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Found 64 papers, 6 papers with code
CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue
In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN).
Exploiting Auxiliary Data for Offensive Language Detection with Bidirectional Transformers
Our approach introduces domain adaptation (DA) training procedures to ALBERT, such that it can effectively exploit auxiliary data from source domains to improve the OLD performance in a target domain.
Fusion of Self-supervised Learned Models for MOS Prediction
This challenge aims to predict MOS scores of synthetic speech on two tracks, the main track and a more challenging sub-track: out-of-domain (OOD).
Hierarchical Softmax for End-to-End Low-resource Multilingual Speech Recognition
Low resource speech recognition has been long-suffering from insufficient training data.
Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials
In our paper, the basket trial is employed as an intuitive example to present this new causal inference setting.
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data
In this paper, we reveal the weaknesses of these strategies, i. e., they lead to the loss of predictive information when enforcing the domain invariance; and the treatment effect estimation performance is unstable, which heavily relies on the characteristics of the domain distributions and the choice of domain divergence metrics.
Invertible Image Dataset Protection
We develop a reversible adversarial example generator (RAEG) that introduces slight changes to the images to fool traditional classification models.
GenReg: Deep Generative Method for Fast Point Cloud Registration
To solve this challenge, we propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration.
On Generating Identifiable Virtual Faces
Our virtual face images are visually different from the original ones for privacy protection.
CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization
In this paper, we propose a challenging and untouched problem: \textit{Open-Set Single Domain Generalization} (OS-SDG), where target domains include unseen categories out of source label space.
Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation
In this paper, we propose a pairwise adversarial training approach to augment training data for unsupervised class-imbalanced domain adaptation.
Automated Graph Learning via Population Based Self-Tuning GCN
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification.
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose.
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data
The foremost challenge in treatment effect estimation is how to capture hidden confounders.
Safe reopening of university campuses is possible with COVID-19 vaccination
We construct an agent-based SEIR model to simulate COVID-19 spread at a 16000-student mostly non-residential urban university during the Fall 2021 Semester.
Edge: Enriching Knowledge Graph Embeddings with External Text
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods.
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL).
Searching for Fast Model Families on Datacenter Accelerators
On top of our DC accelerator optimized neural architecture search space, we further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency.
Continual Lifelong Causal Effect Inference with Real World Evidence
We propose a Continual Causal Effect Representation Learning method for estimating causal effect with observational data, which are incrementally available from non-stationary data distributions.
Transferable Feature Learning on Graphs Across Visual Domains
In this paper, we propose a novel Transferable Feature Learning approach on Graphs (TFLG) for unsupervised adversarial domain adaptation, which jointly incorporates sample-level and class-level structure information across two domains.
Co-embedding of Nodes and Edges with Graph Neural Networks
CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space.
Matching in Selective and Balanced Representation Space for Treatment Effects Estimation
The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference.
Collaborative Attention Mechanism for Multi-View Action Recognition
Extensive experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning
Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions.
Stereotype-Free Classification of Fictitious Faces
The proposed approach aids labeling new data (fictitious output images) by minimizing a penalized version of the least squares cost function between realistic pictures and target pictures.
SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud
First, the semantic context information in LiDAR is seldom explored in previous works, which may help identify ambiguous vehicles.
A Survey on Causal Inference
Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up.
Cross-scale Attention Model for Acoustic Event Classification
However, a potential limitation of the network is that the discriminative features from the bottom layers (which can model the short-range dependency) are smoothed out in the final representation.
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space
Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient.
Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning
New methodologies will be needed to ensure the airspace remains safe and efficient as traffic densities rise to accommodate new unmanned operations.
Correlative Channel-Aware Fusion for Multi-View Time Series Classification
Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views.
Learning Robust Data Representation: A Knowledge Flow Perspective
It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompleteness or knowledge domain mismatch.
Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates.
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.
CensNet: Convolution with Edge-Node Switching in Graph Neural Networks
In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features.
Ranked #1 on
Graph Regression
on Lipophilicity
(RMSE@80%Train metric)
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation
Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients.
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.
Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model.
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Recently, deep learning based video super-resolution (SR) methods have achieved promising performance.
SADIH: Semantic-Aware DIscrete Hashing
Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.
Scene Graph Generation with External Knowledge and Image Reconstruction
Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction,~\etc.
Supervised Transfer Learning for Product Information Question Answering
Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product related questions and experienced customers may provide answers voluntarily.
Representation Learning for Treatment Effect Estimation from Observational Data
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation
However, existing approaches separate target label optimization and domain-invariant feature learning as different steps.
X-GANs: Image Reconstruction Made Easy for Extreme Cases
Image reconstruction including image restoration and denoising is a challenging problem in the field of image computing.
A Review on Deep Learning Techniques Applied to Answer Selection
Given a question and a set of candidate answers, answer selection is the task of identifying which of the candidates answers the question correctly.
A Simple End-to-End Question Answering Model for Product Information
When evaluating a potential product purchase, customers may have many questions in mind.
Supervised Treebank Conversion: Data and Approaches
Treebank conversion is a straightforward and effective way to exploit various heterogeneous treebanks for boosting parsing performance.
A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data
It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce.
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation
Estimating treatment effects from observational data is challenging due to the missing counterfactuals.
Enabling Sparse Winograd Convolution by Native Pruning
Sparse methods and the use of Winograd convolutions are two orthogonal approaches, each of which significantly accelerates convolution computations in modern CNNs.
Parallelizing Word2Vec in Multi-Core and Many-Core Architectures
Word2vec is a widely used algorithm for extracting low-dimensional vector representations of words.
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Pruning CNNs in a way that increase inference speed often imposes specific sparsity structures, thus limiting the achievable sparsity levels.
Parallelizing Word2Vec in Shared and Distributed Memory
In combination, these techniques allow us to scale up the computation near linearly across cores and nodes, and process hundreds of millions of words per second, which is the fastest word2vec implementation to the best of our knowledge.
Temporal Subspace Clustering for Human Motion Segmentation
Subspace clustering is an effective technique for segmenting data drawn from multiple subspaces.
