Search Results for author:
Found 89 papers, 37 papers with code
TopoGAN: A Topology-Aware Generative Adversarial Network
We show in experiments that our method generates synthetic images with realistic topology.
PLATO-KAG: Unsupervised Knowledge-Grounded Conversation via Joint Modeling
Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation.
New Threats against Object Detector with Non-local Block
The experiments show that the universal patches are able to mislead the detector with greater probabilities.
SINC: Service Information Augmented Open-Domain Conversation
Generative open-domain dialogue systems can benefit from external knowledge, but the lack of external knowledge resources and the difficulty in finding relevant knowledge limit the development of this technology.
HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation
The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system.
Active Source Free Domain Adaptation
We then propose minimum happy points learning (MHPL) to actively explore and exploit MH points.
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer
Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection
In this paper, we first show that the expected Kendall's rank correlation is positively correlated to cosine similarity and then indicate that the direction of attribution is the key to attribution robustness.
An empirical equilibrium model of formal and informal credit markets in developing countries
I develop and estimate a dynamic equilibrium model of risky entrepreneurs' borrowing and savings decisions incorporating both formal and local-informal credit markets.
Optimal allocations to heterogeneous agents with an application to stimulus checks
A planner allocates discrete transfers of size $D_g$ to $N$ heterogeneous groups labeled $g$ and has CES preferences over the resulting outcomes, $H_g(D_g)$.
Structure-aware Protein Self-supervised Learning
Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning.
Early life height and weight production functions with endogenous energy and protein inputs
We examine effects of protein and energy intakes on height and weight growth for children between 6 and 24 months old in Guatemala and the Philippines.
You are what your parents expect: Height and local reference points
We explore the exogenous variation in reference height produced by a protein-supplementation experiment in Guatemala to estimate our model's parameters.
Fewer, better pathways for all? Intersectional impacts of rural school consolidation in China's minority regions
Much more than Han youth, ethnic minority youth were negatively affected by closure, in terms of its impact on both educational attainment and written Mandarin facility.
Same environment, stratified impacts? Air pollution, extreme temperatures, and birth weight in south China
This paper investigates whether associations between birth weight and prenatal ambient environmental conditions--pollution and extreme temperatures--differ by 1) maternal education; 2) children's innate health; and 3) interactions between these two.
Estimating the Effects of Educational System Consolidation: The Case of China's Rural School Closure Initiative
We estimate the impact of educational infrastructure consolidation on educational attainment using the case of China's rural primary school closure policies in the early 2000s.
EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation
The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution.
Ranked #4 on
6D Pose Estimation using RGB
on LineMOD
Controllable energy angular spectrum method
A controllable energy method, which considers the undersampling issue of the transfer function and valid spectral energy of a source signal, is proposed to implement angular spectrum diffraction calculation in near and far fields.
Information retrieval for label noise document ranking by bag sampling and group-wise loss
To fit bag sampling well, after query and document are encoded, the global features of each group are extracted by convolutional layer and max-pooling to improve the model's resistance to the impact of labeling noise, finally, calculate the LCE group-wise loss.
Detecting Owner-member Relationship with Graph Convolution Network in Fisheye Camera System
The owner-member relationship between wheels and vehicles contributes significantly to the 3D perception of vehicles, especially in embedded environments.
Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer
During the KD process, the TCL loss transfers the local structure, exploits the higher order information, and mitigates the misalignment of the heterogeneous output of teacher and student networks.
Network-ELAA Beamforming and Coverage Analysis for eMBB/URLLC in Spatially Non-Stationary Rician Channels
Finally, it is shown that the network-ELAA can offer significant coverage extension (50% or more in most of cases) when comparing with the single-AP scenario.
Lightweight Object-level Topological Semantic Mapping and Long-term Global Localization based on Graph Matching
In this paper, we present a novel lightweight object-level mapping and localization method with high accuracy and robustness.
