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Found 107 papers, 29 papers with code
Improving Abstractive Dialogue Summarization with Hierarchical Pretraining and Topic Segment
With the increasing abundance of meeting transcripts, meeting summary has attracted more and more attention from researchers.
MABNet: A Lightweight Stereo Network Based on Multibranch Adjustable Bottleneck Module
MABNet is based on a novel Multibranch Adjustable Bottleneck (MAB) module, which is less demanding on parameters and computation.
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification
To this end, we propose a novel Diversified Multiple Instance Learning Network (D-MILN), which is able to achieve aspect-level sentiment classification with only document-level weak supervision.
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Feature selection and instance selection are two important techniques of data processing.
Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices
In this paper, a binarized convolutional neural network suitable for ECG monitoring is proposed, which is hardware-friendly and more suitable for use in resource-constrained wearable devices.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
The Devil is in the Frequency: Geminated Gestalt Autoencoder for Self-Supervised Visual Pre-Training
The self-supervised Masked Image Modeling (MIM) schema, following "mask-and-reconstruct" pipeline of recovering contents from masked image, has recently captured the increasing interest in the multimedia community, owing to the excellent ability of learning visual representation from unlabeled data.
Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network
Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general.
Knowledge Mining with Scene Text for Fine-Grained Recognition
Recently, the semantics of scene text has been proven to be essential in fine-grained image classification.
Joint Covering Congestion Rents in Multi-area Power Systems Considering Loop Flow Effects
We consider the problem of how multiple areas should jointly cover congestion rents of internal and tie-lines in an interconnected power system.
Elastica Models for Color Image Regularization
Recently, the authors proposed a color elastica model, which minimizes both the surface area and elastica of the image manifold.
UNIMO-2: End-to-End Unified Vision-Language Grounded Learning
In particular, we propose to conduct grounded learning on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the visual and textual semantic spaces on different types of corpora.
PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling
We propose a generative adversarial network for point cloud upsampling, which can not only make the upsampled points evenly distributed on the underlying surface but also efficiently generate clean high frequency regions.
Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization
In order to adapt to our compression method, we need a smaller and simpler network.
Network resilience in the aging brain
Degeneration and adaptation are two competing sides of the same coin called resilience in the progressive processes of brain aging or diseases.
CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery
We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vectors.
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning
In this work, we propose Exploratory data for Offline RL (ExORL), a data-centric approach to offline RL.
Continual Learning with Recursive Gradient Optimization
Learning multiple tasks sequentially without forgetting previous knowledge, called Continual Learning(CL), remains a long-standing challenge for neural networks.
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces
Learning operators between infinitely dimensional spaces is an important learning task arising in wide applications in machine learning, imaging science, mathematical modeling and simulations, etc.
Learning to Walk with Dual Agents for Knowledge Graph Reasoning
Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge graph (KG) by exploring multi-hop relational paths.
Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment
Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.
Generalized DataWeighting via Class-Level Gradient Manipulation
To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.
Neural Collaborative Graph Machines for Table Structure Recognition
We also show that the proposed NCGM can modulate collaborative pattern of different modalities conditioned on the context of intra-modality cues, which is vital for diversified table cases.
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark
Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications.
NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition
For the sake of trade-off between efficiency and performance, a group of works merely perform SA operation within local patches, whereas the global contextual information is abandoned, which would be indispensable for visual recognition tasks.
Enhanced Fast Boolean Matching based on Sensitivity Signatures Pruning
In this paper, we introduce Boolean sensitivity into Boolean matching and design several sensitivity-related signatures to enhance fast Boolean matching.
Generalized Data Weighting via Class-level Gradient Manipulation
To be specific, GDW unrolls the loss gradient to class-level gradients by the chain rule and reweights the flow of each gradient separately.
URLB: Unsupervised Reinforcement Learning Benchmark
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks.
Emoji-aware Co-attention Network with EmoGraph2vec Model for Sentiment Anaylsis
In this work, we propose a method to learn emoji representations called EmoGraph2vec and design an emoji-aware co-attention network that learns the mutual emotional semantics between text and emojis on short texts of social media.
