Search Results for author:
Found 62 papers, 29 papers with code
Vector-quantized Image Modeling with Improved VQGAN
Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively.
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
Experimental results show that DeepAID can provide high-quality interpretations for unsupervised DL models while meeting the special requirements of security domains.
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives
In many applications of computer graphics, art and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph or layout, and have a computer system automatically generate photo-realistic images that adhere to the input content.
ViTGAN: Training GANs with Vision Transformers
Recently, Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases.
Ranked #12 on
Image Generation
on CIFAR-10
SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search
Graph convolution networks (GCN), which recently becomes new state-of-the-art method for graph node classification, recommendation and other applications, has not been successfully applied to industrial-scale search engine yet.
Improved Transformer for High-Resolution GANs
Attention-based models, exemplified by the Transformer, can effectively model long range dependency, but suffer from the quadratic complexity of self-attention operation, making them difficult to be adopted for high-resolution image generation based on Generative Adversarial Networks (GANs).
Aggregating Nested Transformers
In this work, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical manner.
Ranked #71 on
Image Classification
on CIFAR-10
Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index
Embedding index that enables fast approximate nearest neighbor(ANN) search, serves as an indispensable component for state-of-the-art deep retrieval systems.
PanGu-$α$: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
To enhance the generalization ability of PanGu-$\alpha$, we collect 1. 1TB high-quality Chinese data from a wide range of domains to pretrain the model.
Ranked #1 on
Reading Comprehension (Zero-Shot)
on CMRC 2018
Cloze (multi-choices) (Few-Shot)
Cloze (multi-choices) (One-Shot)
+18
Learning Hamiltonian dynamics by reservoir computer
Reconstructing the KAM dynamics diagram of Hamiltonian system from the time series of a limited number of parameters is an outstanding question in nonlinear science, especially when the Hamiltonian governing the system dynamics are unknown.
Transfer training from smaller language model
We initialize a larger target model from a smaller source model by copy weight values from source model and padding with zeros or small initialization values on it to make the source and target model have approximate outputs, which is valid due to block matrix multiplication and residual connection in transformer structure.
Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction
Despite the recent advances in the literature, existing approaches are limited to moderately short-term prediction (less than a few seconds), while extrapolating it to a longer future quickly leads to destruction in structure and content.
Query Rewriting via Cycle-Consistent Translation for E-Commerce Search
Online A/B experiments show that it improves core e-commerce business metrics significantly.
A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images
In this paper, a novel CD method based on the graph convolutional network (GCN) and multiscale object-based technique is proposed for both homogeneous and heterogeneous images.
Cross-Modal Contrastive Learning for Text-to-Image Generation
The quality of XMC-GAN's output is a major step up from previous models, as we show on three challenging datasets.
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Meanwhile, such applications usually require modeling the intrinsic clusters in high-dimensional data, which usually displays heterogeneous statistical patterns as the patterns of different clusters may appear in different dimensions.
Transfer learning of chaotic systems
Here we investigate transfer learning of chaotic systems from the perspective of synchronization-based state inference, in which a reservoir computer trained by chaotic system A is used to infer the unmeasured variables of chaotic system B, while A is different from B in either parameter or dynamics.
ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding
We argue that such contiguously masking method neglects to model the intra-dependencies and inter-relation of coarse-grained linguistic information.
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes.
Co-evolution of Functional Brain Network at Multiple Scales during Early Infancy
By applying our proposed methodological framework on the collected longitudinal infant dataset, we provided the first evidence that, in the first 2 years of life, the brain functional network is co-evolved at different scales, where each scale displays the unique reconfiguration pattern in terms of modular organization.
GloDyNE: Global Topology Preserving Dynamic Network Embedding
The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step.
Improving NER's Performance with Massive financial corpus
Training large deep neural networks needs massive high quality annotation data, but the time and labor costs are too expensive for small business.
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation
Graph convolutional networks (GCN) have been recently utilized to extract the underlying structures of datasets with some labeled data and high-dimensional features.
A Hybrid Evolutionary Algorithm for Reliable Facility Location Problem
The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply chain and logistics.
Image Augmentations for GAN Training
We provide new state-of-the-art results for conditional generation on CIFAR-10 with both consistency loss and contrastive loss as additional regularizations.
Towards Personalized and Semantic Retrieval: An End-to-End Solution for E-commerce Search via Embedding Learning
Two critical challenges stay in today's e-commerce search: how to retrieve items that are semantically relevant but not exact matching to query terms, and how to retrieve items that are more personalized to different users for the same search query.
