Search Results for author:
Found 531 papers, 169 papers with code
Detecting Adversarial Examples via Key-based Network
In contrast to existing defense methods, the proposed method does not require knowledge of the process for generating adversarial examples and can be applied to defend against different types of attacks.
Learning to Design Games: Strategic Environments in Reinforcement Learning
In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment.
Practical Algorithms for STV and Ranked Pairs with Parallel Universes Tiebreaking
We also propose novel ILP formulations for PUT-winners under STV and RP, respectively.
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning.
Multi-view registration of unordered range scans by fast correspondence propagation of multi-scale descriptors
This paper proposes a global approach for the multi-view registration of unordered range scans.
Multi-view Registration Based on Weighted Low Rank and Sparse Matrix Decomposition of Motions
Then, it proposes the weighted LRS decomposition, where each block element is assigned with one estimated weight to denote its reliability.
MIS-SLAM: Real-time Large Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing
Idled CPU is used to perform ORB- SLAM for providing robust global pose.
Neural Text Generation: Past, Present and Beyond
This paper presents a systematic survey on recent development of neural text generation models.
Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising
From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes.
Exponential Discriminative Metric Embedding in Deep Learning
With the remarkable success achieved by the Convolutional Neural Networks (CNNs) in object recognition recently, deep learning is being widely used in the computer vision community.
Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative
In this article, we mathematically study several GAN related topics, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative.
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
Real-time advertising allows advertisers to bid for each impression for a visiting user.
Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks
In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event.
tau-FPL: Tolerance-Constrained Learning in Linear Time
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications.
Learning Continuous User Representations through Hybrid Filtering with doc2vec
In order to maximize the predictive performance of our look-alike modeling algorithms, we propose two novel hybrid filtering techniques that utilize the recent neural probabilistic language model algorithm doc2vec.
Happiness Pursuit: Personality Learning in a Society of Agents
We find that the agents that achieve higher happiness during testing against hand-coded AI, have lower happiness when competing against each other.
A Neural Stochastic Volatility Model
In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance.
Set-to-Set Hashing with Applications in Visual Recognition
In this paper, we consider the fundamental problem of finding a nearest set from a collection of sets, to a query set.
Effective scaling registration approach by imposing the emphasis on the scale factor
This paper proposes an effective approach for the scaling registration of $m$-D point sets.
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy
Clustering time series under DTW remains a computationally expensive operation.
Learning text representation using recurrent convolutional neural network with highway layers
Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks.
A Survey on Soft Subspace Clustering
Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces.
Feature Selection as a Multiagent Coordination Problem
In this paper, we formulate feature selection as a multiagent coordination problem and propose a novel feature selection method using multiagent reinforcement learning.
Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation
In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response.
Fusing Multi-Stream Deep Networks for Video Classification
A multi-stream framework is proposed to fully utilize the rich multimodal information in videos.
Factorizing LambdaMART for cold start recommendations
Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees.
Learning to Hash for Indexing Big Data - A Survey
Such learning to hash methods exploit information such as data distributions or class labels when optimizing the hash codes or functions.
Deep Attributes from Context-Aware Regional Neural Codes
Results show that deep attribute approaches achieve state-of-the-art results, and outperforms existing peer methods with a significant margin, even though some benchmarks have little overlap of concepts with the pre-trained CNN models.
Two-Stage Metric Learning
This induces in the input data space a new family of distance metric with unique properties.
Question Answering Against Very-Large Text Collections
Question answering involves developing methods to extract useful information from large collections of documents.
A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising
From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB.
Computer Science and Game Theory
Learning Adaptive Display Exposure for Real-Time Advertising
In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?
Learning to Communicate Implicitly By Actions
The auxiliary reward for communication is integrated into the learning of the policy module.
Learning Shared Dynamics with Meta-World Models
Humans have consciousness as the ability to perceive events and objects: a mental model of the world developed from the most impoverished of visual stimuli, enabling humans to make rapid decisions and take actions.
Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation
With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume.
3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN
Deep learning on point clouds has made a lot of progress recently.