Exploring Domain-Invariant Parameters for Source Free Domain Adaptation
In contrast, we provide a fascinating insight: rather than attempting to learn domain-invariant representations, it is better to explore the domain-invariant parameters of the source model.
Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening
Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success.
TAGPerson: A Target-Aware Generation Pipeline for Person Re-identification
In TAGPerson, we extract information from target scenes and use them to control our parameterized rendering process to generate target-aware synthetic images, which would hold a smaller gap to the real images in the target domain.
ELSA: Enhanced Local Self-Attention for Vision Transformer
Self-attention is powerful in modeling long-range dependencies, but it is weak in local finer-level feature learning.
Ranked #29 on
Instance Segmentation
on COCO minival
TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations
Task-oriented dialogue systems have been plagued by the difficulties of obtaining large-scale and high-quality annotated conversations.
Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition
Decoupling spatiotemporal representation refers to decomposing the spatial and temporal features into dimension-independent factors.
Multimodal Pre-Training Model for Sequence-based Prediction of Protein-Protein Interaction
However, neither sequences nor contact maps can fully characterize structures and functions of the proteins, which are closely related to the PPI problem.
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation
Unsupervised semantic segmentation aims to obtain high-level semantic representation on low-level visual features without manual annotations.
CELLS: Cost-Effective Evolution in Latent Space for Goal-Directed Molecular Generation
We adopt a pre-trained molecular generative model to map the latent and observation spaces, taking advantage of the large-scale unlabeled molecules to learn chemical knowledge.
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification
We first investigate self-supervised learning (SSL) methods with Vision Transformer (ViT) pretrained on unlabelled person images (the LUPerson dataset), and empirically find it significantly surpasses ImageNet supervised pre-training models on ReID tasks.
Ranked #1 on
Unsupervised Person Re-Identification
on Market-1501
(using extra training data)
Docking-based Virtual Screening with Multi-Task Learning
Machine learning shows great potential in virtual screening for drug discovery.
Achieving Human Parity on Visual Question Answering
The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image.
Amendable Generation for Dialogue State Tracking
In this paper, we propose a novel Amendable Generation for Dialogue State Tracking (AG-DST), which contains a two-pass generation process: (1) generating a primitive dialogue state based on the dialogue of the current turn and the previous dialogue state, and (2) amending the primitive dialogue state from the first pass.
Ranked #1 on
Dialogue State Tracking
on Wizard-of-Oz
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+1
Do What Nature Did To Us: Evolving Plastic Recurrent Neural Networks For Generalized Tasks
While artificial neural networks (ANNs) have been widely adopted in machine learning, researchers are increasingly obsessed by the gaps between ANNs and natural neural networks (NNNs).
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation
To explore the limit of dialogue generation pre-training, we present the models of PLATO-XL with up to 11 billion parameters, trained on both Chinese and English social media conversations.
Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion
However, due to the complex nonlinear dynamics in quadrupedal robots and reward sparsity, it is still difficult for RL to learn effective gaits from scratch, especially in challenging tasks such as walking over the balance beam.
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
Along with the pseudo labels, a weight-sharing triple-branch transformer framework is proposed to apply self-attention and cross-attention for source/target feature learning and source-target domain alignment, respectively.
Ranked #1 on
Unsupervised Domain Adaptation
on Office-Home
ADER:Adapting between Exploration and Robustness for Actor-Critic Methods
Combining off-policy reinforcement learning methods with function approximators such as neural networks has been found to lead to overestimation of the value function and sub-optimal solutions.
Scaled ReLU Matters for Training Vision Transformers
In this paper, we further investigate this problem and extend the above conclusion: only early convolutions do not help for stable training, but the scaled ReLU operation in the \textit{convolutional stem} (\textit{conv-stem}) matters.
Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning
In contrast, biological neural networks (BNNs) can adapt to various new tasks by continually updating their connection weights based on their observations, which is aligned with the paradigm of learning effective learning rules in addition to static parameters, e. g., meta-learning.
