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Found 195 papers, 88 papers with code
It is AI’s Turn to Ask Humans a Question: Question-Answer Pair Generation for Children’s Story Books
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
{\mathcal{P}^2}: A Plan-and-Pretrain Approach for Knowledge Graph-to-Text Generation
Text verbalization of knowledge graphs is an important problem with wide application to natural language generation (NLG) systems.
MovieChats: Chat like Humans in a Closed Domain
In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots.
TL-Explorer: A Digital Humanities Tool for Mapping and Analyzing Translated Literature
TL-Explorer is a digital humanities tool for mapping and analyzing translated literature, encompassing the World Map and the Translation Dashboard.
Region Graph Embedding Network for Zero-Shot Learning
To exploit the progressive interactions among these regions, we represent them as a region graph, on which the parts relation reasoning is performed with graph convolutions, thus leading to our PRR branch.
SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis
Furthermore, to combine syntactic structure and semantic information, we equip the attention score matrices by syntactic mask matrices.
Fantastic Questions and Where to Find Them: FairytaleQA – An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU.
All in Tokens: Unifying Output Space of Visual Tasks via Soft Token
With these new techniques and other designs, we show that the proposed general-purpose task-solver can perform both instance segmentation and depth estimation well.
Ranked #1 on
Monocular Depth Estimation
on NYU-Depth V2
TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models
Our TinyMIM model of tiny size achieves 79. 6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget.
Refined Edge Usage of Graph Neural Networks for Edge Prediction
However, existing methods lack elaborate design regarding the distinctions between two tasks that have been frequently overlooked: (i) edges only constitute the topology in the node classification task but can be used as both the topology and the supervisions (i. e., labels) in the edge prediction task; (ii) the node classification makes prediction over each individual node, while the edge prediction is determinated by each pair of nodes.
Policy Transfer via Enhanced Action Space
Though transfer learning is promising to increase the learning efficiency, the existing methods are still subject to the challenges from long-horizon tasks, especially when expert policies are sub-optimal and partially useful.
CBNet: A Plug-and-Play Network for Segmentation-based Scene Text Detection
Recently, segmentation-based methods are quite popular in scene text detection, which mainly contain two steps: text kernel segmentation and expansion.
KPT: Keyword-guided Pre-training for Grounded Dialog Generation
For the former, the grounding knowledge consists of keywords extracted from the response.
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
To address this issue, we present ConvLab-3, a flexible dialogue system toolkit based on a unified TOD data format.
Exploring Discrete Diffusion Models for Image Captioning
The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one.
Could Giant Pretrained Image Models Extract Universal Representations?
In this paper, we present a study of frozen pretrained models when applied to diverse and representative computer vision tasks, including object detection, semantic segmentation and video action recognition.
Ranked #3 on
Action Recognition In Videos
on Kinetics-400
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment Trees
RLET iteratively performs single step reasoning with sentence selection and deduction generation modules, from which the training signal is accumulated across the tree with elaborately designed aligned reward function that is consistent with the evaluation.
DORE: Document Ordered Relation Extraction based on Generative Framework
In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models.
Conversation Disentanglement with Bi-Level Contrastive Learning
Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations.
Self-supervised Amodal Video Object Segmentation
The key intuition is that the occluded part of an object can be explained away if that part is visible in other frames, possibly deformed as long as the deformation can be reasonably learned.
MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks
Electrical properties (EP), namely permittivity and electric conductivity, dictate the interactions between electromagnetic waves and biological tissue.
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens.
Bridging the Gap to Real-World Object-Centric Learning
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world.
Vega-MT: The JD Explore Academy Translation System for WMT22
We describe the JD Explore Academy's submission of the WMT 2022 shared general translation task.
Whole-Body Lesion Segmentation in 18F-FDG PET/CT
There has been growing research interest in using deep learning based method to achieve fully automated segmentation of lesion in Positron emission tomography computed tomography(PET CT) scans for the prognosis of various cancers.
An Empirical Study and Analysis of Learning Generalizable Manipulation Skill in the SAPIEN Simulator
This paper provides a brief overview of our submission to the no interaction track of SAPIEN ManiSkill Challenge 2021.
Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization
However, due to the tissue overlap, X-ray images are difficult to provide fine-grained features for early diagnosis.
