Search Results for author:
Found 41 papers, 15 papers with code
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.
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.
EasyVolcap: Accelerating Neural Volumetric Video Research
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations.
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.
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).
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.
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.
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.
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.
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.
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.
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.
Exploring Graph-aware Multi-View Fusion for Rumor Detection on Social Media
Automatic detecting rumors on social media has become a challenging task.
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.
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.
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.
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.
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.
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.
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.
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.
Learning an Adaptive Learning Rate Schedule
The learning rate is one of the most important hyper-parameters for model training and generalization.
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.
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.
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization
We propose a cumulative oversampling (CO) method for online learning.
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.
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
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.
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.
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.
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.
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.
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.
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.
Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation
To address this limitation, we propose a Text-guided Unsupervised StyleGAN Latent Transformation (TUSLT) model, which adaptively infers a single transformation step in the latent space of StyleGAN to simultaneously manipulate multiple attributes on a given input image.
Blemish-Aware and Progressive Face Retouching With Limited Paired Data
It is worth noting that we reduce the dependence of BPFRe on paired training samples by imposing effective regularization on unpaired ones.
Relightable and Animatable Neural Avatar from Sparse-View Video
Based on the HDQ algorithm, we leverage sphere tracing to efficiently estimate the surface intersection and light visibility.
Im4D: High-Fidelity and Real-Time Novel View Synthesis for Dynamic Scenes
This paper aims to tackle the challenge of dynamic view synthesis from multi-view videos.
4K4D: Real-Time 4D View Synthesis at 4K Resolution
Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU, which is 30x faster than previous methods and achieves the state-of-the-art rendering quality.
AI Competitions and Benchmarks: towards impactful challenges with post-challenge papers, benchmarks and other dissemination actions
Organising an AI challenge does not end with the final event.