Space-Time Local Embeddings
We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space.
Parametric Local Metric Learning for Nearest Neighbor Classification
We present a new parametric local metric learning method in which we learn a smooth metric matrix function over the data manifold.
Metric Learning with Multiple Kernels
However, the problem then becomes finding the appropriate kernel function.
Transfer Representation Learning with TSK Fuzzy System
A core issue in transfer learning is to learn a shared feature space in where the distributions of the data from two domains are matched.
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models
Accurate prediction of fading channel in future is essential to realize adaptive transmission and other methods that can save power and provide gains.
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's cognitive limitation and/or the tractability of the decision problem.
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking
STV and ranked pairs (RP) are two well-studied voting rules for group decision-making.
Online Reconstruction of Indoor Scenes From RGB-D Streams
A system capable of performing robust online volumetric reconstruction of indoor scenes based on input from a handheld RGB-D camera is presented.
End-to-end feature fusion siamese network for adaptive visual tracking
Both features and the channel weights are utilized in a template generation layer to generate a discriminative template.
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Deep Q-learning has achieved a significant success in single-agent decision making tasks.
Multiagent Systems
Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks
Specifically, before each SC decoding attempt, a long short-term memory (LSTM) network is exploited to either (i) locate the first error bit, or (ii) undo a previous `wrong' flip.
Joint Perception and Control as Inference with an Object-based Implementation
Existing model-based reinforcement learning methods often study perception modeling and decision making separately.
Reinforcement Learning for Nested Polar Code Construction
In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques.
Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning
Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS.
Multiple Independent Subspace Clusterings
To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering.
Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices.
Multi-View Multiple Clustering
It then uses matrix factorization on the individual matrices, along with the shared matrix, to generate diverse clusterings of high-quality.
BayesNAS: A Bayesian Approach for Neural Architecture Search
One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training.
Ranking-based Deep Cross-modal Hashing
Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions.
ActiveHNE: Active Heterogeneous Network Embedding
To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN).
Deep Reinforcement Learning for Scheduling in Cellular Networks
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia.
Learning discriminative features in sequence training without requiring framewise labelled data
We propose a novel method which simultaneously models both the sequence discriminative training and the feature discriminative learning within a single network architecture, so that it can learn discriminative deep features in sequence training that obviates the need for presegmented training data.
Joint Information Preservation for Heterogeneous Domain Adaptation
The two domains often lie in different feature spaces due to diverse data collection methods, which leads to the more challenging task of heterogeneous domain adaptation (HDA).
Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.
Weakly-paired Cross-Modal Hashing
FlexCMH first introduces a clustering-based matching strategy to explore the local structure of each cluster, and thus to find the potential correspondence between clusters (and samples therein) across modalities.
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform.
Multiagent Systems
Graph Attention Memory for Visual Navigation
To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module.
Permanent Magnetic Articulograph (PMA) vs Electromagnetic Articulograph (EMA) in Articulation-to-Speech Synthesis for Silent Speech Interface
Articulation-to-speech (ATS) synthesis is a software design in SSI that directly converts articulatory movement information into audible speech signals.
Speech-based Estimation of Bulbar Regression in Amyotrophic Lateral Sclerosis
Experimental results demonstrated the AFSFRS-R bulbar subscore can be predicted from speech samples, which has clinical implication for automatic monitoring of the disease progression of ALS using speech information.
Realistic Channel Models Pre-training
In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models.
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world.
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
We refer to this process as Leverage.
Cross-modal Zero-shot Hashing
It then defines an objective function to achieve deep feature learning compatible with the composite similarity preserving, category attribute space learning, and hashing coding function learning.
MANAS: Multi-Agent Neural Architecture Search
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective.
MarlRank: Multi-agent Reinforced Learning to Rank
By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure.
GREASE: A Generative Model for Relevance Search over Knowledge Graphs
The model applies to meta-path based relevance where a meta-path characterizes a particular type of semantics of relating the query entity to answer entities.
Mixup-breakdown: a consistency training method for improving generalization of speech separation models
Deep-learning based speech separation models confront poor generalization problem that even the state-of-the-art models could abruptly fail when evaluating them in mismatch conditions.