Emoji-based Co-attention Network for Microblog Sentiment Analysis
In this paper, we propose an emoji-based co-attention network that learns the mutual emotional semantics between text and emojis on microblogs.
Spatial-Temporal Transformer for 3D Point Cloud Sequences
We test the effectiveness our PST2 with two different tasks on point cloud sequences, i. e., 4D semantic segmentation and 3D action recognition.
Scaling Fair Learning to Hundreds of Intersectional Groups
In real-world applications, however, there are multiple protected attributes yielding a large number of intersectional protected groups.
There are free lunches
In this paper we propose a novel idea, "There are free lunches" (TAFL) Theorem, which states that some algorithms can achieve the best performance in all possible tasks, in the condition that tasks are given in a specific order.
A Multimodal Sentiment Dataset for Video Recommendation
Based on this, we propose a multimodal sentiment analysis dataset, named baiDu Video Sentiment dataset (DuVideoSenti), and introduce a new sentiment system which is designed to describe the sentimental style of a video on recommendation scenery.
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
Most of existing statistical theories on deep neural networks have sample complexities cursed by the data dimension and therefore cannot well explain the empirical success of deep learning on high-dimensional data.
APS: Active Pretraining with Successor Features
We introduce a new unsupervised pretraining objective for reinforcement learning.
DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation
Therefore, in order to systematically evaluate the factors for building trustworthy systems, we propose a novel and well-annotated sentiment analysis dataset to evaluate robustness and interpretability.
Knowledge accumulating: The general pattern of learning
Artificial Intelligence has been developed for decades with the achievement of great progress.
An Operator-Splitting Method for the Gaussian Curvature Regularization Model with Applications to Surface Smoothing and Imaging
In our method, we decouple the full nonlinearity of Gaussian curvature from differential operators by introducing two matrix- and vector-valued functions.
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal
Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e. g., real estate agents, appraisers, lenders, and buyers).
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose.
JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu
In this work, we present JIZHI - a Model-as-a-Service system - that per second handles hundreds of millions of online inference requests to huge deep models with more than trillions of sparse parameters, for over twenty real-time recommendation services at Baidu, Inc.
Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras
Vehicle Re-identification aims to identify a specific vehicle across time and camera view.
Multi-modal Trajectory Prediction for Autonomous Driving with Semantic Map and Dynamic Graph Attention Network
Predicting future trajectories of surrounding obstacles is a crucial task for autonomous driving cars to achieve a high degree of road safety.
Behavior From the Void: Unsupervised Active Pre-Training
We introduce a new unsupervised pre-training method for reinforcement learning called APT, which stands for Active Pre-Training.
PML: Progressive Margin Loss for Long-tailed Age Classification
In this paper, we propose a progressive margin loss (PML) approach for unconstrained facial age classification.
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning
Electric Vehicle (EV) has become a preferable choice in the modern transportation system due to its environmental and energy sustainability.
Out-of-Town Recommendation with Travel Intention Modeling
Second, a user-specific travel intention is formulated as an aggregation combining home-town preference and generic travel intention together, where the generic travel intention is regarded as a mixture of inherent intentions that can be learned by Neural Topic Model (NTM).
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation
Electric vehicles have been rapidly increasing in usage, but stations to charge them have not always kept up with demand, so efficient routing of vehicles to stations is critical to operating at maximum efficiency.
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments
Thus, we provide a principled approach to tackling the joint problem of causal discovery and latent variable inference.
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix
Nevertheless, we find that the performance of IRM can be dramatically degraded under \emph{strong $\Lambda$ spuriousness} -- when the spurious correlation between the spurious features and the class label is strong due to the strong causal influence of their common cause, the domain label, on both of them (see Fig.
Spatial Object Recommendation with Hints: When Spatial Granularity Matters
We design a novel multi-task learning model called MPR (short for Multi-level POI Recommendation), where each task aims to return the top-k POIs at a certain spatial granularity level.