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning
Long-term vertebral fractures severely affect the life quality of patients, causing kyphotic, lumbar deformity and even paralysis.
CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation
Empathy is a trait that naturally manifests in human conversation.
A Survey on Incomplete Multi-view Clustering
However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods.
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Text classification can be useful in many real-world scenarios, saving a lot of time for end users.
SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild
We define two SBSR tasks and construct two benchmarks consisting of more than 46, 000 CAD models, 1, 700 realistic models, and 145, 000 sketches in total.
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification
It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras.
Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling
In this work, we propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification.
TT-PINN: A Tensor-Compressed Neural PDE Solver for Edge Computing
Physics-informed neural networks (PINNs) have been increasingly employed due to their capability of modeling complex physics systems.
Self-Healing Robust Neural Networks via Closed-Loop Control
While numerous attack and defense techniques have been developed, this work investigates the robustness issue from a new angle: can we design a self-healing neural network that can automatically detect and fix the vulnerability issue by itself?
Learning Enhanced Representations for Tabular Data via Neighborhood Propagation
Prediction over tabular data is an essential and fundamental problem in many important downstream tasks.
On Data Scaling in Masked Image Modeling
Our study reveals that: (i) Masked image modeling is also demanding on larger data.
Predicting Electricity Infrastructure Induced Wildfire Risk in California
Our data include historical ignition and wire-down points triggered by grid infrastructure collected between 2015 to 2019 in Pacific Gas & Electricity territory along with various weather, vegetation, and very high resolution data on grid infrastructure including location, age, materials.
Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation
These properties, which we aggregately refer to as optimization friendliness, are identified and analyzed by a set of attention- and optimization-related diagnosis tools.
Ranked #2 on
Instance Segmentation
on COCO test-dev
(using extra training data)
Revealing the Dark Secrets of Masked Image Modeling
In this paper, we compare MIM with the long-dominant supervised pre-trained models from two perspectives, the visualizations and the experiments, to uncover their key representational differences.
Ranked #1 on
Monocular Depth Estimation
on KITTI Eigen split
Tensor Shape Search for Optimum Data Compression
Various tensor decomposition methods have been proposed for data compression.
Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users
Risk assessment is a substantial problem for financial institutions that has been extensively studied both for its methodological richness and its various practical applications.
Dialogue Meaning Representation for Task-Oriented Dialogue Systems
We propose Dialogue Meaning Representation (DMR), a pliable and easily extendable representation for task-oriented dialogue.
iCAR: Bridging Image Classification and Image-text Alignment for Visual Recognition
Second, we convert the image classification problem from learning parametric category classifier weights to learning a text encoder as a meta network to generate category classifier weights.
BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representation
Data augmentations (DA) are the cores to achieving robust sequence-to-sequence learning on various natural language processing (NLP) tasks.
Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment
Meanwhile, representative features for image quality perception in the spatial and frequency domains can also be derived from the IQA model, which are then fed into another windowed transformer architecture for video quality assessment (VQA).
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension
Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Ranked #1 on
Question Generation
on FairytaleQA
EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training
Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems.
Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning
It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method.
StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement
Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions.
Towards Collaborative Question Answering: A Preliminary Study
Knowledge and expertise in the real-world can be disjointedly owned.
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy
A fundamental challenge in Bayesian inference is efficient representation of a target distribution.
A Critical Review of Inductive Logic Programming Techniques for Explainable AI
Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption.
BIG-bench Machine Learning
Explainable artificial intelligence
+2
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
However, semantic segmentation and the CLIP model perform on different visual granularity, that semantic segmentation processes on pixels while CLIP performs on images.
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction
Learning the distribution of future trajectories conditioned on the past is a crucial problem for understanding multi-agent systems.
Representation Learning on Spatial Networks
Specifically, a provably information-lossless and roto-translation invariant representation of spatial information on networks is presented.
SimMIM: A Simple Framework for Masked Image Modeling
We also leverage this approach to facilitate the training of a 3B model (SwinV2-G), that by $40\times$ less data than that in previous practice, we achieve the state-of-the-art on four representative vision benchmarks.
Representation Learning
Self-Supervised Image Classification
Swin Transformer V2: Scaling Up Capacity and Resolution
Three main techniques are proposed: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.
