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Found 65 papers, 19 papers with code
Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization
In this paper, we propose CODESCRIBE to model the hierarchical syntax structure of code by introducing a novel triplet position for code summarization.
Latent Schr{ö}dinger Bridge Diffusion Model for Generative Learning
Our theoretical analysis encompasses the establishment of end-to-end error analysis for learning distributions via the latent Schr{\"o}dinger bridge diffusion model.
Model Will Tell: Training Membership Inference for Diffusion Models
In this paper, we explore a novel perspective for the TMI task by leveraging the intrinsic generative priors within the diffusion model.
Region-Aware Exposure Consistency Network for Mixed Exposure Correction
Exposure correction aims to enhance images suffering from improper exposure to achieve satisfactory visual effects.
Learning Exposure Correction in Dynamic Scenes
While image exposure correction is a popular topic, the video counterpart is less explored in the literature.
Portrait Diffusion: Training-free Face Stylization with Chain-of-Painting
Face stylization refers to the transformation of a face into a specific portrait style.
A Hybrid Frame Structure Design of OTFS for Multi-tasks Communications
Orthogonal time frequency space (OTFS) is a promising waveform in high mobility scenarios for it fully exploits the time-frequency diversity using a discrete Fourier transform (DFT) based two dimensional spreading.
OSM-Net: One-to-Many One-shot Talking Head Generation with Spontaneous Head Motions
Other works construct one-to-one mapping between audio signal and head motion sequences, introducing ambiguity correspondences into the mapping since people can behave differently in head motions when speaking the same content.
MFR-Net: Multi-faceted Responsive Listening Head Generation via Denoising Diffusion Model
Responsive listening head generation is an important task that aims to model face-to-face communication scenarios by generating a listener head video given a speaker video and a listener head image.
Faster Stochastic Variance Reduction Methods for Compositional MiniMax Optimization
All the proposed complexities indicate that our proposed methods can match lower bounds to existing minimax optimizations, without requiring a large batch size in each iteration.
"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets
With the advent of general-purpose Generative AI, the interest in discerning its impact on the labor market escalates.
Mobile Supply: The Last Piece of Jigsaw of Recommender System
In order to address the problem of pagination trigger mechanism, we propose a completely new module in the pipeline of recommender system named Mobile Supply.
ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
We propose a novel Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue.
Asymmetric Patch Sampling for Contrastive Learning
Asymmetric appearance between positive pair effectively reduces the risk of representation degradation in contrastive learning.
Adaptive Graph Convolutional Subspace Clustering
Spectral-type subspace clustering algorithms have shown excellent performance in many subspace clustering applications.
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models
The problem of split VFL is to train a model split between the server and the clients.
FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions
Specifically, the head pose prediction module is designed to generate head pose sequences from the source face and driving audio.
Joint embedding in Hierarchical distance and semantic representation learning for link prediction
Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space.
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
The task of repository-level code completion is to continue writing the unfinished code based on a broader context of the repository.
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery
To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning).
OPT: One-shot Pose-Controllable Talking Head Generation
To solve the identity mismatch problem and achieve high-quality free pose control, we present One-shot Pose-controllable Talking head generation network (OPT).
CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation
We introduce a novel method, CPPF++, designed for sim-to-real pose estimation.
Diverse Title Generation for Stack Overflow Posts with Multiple Sampling Enhanced Transformer
Stack Overflow is one of the most popular programming communities where developers can seek help for their encountered problems.
Semantic decomposition Network with Contrastive and Structural Constraints for Dental Plaque Segmentation
Therefore, we propose a semantic decomposition network (SDNet) that introduces two single-task branches to separately address the segmentation of teeth and dental plaque and designs additional constraints to learn category-specific features for each branch, thus facilitating the semantic decomposition and improving the performance of dental plaque segmentation.
A Frequency-aware Software Cache for Large Recommendation System Embeddings
Deep learning recommendation models (DLRMs) have been widely applied in Internet companies.
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency
Knowledge graph-to-text (KG-to-text) generation aims to generate easy-to-understand sentences from the KG, and at the same time, maintains semantic consistency between generated sentences and the KG.
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training
Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework.
Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder
This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI.
MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation
First, a multi-task learning network is proposed to implement AD detection and MMSE score prediction, which exploits feature correlation by adding three multi-task interaction layers between the backbones of the two tasks.
Compilable Neural Code Generation with Compiler Feedback
Automatically generating compilable programs with (or without) natural language descriptions has always been a touchstone problem for computational linguistics and automated software engineering.
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation
Experiments over publicly available datasets demonstrate that our method can help models generate better responses, even such training data are usually impressed as low-quality data.
ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention
The two types of features are entered into the parallel encoders paths with residual attention for extracting feature representation, and then fused into a channel-spatial attention module to adaptively focus on the important features between channel and spatial part for the classification task.
Ranked #9 on
Audio Classification
on ICBHI Respiratory Sound Database
(using extra training data)
Financial time series forecasting with multi-modality graph neural network
The heterogeneous graph network is constructed by the sources as nodes and relations in our financial knowledge graph as edges.
MDistMult: A Multiple Scoring Functions Model for Link Prediction on Antiviral Drugs Knowledge Graph
However, KGs are always incomplete, especially the new constructed COVID-19 KGs.
Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching
Based on the RPC warping, we propose the deep learning based satellite MVS (SatMVS) framework for large-scale and wide depth range Earth surface reconstruction.