Detecting Causal Language Use in Science Findings
We then applied the prediction model to measure the causal language use in the research conclusions of about 38, 000 observational studies in PubMed.
Active Multi-Label Crowd Consensus
AMCC accounts for the commonality and individuality of workers, and assumes that workers can be organized into different groups.
Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning
In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate.
Multi-View Multiple Clusterings using Deep Matrix Factorization
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results.
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning
Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation.
Attention-Aware Answers of the Crowd
However, they all assume that workers' label quality is stable over time (always at the same level whenever they conduct the tasks).
Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach
However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics.
Compositional ADAM: An Adaptive Compositional Solver
In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values.
Learning Structured Communication for Multi-agent Reinforcement Learning
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution
Our methods can significantly reduce the cost of development and maintenance of anomaly detection.
Partial Multi-label Learning with Label and Feature Collaboration
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant.
Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives
To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives.
Dynamic Reconstruction of Deformable Soft-tissue with Stereo Scope in Minimal Invasive Surgery
Our SLAM system can: (1) Incrementally build a live model by progressively fusing new observations with vivid accurate texture.
Dynamic Reconstruction
Simultaneous Localization and Mapping
Consistent and Complementary Graph Regularized Multi-view Subspace Clustering
The complementary graph regularizer investigates the specific information of multiple views.
ControlVAE: Controllable Variational Autoencoder
Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation learning.
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation
Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion.
Deep Feature-preserving Normal Estimation for Point Cloud Filtering
In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features.
Actor-Critic Reinforcement Learning for Control with Stability Guarantee
Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation.
Resisting Crowd Occlusion and Hard Negatives for Pedestrian Detection in the Wild
Pedestrian detection has been heavily studied in the last decade due to its wide application.
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.
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices
We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer.
Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
The model provides automated recognition of given scans and generation of reports.
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
Graph neural networks are promising architecture for learning and inference with graph-structured data.
Bidirectional Loss Function for Label Enhancement and Distribution Learning
More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one.
InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling
Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9. 0% mAP on the nuScenes test set.
Ranked #328 on
3D Object Detection
on nuScenes
NPCFace: Negative-Positive Collaborative Training for Large-scale Face Recognition
However, the correlation between hard positive and hard negative is overlooked, and so is the relation between the margins in positive and negative logits.
Bilevel Learning Model Towards Industrial Scheduling
In this paper, we propose a Bilevel Deep reinforcement learning Scheduler, \textit{BDS}, in which the higher level is responsible for exploring an initial global sequence, whereas the lower level is aiming at exploitation for partial sequence refinements, and the two levels are connected by a sliding-window sampling mechanism.
Adaptive Structural Fingerprints for Graph Attention Networks
Yet, how to fully exploit rich structural information in the attention mechanism remains a challenge.
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
Computational prediction of RNA tertiary structures using machine learning methods
RNAs play crucial and versatile roles in biological processes.
3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose Estimation
To address the problem, a novel 3DPVNet is presented in this work, which utilizes 3D local patches to vote for the object 6D poses.
Reinforcement Learning for Control with Probabilistic Stability Guarantee
Reinforcement learning is promising to control dynamical systems for which the traditional control methods are hardly applicable.
Regioned Episodic Reinforcement Learning
Goal-oriented reinforcement learning algorithms are often good at exploration, not exploitation, while episodic algorithms excel at exploitation, not exploration.
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium
Therefore, to achieve robust CMARL, we introduce novel strategies to encourage agents to learn correlated equilibrium while maximally preserving the convenience of the decentralized execution.
TextTN: Probabilistic Encoding of Language on Tensor Network
As a novel model that bridges machine learning and quantum theory, tensor network (TN) has recently gained increasing attention and successful applications for processing natural images.
Learning to Explore with Pleasure
Exploration is a long-standing challenge in sequential decision problem in machine learning.
Learning Predictive Communication by Imagination in Networked System Control
Dealing with multi-agent control in networked systems is one of the biggest challenges in Reinforcement Learning (RL) and limited success has been presented compared to recent deep reinforcement learning in single-agent domain.
