{improved} adaptive complexity $O\left(\epsilon^{-\frac{2}{1-\alpha}}\right)$, where $\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq \alpha\leq 1/2$.
A Two-step Calibration Method for Unfocused Light Field Camera Based on Projection Model Analysis
The proposed method is able to reuse traditional camera calibration methods for the direction parameter set.
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization
Therefore many modified normalization techniques have been proposed, which either fail to restore the performance of BN completely, or have to introduce additional nonlinear operations in inference procedure and increase huge consumption.
How Does BN Increase Collapsed Neural Network Filters?
This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result.
Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning
To improve domain-adapted sentiment classification by learning sentiment from the target domain as well, we devise a novel deep adversarial mutual learning approach involving two groups of feature extractors, domain discriminators, sentiment classifiers, and label probers.
Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption
Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users’ writing style and traits, and is more practical to meet users’ real demands.
Improving Efficiency in Large-Scale Decentralized Distributed Training
Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks.
Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering
Such a method should scale up well, model the heterogeneous noise, and address the communication issue in a distributed system.
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling
Despite the wide applications of Adam in reinforcement learning (RL), the theoretical convergence of Adam-type RL algorithms has not been established.
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences.
Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
Specifically, we build a Multi-Relational Item Graph (MRIG) based on all behavior sequences from all sessions, involving target and auxiliary behavior types.
Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network
Given a set of symptoms to treat, we aim to generate an overall syndrome representation by effectively fusing the embeddings of all the symptoms in the set, to mimic how a doctor induces the syndromes.
Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition
The past decade has witnessed great progress in Automatic Speech Recognition (ASR) due to advances in deep learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection
The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes.
Sex Differences in Severity and Mortality Among Patients With COVID-19: Evidence from Pooled Literature Analysis and Insights from Integrated Bioinformatic Analysis
Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences.
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
Ranked #17 on
Fine-Grained Image Classification
on CUB-200-2011
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
Specifically, the fractional dilated kernel is adaptively constructed according to the image aspect ratios, where the interpolation of nearest two integers dilated kernels is used to cope with the misalignment of fractional sampling.
Learning from a Lightweight Teacher for Efficient Knowledge Distillation
Knowledge Distillation (KD) is an effective framework for compressing deep learning models, realized by a student-teacher paradigm requiring small student networks to mimic the soft target generated by well-trained teachers.
Map Generation from Large Scale Incomplete and Inaccurate Data Labels
Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc..
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
To achieve an extremely fast NAS while preserving the high accuracy, we propose to identify the vital blocks and make them the priority in the architecture search.
GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation
We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together.
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications
To reduce the design effort and achieve the right balance, we propose FP-Stereo for building high-performance stereo matching pipelines on FPGAs automatically.
PointTrack++ for Effective Online Multi-Object Tracking and Segmentation
In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.
Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods including 3D tracking methods by large margins (5. 4% higher MOTSA and 18 times faster over MOTSFusion) with the near real-time speed (22 FPS).
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+1
Multi-future Merchant Transaction Prediction
We use experiments on real-world merchant transaction data to demonstrate the effectiveness of our proposed model.
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent
In this paper, we first characterize the convergence rate for Q-AMSGrad, which is the Q-learning algorithm with AMSGrad update (a commonly adopted alternative of Adam for theoretical analysis).
Automatic Image Labelling at Pixel Level
A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from a source domain, and such GFN then transfers such segmentation knowledge to generate coarse object masks in the target domain.
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains.
Ranked #15 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending
In this paper, we propose a novel lane-sensitive architecture search framework named CurveLane-NAS to automatically capture both long-ranged coherent and accurate short-range curve information while unifying both architecture search and post-processing on curve lane predictions via point blending.
Ranked #12 on
Lane Detection
on CurveLanes
Multi-stream RNN for Merchant Transaction Prediction
New challenges have surfaced in monitoring and guaranteeing the integrity of payment processing systems.
Research Progress of Convolutional Neural Network and its Application in Object Detection
With the improvement of computer performance and the increase of data volume, the object detection based on convolutional neural network (CNN) has become the main algorithm for object detection.
Momentum Q-learning with Finite-Sample Convergence Guarantee
For the infinite state-action space case, we establish the convergence guarantee for MomentumQ with linear function approximations and Markovian sampling.
Pneumonia after bacterial or viral infection preceded or followed by radiation exposure -- a reanalysis of older radiobiological data and implications for low dose radiotherapy for COVID-19 pneumonia
For 7 studies that evaluated post-inoculation radiation exposure (more relevant to LDRT for COVID-19 pneumonia) the results are heterogeneous, with 2 studies showing a significant increase (p<0. 001) and another showing a significant decrease (p<0. 001) in mortality associated with radiation exposure.
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes
The task of room layout estimation is to locate the wall-floor, wall-ceiling, and wall-wall boundaries.
Cluster-level Feature Alignment for Person Re-identification
Instance-level alignment is widely exploited for person re-identification, e. g. spatial alignment, latent semantic alignment and triplet alignment.
Ranked #30 on
Person Re-Identification
on DukeMTMC-reID
A(DP)$^2$SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy
A popular distributed learning strategy is federated learning, where there is a central server storing the global model and a set of local computing nodes updating the model parameters with their corresponding data.
Products-10K: A Large-scale Product Recognition Dataset
With the rapid development of electronic commerce, the way of shopping has experienced a revolutionary evolution.
Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator
Extensive experiments are conducted to demonstrate the accuracy of the proposed imitating learning process as well as the reliability of the holistic system for autonomous drone navigation.
