Search Results for author:
Found 34 papers, 13 papers with code
Learning Neural Volumetric Representations of Dynamic Humans in Minutes
In this paper, we propose a novel method for learning neural volumetric videos of dynamic humans from sparse view videos in minutes with competitive visual quality.
Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media
Automatic detecting rumors on social media has become a challenging task.
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Researchers naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce the human effort and achieve generally top-performing GNNs, but their methods focus more on the architecture search.
Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos
Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images.
Comparison of Spatio-Temporal Models for Human Motion and Pose Forecasting in Face-to-Face Interaction Scenarios
In this work, we present the first systematic comparison of state-of-the-art approaches for behavior forecasting.
Didn't see that coming: a survey on non-verbal social human behavior forecasting
Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years.
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019
Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly.
360-Attack: Distortion-Aware Perturbations From Perspective-Views
In this paper, we propose an adversarial attack targeting spherical images, called 360-attactk, that transfers adversarial perturbations from perspective-view (PV) images to a final adversarial spherical image.
Efficient Neural Radiance Fields for Interactive Free-viewpoint Video
We propose a novel scene representation, called ENeRF, for the fast creation of interactive free-viewpoint videos.
Confidence Propagation Cluster: Unleash Full Potential of Object Detectors
It has been a long history that most object detection methods obtain objects by using the non-maximum suppression (NMS) and its improved versions like Soft-NMS to remove redundant bounding boxes.
Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform
A public instance of Codabench (https://www. codabench. org/) is open to everyone, free of charge, and allows benchmark organizers to compare fairly submissions, under the same setting (software, hardware, data, algorithms), with custom protocols and data formats.
AutoML Meets Time Series Regression Design and Analysis of the AutoSeries Challenge
Driven by business scenarios, we organized the first Automated Time Series Regression challenge (AutoSeries) for the WSDM Cup 2020.
BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark
Most ML approaches focus on generalization performance on unseen data that are similar to the training data (In-Distribution, or IND).
MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction
Through key-value matching based on relevancy evaluation, the proposed MatchVIE can bypass the recognitions to various semantics, and simply focuses on the strong relevancy between entities.
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020
It was based on tuning (validation set) performance of standard machine learning models on real datasets.
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records
One important challenge of applying deep learning to electronic health records (EHR) is the complexity of their multimodal structure.
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
We evaluate existing offline RL algorithms on NeoRL and argue that the performance of a policy should also be compared with the deterministic version of the behavior policy, instead of the dataset reward.
Solution landscapes of the diblock copolymer-homopolymer model under two-dimensional confinement
We investigate the solution landscapes of the confined diblock copolymer and homopolymer in two-dimensional domain by using the extended Ohta--Kawasaki model.
Soft Condensed Matter Computational Physics
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification
The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks.
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization
We propose a cumulative oversampling (CO) method for online learning.
Deep Physiological State Space Model for Clinical Forecasting
Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinical measurements.
Flow Contrastive Estimation of Energy-Based Models
(2) The update of the flow model approximately minimizes the Jensen-Shannon divergence between the flow model and the data distribution.
Beyond Adaptive Submodularity: Adaptive Influence Maximization with Intermediary Constraints
We consider a brand with a given budget that wants to promote a product over multiple rounds of influencer marketing.
LocalGAN: Modeling Local Distributions for Adversarial Response Generation
This paper presents a new methodology for modeling the local semantic distribution of responses to a given query in the human-conversation corpus, and on this basis, explores a specified adversarial learning mechanism for training Neural Response Generation (NRG) models to build conversational agents.
Learning an Adaptive Learning Rate Schedule
The learning rate is one of the most important hyper-parameters for model training and generalization.
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
A recent study showed that using the graphical structure underlying EHR data (e. g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction.
A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation
Generative dialog models usually adopt beam search as the inference method to generate responses.
LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics
The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.
Group Linguistic Bias Aware Neural Response Generation
For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users{'} preference on language styles, topics, etc.
Neural Response Generation via GAN with an Approximate Embedding Layer
This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones.
Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model
Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems.
Incorporating Loose-Structured Knowledge into Conversation Modeling via Recall-Gate LSTM
Modeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability.
Activity Auto-Completion: Predicting Human Activities From Partial Videos
Finally, the missing observation of an activity is predicted as the activity candidates provided by the auto-completion model.