Unsupervised Active Pre-Training for Reinforcement Learning
On DMControl suite, APT beats all baselines in terms of asymptotic performance and data efficiency and dramatically improves performance on tasks that are extremely difficult for training from scratch.
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
Existed pre-training methods either focus on single-modal tasks or multi-modal tasks, and cannot effectively adapt to each other.
Ranked #2 on
Image Captioning
on COCO
Statistics of non-polarized points in the CMB polarization maps
We show that the total number density of non-polarized points of the E- and B-families is closely related to the presence of lensing and the tensor-to-scalar ratio $r$.
Cosmology and Nongalactic Astrophysics
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks
Specifically, we first propose a heterogeneous recurrent graph neural network to model the spatiotemporal autocorrelation among air quality and weather monitoring stations.
Dirac semimetal PdTe2 temperature-dependent quasiparticle dynamics and electron-phonon coupling
Two distinct carrier and coherent phonons relaxation processes were identified in the 5 K - 300 K range.
Materials Science Superconductivity
Magnetoelectric coupling and decoupling in multiferroic hexagonal YbFeO3 thin films
The coupling between ferroelectric and magnetic orders in multiferroic materials and the nature of magnetoelectric (ME) effects are enduring experimental challenges.
Materials Science Other Condensed Matter
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks
Our theory shows that deep neural networks are adaptive to the low-dimensional geometric structures of the covariates, and partially explains the success of deep learning for causal inference.
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop
In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF).
DARWIN: A Highly Flexible Platform for Imaging Research in Radiology
Our platform is consists of a radiomics module and a deep learning module.
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning
Motivated by such a computational dilemma, this study is to develop a novel feature space navigation method.
Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning
Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments.
A Color Elastica Model for Vector-Valued Image Regularization
Here, we introduce an addition to the Polyakov action for color images that minimizes the color manifold curvature.
Semantic Context Encoding for Accurate 3D Point Cloud Segmentation
In this paper, we propose a simple yet effective Point Context Encoding (PointCE) module to capture semantic contexts of a point cloud and adaptively highlight intermediate feature maps.
Hybrid Discriminative-Generative Training via Contrastive Learning
In this paper we show that through the perspective of hybrid discriminative-generative training of energy-based models we can make a direct connection between contrastive learning and supervised learning.
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine
Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation.
Global context reasoning for semantic segmentation of 3d point clouds
In this paper, we propose a Point Global Context Reasoning (PointGCR) module to capture global contextual information along the channel dimension.
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.
Inferring Vector Magnetic Fields from Stokes Profiles of GST/NIRIS Using a Convolutional Neural Network
By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS.
Solar and Stellar Astrophysics
PuzzleNet: Scene Text Detection by Segment Context Graph Learning
Recently, a series of decomposition-based scene text detection methods has achieved impressive progress by decomposing challenging text regions into pieces and linking them in a bottom-up manner.
Predicting Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and Recurrent Neural Networks
We present two recurrent neural networks (RNNs), one based on gated recurrent units and the other based on long short-term memory, for predicting whether an active region (AR) that produces an M- or X-class flare will also produce a coronal mass ejection (CME).
Curvature Regularized Surface Reconstruction from Point Cloud
We propose a variational functional and fast algorithms to reconstruct implicit surface from point cloud data with a curvature constraint.
Learning functions varying along a central subspace
The estimation error of this variance quantity is also given in this paper.
Deep Learning for 3D Point Clouds: A Survey
To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.
A Comprehensive Study and Comparison of Core Technologies for MPEG 3D Point Cloud Compression
Point cloud based 3D visual representation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way.
Towards Omni-Supervised Face Alignment for Large Scale Unlabeled Videos
In this paper, we propose a spatial-temporal relational reasoning networks (STRRN) approach to investigate the problem of omni-supervised face alignment in videos.
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e. g., camera, ultrasonic sensor, and GPS).
Triply Robust Off-Policy Evaluation
Ours is a general approach that can be used to augment any existing OPE method that utilizes the direct method.