An Ensemble Learning Approach for In-situ Monitoring of FPGA Dynamic Power
As field-programmable gate arrays become prevalent in critical application domains, their power consumption is of high concern.
Decision Tree Based Hardware Power Monitoring for Run Time Dynamic Power Management in FPGA
A flexible architecture of the hardware power monitoring is proposed, which can be instrumented in any RTL design for runtime power estimation, dispensing with the need for extra power measurement devices.
Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA
We further present a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques.
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning
In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.
Towards a Flexible Embedding Learning Framework
Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database.
TernaryBERT: Distillation-aware Ultra-low Bit BERT
Transformer-based pre-training models like BERT have achieved remarkable performance in many natural language processing tasks. However, these models are both computation and memory expensive, hindering their deployment to resource-constrained devices.
Kernel Based Progressive Distillation for Adder Neural Networks
A convolutional neural network (CNN) with the same architecture is simultaneously initialized and trained as a teacher network, features and weights of ANN and CNN will be transformed to a new space to eliminate the accuracy drop.
Finite-Time Analysis for Double Q-learning
Although Q-learning is one of the most successful algorithms for finding the best action-value function (and thus the optimal policy) in reinforcement learning, its implementation often suffers from large overestimation of Q-function values incurred by random sampling.
OpenUE: An Open Toolkit of Universal Extraction from Text
We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.
Ranked #3 on
Joint Entity and Relation Extraction
on WebNLG
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph
On the one hand, sparse KGs contain less information, which makes it difficult for the model to choose correct paths.
Knowledge Association with Hyperbolic Knowledge Graph Embeddings
Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations.
Ranked #28 on
Entity Alignment
on DBP15k zh-en
How Can Self-Attention Networks Recognize Dyck-n Languages?
For $\mathcal{D}_2$, we find that SA$^-$ completely breaks down on long sequences whereas the accuracy of SA$^+$ is 58. 82$\%$.
G-DARTS-A: Groups of Channel Parallel Sampling with Attention
Differentiable Architecture Search (DARTS) provides a baseline for searching effective network architectures based gradient, but it is accompanied by huge computational overhead in searching and training network architecture.
Diversity in immunogenomics: the value and the challenge
With the advent of high-throughput sequencing technologies, the fields of immunogenomics and adaptive immune receptor repertoire research are facing both opportunities and challenges.
Learn to Navigate Maplessly with Varied LiDAR Configurations: A Support Point-Based Approach
Deep reinforcement learning (DRL) demonstrates great potential in mapless navigation domain.
Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint.
Ranked #695 on
Image Classification
on ImageNet
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
The experimental results show that the ranking lists of the comparison systems on the DS-labelled test data and human-annotated test data are different.
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings.
Summarizing Chinese Medical Answer with Graph Convolution Networks and Question-focused Dual Attention
It is desirable to generate answer summaries for online search engines, particularly summaries that can reveal direct answers to questions.
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models.
Merchant Category Identification Using Credit Card Transactions
In this work, we approach this problem from a multi-modal learning perspective, where we use not only the merchant time series data but also the information of merchant-merchant relationship (i. e., affinity) to verify the self-reported business type (i. e., merchant category) of a given merchant.
Adam$^+$: A Stochastic Method with Adaptive Variance Reduction
As a result, Adam$^+$ requires few parameter tuning, as Adam, but it enjoys a provable convergence guarantee.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Improving Relation Extraction with Relational Paraphrase Sentences
In this paper, we propose an alternative approach to improve RE systems via enriching diverse expressions by relational paraphrase sentences.
Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets
To this end, we summarize a tiny formula for downsizing neural architectures through a series of smaller models derived from the EfficientNet-B0 with the FLOPs constraint.
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension
Most existing RC models are developed on formal datasets such as news articles and Wikipedia documents, which severely limit their performances when directly applied to the noisy and informal texts in social media.
Online Decision Based Visual Tracking via Reinforcement Learning
A deep visual tracker is typically based on either object detection or template matching while each of them is only suitable for a particular group of scenes.
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
In particular, we propose a novel joint-training framework to train plain CNN by leveraging the gradients of the ResNet counterpart.
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning
Human judgments of word similarity have been a popular method of evaluating the quality of word embedding.
Hard-ODT: Hardware-Friendly Online Decision Tree Learning Algorithm and System
Following this, we present Hard-ODT, a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques.
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
In this paper, we will share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game.
Anomalous Hall and Nernst Effects in FeRh
Antiferromagnets with tunable phase transitions are promising for future spintronics applications.
Materials Science
BinaryBERT: Pushing the Limit of BERT Quantization
In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization.
RRL: A Scalable Classifier for Interpretable Rule-Based Representation Learning
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection
Here we present a novel self-supervised 3D Object detection framework that seamlessly integrates the geometry-aware contrast and clustering harmonization to lift the unsupervised 3D representation learning, named GCC-3D.
Double Q-learning: New Analysis and Sharper Finite-time Bound
Double Q-learning (Hasselt 2010) has gained significant success in practice due to its effectiveness in overcoming the overestimation issue of Q-learning.
Why Does Decentralized Training Outperform Synchronous Training In The Large Batch Setting?
We conduct extensive studies over 12 state-of-the-art DL models/tasks and demonstrate that DPSGD consistently outperforms SSGD in the large batch setting; and DPSGD converges in cases where SSGD diverges for large learning rates.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing
The semi-supervised semantic segmentation methods utilize the unlabeled data to increase the feature discriminative ability to alleviate the burden of the annotated data.
Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification
By studying two unsupervised person re-ID methods in a cross-method way, we point out a hard negative problem is handled implicitly by their designs of data augmentations and PK sampler respectively.