Exploring the Quality of GAN Generated Images for Person Re-Identification
Recently, GAN based method has demonstrated strong effectiveness in generating augmentation data for person re-identification (ReID), on account of its ability to bridge the gap between domains and enrich the data variety in feature space.
Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity
To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool).
Graph Convolution for Re-ranking in Person Re-identification
In this work, we propose a graph-based re-ranking method to improve learned features while still keeping Euclidean distance as the similarity metric.
Action Set Based Policy Optimization for Safe Power Grid Management
At the planning stage, the search space is limited to the action set produced by the policy.
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction
Recent advances in graph neural networks (GNNs) have shown great promise in applying GNNs for molecular representation learning.
KVT: k-NN Attention for Boosting Vision Transformers
A key component in vision transformers is the fully-connected self-attention which is more powerful than CNNs in modelling long range dependencies.
An Empirical Study of Vehicle Re-Identification on the AI City Challenge
We mainly focus on four points, i. e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge.
City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones
Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions.
TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer
Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures.
A Unified Pre-training Framework for Conversational AI
In this work, we explore the application of PLATO-2 on various dialogue systems, including open-domain conversation, knowledge grounded dialogue, and task-oriented conversation.
Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation
Temporal action proposal generation (TAPG) is a fundamental and challenging task in video understanding, especially in temporal action detection.
Molecular Representation Learning by Leveraging Chemical Information
As graph neural networks have achieved great success in many domains, some studies apply graph neural networks to molecular property prediction and regard each molecule as a graph.
Ranked #5 on
Graph Property Prediction
on ogbg-molhiv
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.
TransReID: Transformer-based Object Re-Identification
Extracting robust feature representation is one of the key challenges in object re-identification (ReID).
Ranked #1 on
Person Re-Identification
on Market-1501-C
Learning to Select External Knowledge with Multi-Scale Negative Sampling
The Track-1 of DSTC9 aims to effectively answer user requests or questions during task-oriented dialogues, which are out of the scope of APIs/DB.
1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking
We extend the classical tracking-by-detection paradigm to this tracking-any-object task.
Ranked #1 on
Multi-Object Tracking
on TAO
(using extra training data)
Multi-object Tracking with a Hierarchical Single-branch Network
Recent Multiple Object Tracking (MOT) methods have gradually attempted to integrate object detection and instance re-identification (Re-ID) into a united network to form a one-stage solution.
1st Place Solution to VisDA-2020: Bias Elimination for Domain Adaptive Pedestrian Re-identification
Considering the large gap between the source domain and target domain, we focused on solving two biases that influenced the performance on domain adaptive pedestrian Re-ID and proposed a two-stage training procedure.
Besov and Triebel-Lizorkin Spaces on Spaces of Homogeneous Type with Applications to Boundedness of Calderón-Zygmund Operators
In this article, the authors introduce Besov and Triebel-Lizorkin spaces on spaces of homogeneous type in the sense of Coifman and Weiss, prove that these (in)homogeneous Besov and Triebel-Lizorkin spaces are independent of the choices of both exp-ATIs (or exp-IATIs) and underlying spaces of distributions, and give some basic properties of these spaces.
Functional Analysis Analysis of PDEs Classical Analysis and ODEs Primary 46E35, Secondary 42B25, 42B20, 42B35, 30L99
Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction
The hierarchical attentive aggregation can capture spatial dependencies among atoms, as well as fuse the position-enhanced information with the capability of discriminating multiple spatial relations among atoms.
Heterochromatic nonlinear optical responses in upconversion nanoparticles for point spread function engineering
Point spread function (PSF) engineering of the emitter can code higher spatial frequency information of an image to break diffraction limit but suffer from the complexed optical systems.
Optics
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors
Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution optical sensors, facilitating better-performing target detection and autonomous navigation in a wide range of robotics applications.