Guided Adaptive Credit Assignment for Sample Efficient Policy Optimization
In this work, we propose a guided adaptive credit assignment method to do effectively credit assignment for policy gradient methods.
Dense Scale Network for Crowd Counting
The key component of our network is the dense dilated convolution block, in which each dilation layer is densely connected with the others to preserve information from continuously varied scales.
AdaptiveFace: Adaptive Margin and Sampling for Face Recognition
Training large-scale unbalanced data is the central topic in face recognition.
Predicting Solar Flares Using a Long Short-Term Memory Network
The essence of our approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples.
Learning to Search Efficient DenseNet with Layer-wise Pruning
Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources.
Competitive Experience Replay
We propose a novel method called competitive experience replay, which efficiently supplements a sparse reward by placing learning in the context of an exploration competition between a pair of agents.
Sequence-based Person Attribute Recognition with Joint CTC-Attention Model
Attribute recognition has become crucial because of its wide applications in many computer vision tasks, such as person re-identification.
Variational Inference with Tail-adaptive f-Divergence
Variational inference with {\alpha}-divergences has been widely used in modern probabilistic machine learning.
Shrinkage-based Bias-Variance Trade-off for Deep Reinforcement Learning
Deep reinforcement learning has achieved remarkable successes in solving various challenging artificial intelligence tasks.
Pattern-revising Enhanced Simple Question Answering over Knowledge Bases
Question Answering over Knowledge Bases (KB-QA), which automatically answer natural language questions based on the facts contained by a knowledge base, is one of the most important natural language processing (NLP) tasks.
Large-scale Bisample Learning on ID Versus Spot Face Recognition
In this paper, we propose a deep learning based large-scale bisample learning (LBL) method for IvS face recognition.
On Connecting Stochastic Gradient MCMC and Differential Privacy
Significant success has been realized recently on applying machine learning to real-world applications.
N-gram Model for Chinese Grammatical Error Diagnosis
Detection and correction of Chinese grammatical errors have been two of major challenges for Chinese automatic grammatical error diagnosis. This paper presents an N-gram model for automatic detection and correction of Chinese grammatical errors in NLPTEA 2017 task.
Action-depedent Control Variates for Policy Optimization via Stein's Identity
Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems.
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input.
Triangle Generative Adversarial Networks
The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs.
Image-to-Image Translation
Semi-Supervised Image Classification
+1
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching.
Neural Person Search Machines
We investigate the problem of person search in the wild in this work.
Ranked #4 on
Person Re-Identification
on CUHK-SYSU
Video-based Person Re-identification with Accumulative Motion Context
Video based person re-identification plays a central role in realistic security and video surveillance.
Stochastic Sequential Neural Networks with Structured Inference
Inheriting these advantages of stochastic neural sequential models, we propose a structured and stochastic sequential neural network, which models both the long-term dependencies via recurrent neural networks and the uncertainty in the segmentation and labels via discrete random variables.
Multi-View Image Generation from a Single-View
This paper addresses a challenging problem -- how to generate multi-view cloth images from only a single view input.
Learning Policies for Markov Decision Processes from Data
We assume that the policy belongs to a class of parameterized policies which are defined using features associated with the state-action pairs.
Video-based Person Re-identification with Accumulative Motion Context
Video based person re-identification plays a central role in realistic security and video surveillance.
Recurrent Image Captioner: Describing Images with Spatial-Invariant Transformation and Attention Filtering
Along with the prosperity of recurrent neural network in modelling sequential data and the power of attention mechanism in automatically identify salient information, image captioning, a. k. a., image description, has been remarkably advanced in recent years.
Panoptic Studio: A Massively Multiview System for Social Interaction Capture
The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions.
End-to-End Comparative Attention Networks for Person Re-identification
The CAN model is able to learn which parts of images are relevant for discerning persons and automatically integrates information from different parts to determine whether a pair of images belongs to the same person.
Panoptic Studio: A Massively Multiview System for Social Motion Capture
We present an approach to capture the 3D structure and motion of a group of people engaged in a social interaction.