Ranked #3 on
Instance Segmentation
on COCO minival
(using extra training data)
Bootstrap Your Object Detector via Mixed Training
We introduce MixTraining, a new training paradigm for object detection that can improve the performance of existing detectors for free.
Why Propagate Alone? Parallel Use of Labels and Features on Graphs
In this regard, it has recently been proposed to use a randomly-selected portion of the training labels as GNN inputs, concatenated with the original node features for making predictions on the remaining labels.
Inductive Relation Prediction Using Analogy Subgraph Embeddings
Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i. e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training.
Kokoyi: Executable LaTeX for End-to-end Deep Learning
Despite substantial efforts from the deep learning system community to relieve researchers and practitioners from the burden of implementing models with ever-growing complexity, a considerable lingual gap remains between developing models in the language of mathematics and implementing them in the languages of computer.
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
Existing question answering (QA) techniques are created mainly to answer questions asked by humans.
Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation
To this end, we propose a novel deep collaborative multi-modal learning (DCML) to integrate the underlying information presented in facial properties in an adaptive manner to strengthen the facial details for effective unsupervised kinship verification.
Kinship Verification Based on Cross-Generation Feature Interaction Learning
Specifically, we take parents and children as a whole to extract the expressive local and non-local features.
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones.
TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation
Accurate segmentation of organs or lesions from medical images is crucial for reliable diagnosis of diseases and organ morphometry.
Learning Hierarchical Graph Neural Networks for Image Clustering
Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
Context-Aware Attention-Based Data Augmentation for POI Recommendation
It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications.
Video Swin Transformer
The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks.
Ranked #20 on
Action Classification
on Kinetics-600
(using extra training data)
End-to-End Semi-Supervised Object Detection with Soft Teacher
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods.
Ranked #4 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation
To alleviate these problems, we propose a novel deep medical image segmentation framework called Dual Swin Transformer U-Net (DS-TransUNet), which might be the first attempt to concurrently incorporate the advantages of hierarchical Swin Transformer into both encoder and decoder of the standard U-shaped architecture to enhance the semantic segmentation quality of varying medical images.
A Unified Generative Framework for Aspect-Based Sentiment Analysis
Aspect-based Sentiment Analysis (ABSA) aims to identify the aspect terms, their corresponding sentiment polarities, and the opinion terms.
Ranked #1 on
Aspect Sentiment Triplet Extraction
on SemEval
Aspect-oriented Opinion Extraction
Aspect Sentiment Triplet Extraction
+1
A Unified Generative Framework for Various NER Subtasks
To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework.
Ranked #9 on
Nested Named Entity Recognition
on GENIA
Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashing
However, deep hashing networks are vulnerable to adversarial examples, which is a practical secure problem but seldom studied in hashing-based retrieval field.
Breaking Shortcut: Exploring Fully Convolutional Cycle-Consistency for Video Correspondence Learning
Previous cycle-consistency correspondence learning methods usually leverage image patches for training.
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration
This paper emphasizes the importance and efficacy of training, quantization and accelerator design, and calls for more research breakthroughs in the area for AI on the edge.
Self-Supervised Learning with Swin Transformers
We are witnessing a modeling shift from CNN to Transformers in computer vision.
Ranked #52 on
Self-Supervised Image Classification
on ImageNet
Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections
However, the next word prediction training objective is still misaligned with the target zero-shot learning objective.
Group-Free 3D Object Detection via Transformers
Instead of grouping local points to each object candidate, our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers \cite{vaswani2017attention}, where the contribution of each point is automatically learned in the network training.
Ranked #3 on
3D Object Detection
on SUN-RGBD
High-Dimensional Uncertainty Quantification via Tensor Regression with Rank Determination and Adaptive Sampling
Recently, low-rank tensor methods have been developed to mitigate this issue, but two fundamental challenges remain open: how to automatically determine the tensor rank and how to adaptively pick the informative simulation samples.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision.
Ranked #2 on
Image Classification
on OmniBenchmark
Bag of Tricks for Node Classification with Graph Neural Networks
Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependencies, and spurious edges, e. g., as can occur as a result of graph heterophily or adversarial attacks.
A Unified Framework for Specification Tests of Continuous Treatment Effect Models
We propose a general framework for the specification testing of continuous treatment effect models.