Understanding and Leveraging Causal Relations in Deep Reinforcement Learning
Reinforcement Learning (RL) has shown great potential to deal with sequential decision-making problems.
Deep Incomplete Multi-View Multiple Clusterings
Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering.
Multi-typed Objects Multi-view Multi-instance Multi-label Learning
Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels.
Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding
The light encoder architecture separates the shared pre-trained networks from the mappings of generally encoded knowledge to specific domains of SLU, allowing for the domain adaptation to be performed solely at the light encoder and thus increasing efficiency.
Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network
In this paper, the Convolutional Neutral Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer.
Gröbner-Shirshov bases for the Coxeter groups of the types $G_2,F_4,E_6,E_7$ and $E_8$
The author is mainly interest in the Gr\"{o}bner-Shirshov bases of finite Coxeter groups.
Group Theory Rings and Algebras 16S15, 20F55
Liouville type theorems and periodic solutions for the nonhomogeneous parabolic systems
In the present paper we derive Liouville type results and existence of periodic solutions for $\chi^{(2)}$ type systems with non-homogeneous nonlinearities.
Analysis of PDEs 35K9, 35J61, 35B45
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning
In this paper, we show that targeted attacks on black-box NMT systems are feasible, based on poisoning a small fraction of their parallel training data.
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.
Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets
In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
In comparison with the existing RL algorithms, the proposed method can achieve superior performance in terms of maintaining safety.
Automated Prostate Cancer Diagnosis Based on Gleason Grading Using Convolutional Neural Network
The Gleason grading system using histological images is the most powerful diagnostic and prognostic predictor of prostate cancer.
Spectrum Cartography via Coupled Block-Term Tensor Decomposition
Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i. e., a radio map)---from limited samples taken sparsely over the region.
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling
This work employs an idea that models spectral images as tensors following the block-term decomposition model with multilinear rank-$(L_r, L_r, 1)$ terms (i. e., the LL1 model) and formulates the HSR problem as a coupled LL1 tensor decomposition problem.
Time irreversibility and amplitude irreversibility measures for nonequilibrium processes
Time irreversibility should be measured based on the permutations of symmetric vectors rather than symmetric permutations, whereas symmetric permutations can instead be employed to determine the quantitative amplitude irreversibility -- a novel parameter proposed in this paper for nonequilibrium calculated by means of the probabilistic difference in amplitude fluctuations.
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions
Active learning generally involves querying the most representative samples for human labeling, which has been widely studied in many fields such as image classification and object detection.
Automatic Test Suite Generation for Key-Points Detection DNNs using Many-Objective Search (Experience Paper)
In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search.
Learn molecular representations from large-scale unlabeled molecules for drug discovery
Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules.
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction
The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations.
Causal World Models by Unsupervised Deconfounding of Physical Dynamics
The capability of imagining internally with a mental model of the world is vitally important for human cognition.
Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography
In this work, a Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to improve the performance of classification model the mammography-based CAD.
Efficient Semi-Implicit Variational Inference
In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI).
A relic sketch extraction framework based on detail-aware hierarchical deep network
For the fine extraction stage, we design a new multiscale U-Net (MSU-Net) to effectively remove disease noise and refine the sketch.
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games.
Controllable and Diverse Text Generation in E-commerce
The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing \textit{Apex} to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy.
Contrastive Separative Coding for Self-supervised Representation Learning
To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC).
Tune-In: Training Under Negative Environments with Interference for Attention Networks Simulating Cocktail Party Effect
We study the cocktail party problem and propose a novel attention network called Tune-In, abbreviated for training under negative environments with interference.
High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection
As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists.
Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks
We not only verify the performance gain achieved, but also provide implementation-friend designs, i. e., a scalable neural network design for the agent and a virtual environment training framework.
A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics
Learning and analyzing rap lyrics is a significant basis for many web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web.
Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images
In this paper, in order to solve the boundary discontinuity problem in OBB regression, we propose to detect SAR ships by learning polar encodings.