Infrared small target detection based on isotropic constraint under complex background
Infrared search and tracking (IRST) system has been widely concerned and applied in the area of national defence.
Neural Video Coding using Multiscale Motion Compensation and Spatiotemporal Context Model
Over the past two decades, traditional block-based video coding has made remarkable progress and spawned a series of well-known standards such as MPEG-4, H. 264/AVC and H. 265/HEVC.
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
To build a high-quality open-domain chatbot, we introduce the effective training process of PLATO-2 via curriculum learning.
SUPER: A Novel Lane Detection System
AI-based lane detection algorithms were actively studied over the last few years.
PSDet: Efficient and Universal Parking Slot Detection
\romannumeral2, Expert knowledge for parking slot detection is under-estimated.
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.
A Quantitative Analytical Model for Predicting and Optimizing the Rate Performance of Battery Cells
An important objective of designing lithium-ion rechargeable battery cells is to maximize their rate performance without compromising the energy density, which is mainly achieved through computationally expensive numerical simulations at present.
Materials Science Applied Physics
Efficient and Robust Reinforcement Learning with Uncertainty-based Value Expansion
By integrating dynamics models into model-free reinforcement learning (RL) methods, model-based value expansion (MVE) algorithms have shown a significant advantage in sample efficiency as well as value estimation.
MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems
The proposed method takes advantage of the characteristics of recommender systems and draws ideas from the model-based reinforcement learning method for higher sample efficiency.
Model-based Reinforcement Learning
Recommendation Systems
+1
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
Pre-training models have been proved effective for a wide range of natural language processing tasks.
Risk Averse Value Expansion for Sample Efficient and Robust Policy Learning
A promising direction is to combine model-based reinforcement learning with model-free reinforcement learning, such as model-based value expansion(MVE).
Hyperspectral City V1.0 Dataset and Benchmark
This document introduces the background and the usage of the Hyperspectral City Dataset and the benchmark.
Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection
In this paper, we propose a multi-mapping mechanism to better capture the one-to-many relationship, where multiple mapping modules are employed as latent mechanisms to model the semantic mappings from an input post to its diverse responses.
Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment
In this paper, a novel Generation-Evaluation framework is developed for multi-turn conversations with the objective of letting both participants know more about each other.
Learning to Select Knowledge for Response Generation in Dialog Systems
Specifically, a posterior distribution over knowledge is inferred from both utterances and responses, and it ensures the appropriate selection of knowledge during the training process.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Sequential Evaluation and Generation Framework for Combinatorial Recommender System
On the one hand of this framework, an evaluation model is trained to evaluate the expected overall utility, by fully considering the user, item information and the correlations among the co-exposed items.
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
We built neural networks as our policy to map sensor readings to control signals on the UAV.
High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking
Our empirical results complement existing theory and provide a resource to compare methods across a range of scenarios and metrics.
Seeds Cleansing CNMF for Spatiotemporal Neural Signals Extraction of Miniscope Imaging Data
Miniscope calcium imaging is increasingly being used to monitor large populations of neuronal activities in freely behaving animals.
Neurons and Cognition Quantitative Methods
Integrating Dashcam Views Through Inter-Video Mapping
In this paper, an inter-video mapping approach is proposed to integrate video footages from two dashcams installed on a preceding and its following vehicle to provide the illusion that the driver of the following vehicle can see-through the preceding one.
3D-Assisted Feature Synthesis for Novel Views of an Object
Comparing two images from different views has been a long-standing challenging problem in computer vision, as visual features are not stable under large view point changes.
3D-Assisted Image Feature Synthesis for Novel Views of an Object
In this paper, given a single input image of an object, we synthesize new features for other views of the same object.
Unsupervised Multi-Class Joint Image Segmentation
Joint segmentation of image sets is a challenging problem, especially when there are multiple objects with variable appearance shared among the images in the collection and the set of objects present in each particular image is itself varying and unknown.