Bayesian Inference with Certifiable Adversarial Robustness
We consider adversarial training of deep neural networks through the lens of Bayesian learning, and present a principled framework for adversarial training of Bayesian Neural Networks (BNNs) with certifiable guarantees.
Towards Robust Neural Networks via Close-loop Control
We connect the robustness of neural networks with optimal control using the geometrical information of underlying data to design the control objective.
Semantics Disentangling for Generalized Zero-Shot Learning
Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training.
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning
To address these issues, in this paper, we propose a novel framework that leverages dual variational autoencoders with a triplet loss to learn discriminative latent features and applies the entropy-based calibration to minimize the uncertainty in the overlapped area between the seen and unseen classes.
Composite Adversarial Training for Multiple Adversarial Perturbations and Beyond
One intriguing property of deep neural networks (DNNs) is their vulnerability to adversarial perturbations.
Explore with Dynamic Map: Graph Structured Reinforcement Learning
In reinforcement learning, a map with states and transitions built based on historical trajectories is often helpful in exploration and exploitation.
Active Sampling for Accelerated MRI with Low-Rank Tensors
In this paper, we introduce an active low-rank tensor model for fast MR imaging.
TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors
To bridge this gap, we design and implement TROJANZOO, the first open-source platform for evaluating neural backdoor attacks/defenses in a unified, holistic, and practical manner.
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain.
GenWiki: A Dataset of 1.3 Million Content-Sharing Text and Graphs for Unsupervised Graph-to-Text Generation
With a human-annotated test set, we provide this new benchmark dataset for future research on unsupervised text generation from knowledge graphs.
Ranked #1 on
Unsupervised KG-to-Text Generation
on GenWiki (Fine)
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations.
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning
We argue that the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be sub-optimal for downstream tasks requiring dense pixel predictions.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determination
This paper presents a novel end-to-end framework for low-rank tensorized training of neural networks.
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight min-cut graph partitioning algorithm along with multiple balancing constraints.
CoLAKE: Contextualized Language and Knowledge Embedding
With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, most existing models consider shallow, static, and separately pre-trained entity embeddings, which limits the performance gains of these models.
Casual inference of General Treatment Effects using Neural Networks with A Diverging Number of Confounders
The estimation of causal effects is a primary goal of behavioral, social, economic and biomedical sciences.
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation spaces.
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
FeatGraph provides a flexible programming interface to express diverse GNN models by composing coarse-grained sparse templates with fine-grained user-defined functions (UDFs) on each vertex/edge.
Trojaning Language Models for Fun and Profit
Recent years have witnessed the emergence of a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are composed with simple downstream models and fine-tuned for a variety of NLP tasks.
Adversarial Bipartite Graph Learning for Video Domain Adaptation
To further enhance the model capacity and testify the robustness of the proposed architecture on difficult transfer tasks, we extend our model to work in a semi-supervised setting using an additional video-level bipartite graph.
Ranked #2 on
Domain Adaptation
on HMDB --> UCF (full)
RepPoints V2: Verification Meets Regression for Object Detection
Though RepPoints provides high performance, we find that its heavy reliance on regression for object localization leaves room for improvement.
Ranked #66 on
Object Detection
on COCO test-dev
A Closer Look at Local Aggregation Operators in Point Cloud Analysis
Our investigation reveals that despite the different designs of these operators, all of these operators make surprisingly similar contributions to the network performance under the same network input and feature numbers and result in the state-of-the-art accuracy on standard benchmarks.
Ranked #3 on
3D Semantic Segmentation
on PartNet
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.
Parametric Instance Classification for Unsupervised Visual Feature Learning
This paper presents parametric instance classification (PIC) for unsupervised visual feature learning.
Disentangled Non-Local Neural Networks
This paper first studies the non-local block in depth, where we find that its attention computation can be split into two terms, a whitened pairwise term accounting for the relationship between two pixels and a unary term representing the saliency of every pixel.
Ranked #13 on
Semantic Segmentation
on Cityscapes test
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
Specifically, we design a complementary dual-level semantic transfer mechanism to efficiently discover the potential semantics of tags and seamlessly transfer them into binary hash codes.
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG~2017 dataset after preprocessing, which is far fewer than the millions of data for other tasks such as machine translation.