Zero-shot Adversarial Quantization
To address the above issues, we propose a zero-shot adversarial quantization (ZAQ) framework, facilitating effective discrepancy estimation and knowledge transfer from a full-precision model to its quantized model.
Ranked #2 on
Data Free Quantization
on CIFAR-100
(CIFAR-100 W5A5 Top-1 Accuracy metric)
Source-Free Domain Adaptation for Semantic Segmentation
To cope with this issue, we propose a source-free domain adaptation framework for semantic segmentation, namely SFDA, in which only a well-trained source model and an unlabeled target domain dataset are available for adaptation.
Two-phase weakly supervised object detection with pseudo ground truth mining
We explore the effectiveness of some representative detectors utilized as the second-phase detector in two-phase WSOD and propose a two-phase WSOD architecture.
A Lossless Intra Reference Block Recompression Scheme for Bandwidth Reduction in HEVC-IBC
The reference frame memory accesses in inter prediction result in high DRAM bandwidth requirement and power consumption.
A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surface
The evolution of clusters on the linear array and planar array is also considered in the proposed model.
Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach
The first is based on multi-view convolutional neural network by treating signals from different receive antennas as different views of a 3D object and designing the location and operation of view-pooling layer that are suitable for feature fusion of multi-antenna signals.
Investigating the Utility of Multimodal Conversational Technology and Audiovisual Analytic Measures for the Assessment and Monitoring of Amyotrophic Lateral Sclerosis at Scale
Our results provide encouraging evidence of the utility of automatically extracted audiovisual analytics for scalable remote patient assessment and monitoring in ALS.
A General 3D Space-Time-Frequency Non-Stationary THz Channel Model for 6G Ultra-Massive MIMO Wireless Communication Systems
The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users.
A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surfaces with Practical Phase Shifts
In this paper, a three-dimensional (3D) geometry based stochastic model (GBSM) for a massive multiple-input multiple-output (MIMO) communication system employing practical discrete intelligent reflecting surface (IRS) is proposed.
AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy
Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome.
Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images
Specifically, the encoder of a DL model that is pre-trained on the source domain is used to initialize the encoder of a reconstruction model.
Integrated Communication and Navigation for Ultra-Dense LEO Satellite Networks: Vision, Challenges and Solutions
Next generation beyond 5G networks are expected to provide both Terabits per second data rate communication services and centimeter-level accuracy localization services in an efficient, seamless and cost-effective manner.
Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems
Our method is entirely data driven and thus adaptive, i. e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically.
Learning to Select Cuts for Efficient Mixed-Integer Programming
In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12. 42% over the production solver without any accuracy loss of solution.
Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition
End-to-end speech recognition generally uses hand-engineered acoustic features as input and excludes the feature extraction module from its joint optimization.
MM-AVS: A Full-Scale Dataset for Multi-modal Summarization
Multimodal summarization becomes increasingly significant as it is the basis for question answering, Web search, and many other downstream tasks.
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling
However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression.
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model
TextVQA requires models to read and reason about text in images to answer questions about them.
Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images
To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised DL network training.
Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flows
We present a method for conditional sampling for pre-trained normalizing flows when only part of an observation is available.
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning
Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, as we show in this paper, these systems are also vulnerable to training attacks.
Linking Health News to Research Literature
Accurately linking news articles to scientific research works is a critical component in a number of applications, such as measuring the social impact of a research work and detecting inaccuracies or distortions in science news.
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation
Mistranslated numbers have the potential to cause serious effects, such as financial loss or medical misinformation.
Distributed Learning for Time-varying Networks: A Scalable Design
The wireless network is undergoing a trend from "onnection of things" to "connection of intelligence".
A Novel 3D Non-Stationary GBSM for 6G THz Ultra-Massive MIMO Wireless Systems
Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) wireless communication systems.
Is Nash Equilibrium Approximator Learnable?
In this paper, we investigate the learnability of the function approximator that approximates Nash equilibrium (NE) for games generated from a distribution.
CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples.