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation
Code representation learning, which aims to encode the semantics of source code into distributed vectors, plays an important role in recent deep-learning-based models for code intelligence.
Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning
This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies.
Hierarchical Neighbor Propagation With Bidirectional Graph Attention Network for Relation Prediction
Abstract—The graph attention network (GAT) [1] has started to become a mainstream neural network architecture since 2018, yielding remarkable performance gains in various natural language processing (NLP) tasks.
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network
Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously.
DeepMI: Deep Multi-lead ECG Fusion for Identifying Myocardial Infarction and its Occurrence-time
We propose an end-to-end deep learning approach, DeepMI, to classify MI from normal cases as well as identifying the time-occurrence of MI (defined as acute, recent and old), using a collection of fusion strategies on 12 ECG leads at data-, feature-, and decision-level.
Toward Understanding Supervised Representation Learning with RKHS and GAN
At the population level, we formulate the ideal representation learning task as that of finding a nonlinear map that minimizes the sum of losses characterizing conditional independence (with RKHS) and disentanglement (with GAN).
Ranked #4 on
Image Classification
on Kuzushiji-MNIST
Sufficient and Disentangled Representation Learning
We provide strong statistical guarantees for the learned representation by establishing an upper bound on the excess error of the objective function and show that it reaches the nonparametric minimax rate under mild conditions.
Multi-grained Trajectory Graph Convolutional Networks for Habit-unrelated Human Motion Prediction
A multi-grained trajectory graph convolutional networks based and lightweight framework is proposed for habit-unrelated human motion prediction.
Quantum simulation of a three-mode optomechanical system based on the Fredkin-type interaction
The realization of multimode optomechanical interactions in the single-photon strong-coupling regime is a desired task in cavity optomechanics, but it remains a challenge in realistic physical systems.
Quantum Physics
Generative Learning With Euler Particle Transport
The key task in training is the estimation of the density ratios or differences that determine the residual maps.
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment
In this paper, we propose a novel Contextual Alignment Enhanced Cross Graph Attention Network (CAECGAT) for the task of cross-lingual entity alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments.
ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding
In this paper, we take the benefits of ConvE and KBGAT together and propose a Relation-aware Inception network with joint local-global structural information for knowledge graph Embedding (ReInceptionE).
Deep Dimension Reduction for Supervised Representation Learning
We propose a deep dimension reduction approach to learning representations with these characteristics.
Estimating Knots and Their Association in Parallel Bilinear Spline Growth Curve Models in the Framework of Individual Measurement Occasions
We present the proposed model by simulation studies and a real-world data analysis.
Methodology
A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset
In this paper, we present a synthetic aerial dataset, called the WHU dataset, we created for MVS tasks, which, to our knowledge, is the first large-scale multi-view aerial dataset.
Learning Implicit Generative Models with Theoretical Guarantees
We then solve the McKean-Vlasov equation numerically using the forward Euler iteration, where the forward Euler map depends on the density ratio (density difference) between the distribution at current iteration and the underlying target distribution.
On Newton Screening
Based on this KKT system, a built-in working set with a relatively small size is first determined using the sum of primal and dual variables generated from the previous iteration, then the primal variable is updated by solving a least-squares problem on the working set and the dual variable updated based on a closed-form expression.
A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models
Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size.
TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction
And the global temporal co-occurrence features represent the co-occurrence relationship that different subsequences in a complex motion sequence are appeared simultaneously, which can be obtained automatically with our proposed TrajectoryNet by reorganizing the temporal information as the depth dimension of the input tensor.
Wasserstein-Wasserstein Auto-Encoders
To address the challenges in learning deep generative models (e. g., the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we propose a novel deep generative model, named Wasserstein-Wasserstein auto-encoders (WWAE).
BOLT-SSI: A Statistical Approach to Screening Interaction Effects for Ultra-High Dimensional Data
In this paper, we first propose a simple method for sure screening interactions (SSI).
Methodology
6D Object Pose Estimation without PnP
On this basis, a special layer, Collinear Equation Layer, is added next to region layer to output the 2D projections of the 3D bounding boxs corners.
6D Object Pose Estimation Based on 2D Bounding Box
Our system trains a novel convolutional neural network to regress the unit quaternion, which represents the 3D rotation, from the partial image inside the bounding box returned by 2D detection systems.
BIVAS: A scalable Bayesian method for bi-level variable selection with applications
To address this problem, we consider variational inference for bi-level variable selection (BIVAS).
Applications
A novel total variation model based on kernel functions and its application
In this novel model, we first map each pixel value of an image into a Hilbert space by using a nonlinear map, and then define a coupled image of an original image in order to construct a kernel function.
Saliency Pattern Detection by Ranking Structured Trees
We show that the linear combination of structured labels can well model the saliency distribution in local regions.
What Makes it Difficult to Understand a Scientific Literature?
Through such analysis, we summarized some characteristics and problems which are reflected by people with different levels of knowledge on the comprehension of difficult science and technology literature, which can be modeled in semantic link network.
Multi-ary Pulse Amplitude Modulated Signal Processing Using Bistable Stochastic Resonance
On this basis, the mechanism of the BSR system response to MPAM signal inputs is elucidated, and a corresponding decoding scheme is proposed.
An adaptive Simulated Annealing-based satellite observation scheduling method combined with a dynamic task clustering strategy
In this study, we present an adaptive Simulated Annealing based scheduling algorithm aggregated with a dynamic task clustering strategy (or ASA-DTC for short) for satellite observation scheduling problems (SOSPs).
A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations.