A Simple Semi-Supervised Learning Framework for Object Detection
Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data.
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring
We improve the recently-proposed ``MixMatch semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring.
Solving Missing-Annotation Object Detection with Background Recalibration Loss
In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.
Improved Consistency Regularization for GANs
Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator.
ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
Ranked #1 on
Generative Question Answering
on CoQA
(using extra training data)
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.
MANELA: A Multi-Agent Algorithm for Learning Network Embeddings
On the other hand, learning network embeddings on distributively stored networks still remained understudied: To the best of our knowledge, all existing algorithms for learning network embeddings have hitherto been exclusively centralized and thus cannot be applied to these networks.
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
We introduce a new local sparse attention layer that preserves two-dimensional geometry and locality.
Ranked #11 on
Conditional Image Generation
on ImageNet 128x128
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels.
Multimodal, Multilingual Grapheme-to-Phoneme Conversion for Low-Resource Languages
Grapheme-to-phoneme conversion (g2p) is the task of predicting the pronunciation of words from their orthographic representation.
Small-GAN: Speeding Up GAN Training Using Core-sets
Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes.
Consistency Regularization for Generative Adversarial Networks
Generative Adversarial Networks (GANs) are known to be difficult to train, despite considerable research effort.
Ranked #4 on
Conditional Image Generation
on ImageNet 128x128
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs
When samples have internal structure, we often see a mismatch between the objective optimized during training and the model's goal during inference.
Distilling Effective Supervision from Severe Label Noise
For instance, on CIFAR100 with a $40\%$ uniform noise ratio and only 10 trusted labeled data per class, our method achieves $80. 2{\pm}0. 3\%$ classification accuracy, where the error rate is only $1. 4\%$ higher than a neural network trained without label noise.
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features.
Multiple instance dense connected convolution neural network for aerial image scene classification
While the current convolution neural network tends to extract global features and global semantic information in a scene, the geo-spatial objects can be located at anywhere in an aerial image scene and their spatial arrangement tends to be more complicated.
Building change detection based on multi-scale filtering and grid partition
Exhaustive experiments indicate that the proposed method can detect building change types directly and outperform the current multi-index learning method.
Approximation Capabilities of Neural ODEs and Invertible Residual Networks
Neural ODEs and i-ResNet are recently proposed methods for enforcing invertibility of residual neural models.
DynWalks: Global Topology and Recent Changes Awareness Dynamic Network Embedding
Dynamic network embedding aims to learn low dimensional embeddings for unseen and seen nodes by using any currently available snapshots of a dynamic network.
Brain Network Construction and Classification Toolbox (BrainNetClass)
The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods.
ERNIE: Enhanced Representation through Knowledge Integration
We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration).
Ranked #3 on
Chinese Sentiment Analysis
on ChnSentiCorp Dev
Chinese Named Entity Recognition
Chinese Sentence Pair Classification
+7
Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms
The dataset is available online, which contains eight types of modulated signals.
Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics
In this paper, we investigate deep learning (DL)-enabled signal demodulation methods and establish the first open dataset of real modulated signals for wireless communication systems.
Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine
Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space structural correlations of multi-antenna channel are typically hidden in matrix or tensor forms.
A Teacher-Student Framework for Maintainable Dialog Manager
Specifically, the {``}student{''} is an extended dialog manager based on a new ontology, and the {``}teacher{''} is existing resources used for guiding the learning process of the {``}student{''}.
A Unified Mammogram Analysis Method via Hybrid Deep Supervision
In this work, we present a unified mammogram analysis framework for both whole-mammogram classification and segmentation.
Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis
Dynamic functional connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may characterize "chronnectome" diagnostic information for improving MCI classification.
Self-Attention Generative Adversarial Networks
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.
Ranked #12 on
Conditional Image Generation
on ImageNet 128x128
Improving GANs Using Optimal Transport
We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.
Ranked #3 on
Text-to-Image Generation
on COCO
(SOA-C metric)
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images.
Ranked #1 on
Text-to-Image Generation
on Oxford 102 Flowers
Link the head to the "beak": Zero Shot Learning from Noisy Text Description at Part Precision
We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations.
SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.
Ranked #2 on
Text-to-Image Generation
on Oxford 102 Flowers
SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition
In this paper, we propose a new CNN architecture that integrates semantic part detection and abstraction (SPDA-CNN) for fine-grained classification.
Multi-lingual Geoparsing based on Machine Translation
Our results for geoparsing Chinese and Arabic text using our multi-lingual geoparsing method are comparable to our results for geoparsing English text with our English tools.