Bilateral Denoising Diffusion Models
In this paper, we propose novel bilateral denoising diffusion models (BDDMs), which take significantly fewer steps to generate high-quality samples.
PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refinement
There are two main problems hindering the performance of the two-phase WSOD approaches, i. e., insufficient learning problem and strict reliance between the FSD and the pseudo ground truth (PGT) generated by the WSOD model.
Top-N Recommendation with Counterfactual User Preference Simulation
To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games
Similar to the role of Markov decision processes in reinforcement learning, Stochastic Games (SGs) lay the foundation for the study of multi-agent reinforcement learning (MARL) and sequential agent interactions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Recommendation Fairness: From Static to Dynamic
Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem.
Revisiting the Characteristics of Stochastic Gradient Noise and Dynamics
In this paper, we characterize the noise of stochastic gradients and analyze the noise-induced dynamics during training deep neural networks by gradient-based optimizers.
VENet: Voting Enhancement Network for 3D Object Detection
Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step.
Adaptive Curriculum Learning
Therefore, in contrast to recent works using a fixed curriculum, we devise a new curriculum learning method, Adaptive Curriculum Learning (Adaptive CL), adapting the difficulty of examples to the current state of the model.
Online Markov Decision Processes with Non-oblivious Strategic Adversary
In this setting, we first demonstrate that MDP-Expert, an existing algorithm that works well with oblivious adversaries can still apply and achieve a policy regret bound of $\mathcal{O}(\sqrt{T \log(L)}+\tau^2\sqrt{ T \log(|A|)})$ where $L$ is the size of adversary's pure strategy set and $|A|$ denotes the size of agent's action space.
SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations
Learning generalizable speech representations for unseen samples in different domains has been a challenge with ever increasing importance to date.
Offline Pre-trained Multi-Agent Decision Transformer
Offline reinforcement learning leverages static datasets to learn optimal policies with no necessity to access the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Modeling Variable Space with Residual Tensor Networks for Multivariate Time Series
In this framework, we derive the mathematical representation of the variable space, and then use a tensor network based on the idea of low-rank approximation to model the variable space.
A neural network framework for learning Green's function
Green's function plays a significant role in both theoretical analysis and numerical computing of partial differential equations (PDEs).
Fully Decentralized Model-based Policy Optimization with Networked Agents
Model-based RL is an effective approach for reducing sample complexity.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Informative Robust Causal Representation for Generalizable Deep Learning
In many real-world scenarios, such as image classification and recommender systems, it is evidence that representation learning can improve model's performance over multiple downstream tasks.
Continual Learning of Neural Networks for Realtime Wireline Cable Position Inference
In our experiments, we compared the proposed method with multiple state-of-the-art continual learning methods and the mREMIND network outperformed others both in accuracy and in disk space usage.
Model-Based Robust Adaptive Semantic Segmentation
To mitigate this challenge, in this paper, we propose model-based robust adaptive training algorithm (MRTAdapt), a new training algorithm to enhance the robustness of DNN-based semantic segmentation methods against natural variations that leverages model-based robust training algorithms and generative adversarial networks.
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization
State-only imitation learning (SOIL) enables agents to learn from massive demonstrations without explicit action or reward information.
Learning Explicit Credit Assignment for Multi-agent Joint Q-learning
In contrast, we formulate an \emph{explicit} credit assignment problem where each agent gives its suggestion about how to weight individual Q-values to explicitly maximize the joint Q-value, besides guaranteeing the Bellman optimality of the joint Q-value.
A channel attention based MLP-Mixer network for motor imagery decoding with EEG
Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task.
Measuring the Non-Transitivity in Chess
In this paper, we quantify the non-transitivity in Chess through real-world data from human players.
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning.
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
In this paper, we introduce a new generation of RL solvers that learn to minimise safety violations while maximising the task reward to the extent that can be tolerated by the safe policy.
Dispensed Transformer Network for Unsupervised Domain Adaptation
To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.
A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
In this paper, we introduce a two-player zero-sum framework between a trainable \emph{Solver} and a \emph{Data Generator} to improve the generalization ability of deep learning-based solvers for Traveling Salesman Problem (TSP).