Relation of the Relations: A New Paradigm of the Relation Extraction Problem
In natural language, often multiple entities appear in the same text.
Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.
Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treat the opposite agent policy as part of the environment.
DGL-KE: Training Knowledge Graph Embeddings at Scale
Experiments on knowledge graphs consisting of over 86M nodes and 338M edges show that DGL-KE can compute embeddings in 100 minutes on an EC2 instance with 8 GPUs and 30 minutes on an EC2 cluster with 4 machines with 48 cores/machine.
Distributed, Parallel, and Cluster Computing
Task-adaptive Asymmetric Deep Cross-modal Hashing
Unlike previous cross-modal hashing approaches, our learning framework jointly optimizes semantic preserving that transforms deep features of multimedia data into binary hash codes, and the semantic regression which directly regresses query modality representation to explicit label.
Negative Margin Matters: Understanding Margin in Few-shot Classification
This paper introduces a negative margin loss to metric learning based few-shot learning methods.
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation
In the feature maps of CNNs, there commonly exists considerable spatial redundancy that leads to much repetitive processing.
Recent Advances and Challenges in Task-oriented Dialog System
Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities.
CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.
Transformer on a Diet
Transformer has been widely used thanks to its ability to capture sequence information in an efficient way.
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
To construct FDRN, we propose a new fast residual dense block (f-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts at the same time.
Dense RepPoints: Representing Visual Objects with Dense Point Sets
We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level.
Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition
But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized.
Deep Self-representative Concept Factorization Network for Representation Learning
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features.
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling
Hamiltonian Monte Carlo (HMC) is an efficient Bayesian sampling method that can make distant proposals in the parameter space by simulating a Hamiltonian dynamical system.
Multi-Scale Self-Attention for Text Classification
In this paper, we introduce the prior knowledge, multi-scale structure, into self-attention modules.
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning
In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning.
Transductive Zero-Shot Hashing for Multilabel Image Retrieval
Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes.
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling
Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of data.
BP-Transformer: Modelling Long-Range Context via Binary Partitioning
The Transformer model is widely successful on many natural language processing tasks.
Ranked #1 on
Machine Translation
on IWSLT2015 Chinese-English
Deep Collaborative Discrete Hashing with Semantic-Invariant Structure
Existing deep hashing approaches fail to fully explore semantic correlations and neglect the effect of linguistic context on visual attention learning, leading to inferior performance.
Active Subspace of Neural Networks: Structural Analysis and Universal Attacks
Secondly, we propose analyzing the vulnerability of a neural network using active subspace and finding an additive universal adversarial attack vector that can misclassify a dataset with a high probability.
Characterize and Transfer Attention in Graph Neural Networks
Does attention matter and, if so, when and how?
Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind
Recent work in cross-lingual contextual word embedding learning cannot handle multi-sense words well.
Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing
Pricing assets has attracted significant attention from the financial technology community.
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Ranked #32 on
Node Classification
on Cora
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part.
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner.
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation
Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a coherent way.
Tucker Tensor Decomposition on FPGA
This paper presents an hardware accelerator for a classical tensor computation framework, Tucker decomposition.
Signal Processing Hardware Architecture
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning
We propose a joint subspace recovery and enhanced locality based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL).
Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning
More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class.
Bayesian Tensorized Neural Networks with Automatic Rank Selection
Tensor decomposition is an effective approach to compress over-parameterized neural networks and to enable their deployment on resource-constrained hardware platforms.
Gradient-based learning for F-measure and other performance metrics
Many important classification performance metrics, e. g. $F$-measure, are non-differentiable and non-decomposable, and are thus unfriendly to gradient descent algorithm.
Spatial-Temporal Relation Networks for Multi-Object Tracking
Recent progress in multiple object tracking (MOT) has shown that a robust similarity score is key to the success of trackers.
Local Relation Networks for Image Recognition
The convolution layer has been the dominant feature extractor in computer vision for years.
Ranked #738 on
Image Classification
on ImageNet
ConvLab: Multi-Domain End-to-End Dialog System Platform
We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments.
An Empirical Study of Spatial Attention Mechanisms in Deep Networks
Attention mechanisms have become a popular component in deep neural networks, yet there has been little examination of how different influencing factors and methods for computing attention from these factors affect performance.