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Found 120 papers, 32 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.
HandNeRF: Neural Radiance Fields for Animatable Interacting Hands
We propose a novel framework to reconstruct accurate appearance and geometry with neural radiance fields (NeRF) for interacting hands, enabling the rendering of photo-realistic images and videos for gesture animation from arbitrary views.
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation
Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving.
An Adaptive Plug-and-Play Network for Few-Shot Learning
Few-shot learning (FSL) requires a model to classify new samples after learning from only a few samples.
Denoising and Prompt-Tuning for Multi-Behavior Recommendation
In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e. g., click, add-to-cart, and purchase).
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP.
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs
The key idea of our CLIP2GAN is to bridge the output feature embedding space of CLIP and the input latent space of StyleGAN, which is realized by introducing a mapping network.
Coordinating CAV Swarms at Intersections with a Deep Learning Model
Connected and automated vehicles (CAVs) are viewed as a special kind of robots that have the potential to significantly improve the safety and efficiency of traffic.
Phonetic-assisted Multi-Target Units Modeling for Improving Conformer-Transducer ASR system
Exploiting effective target modeling units is very important and has always been a concern in end-to-end automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DeepMLE: A Robust Deep Maximum Likelihood Estimator for Two-view Structure from Motion
In addition, in order to increase the robustness of our framework, we formulate the likelihood function of the correlations of 2D image matches as a Gaussian and Uniform mixture distribution which takes the uncertainty caused by illumination changes, image noise and moving objects into account.
Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems
The experimental results show that the proposed estimator helps save 23. 3% of computational cost measured in floating-point operations for the code completion systems, and 80. 2% of rejected prompts lead to unhelpful completion
Improved Fuzzy $H_{\infty}$ Filter Design Method for Nonlinear Systems with Time-Varing Delay
This paper investigates the fuzzy $H_{\infty}$ filter design issue for nonlinear systems with time-varying delay.
A New Fuzzy $H_{\infty}$ Filter Design for Nonlinear Time-Delay Systems with Mismatched Premise Membership Functions
This paper is concerned with the fuzzy $H_{\infty}$ filter design issue for nonlinear systems with time-varying delay.
Energy Minimization in RIS-Assisted UAV-Enabled Wireless Power Transfer Systems
Then, we investigate the general scenario in which the RF signals are radiated during the flight, aiming to minimize the total energy consumption of the UAV by jointly optimizing the UAV's trajectory, flight time and the RIS's reflection coefficients.
Towards Hybrid-Optimization Video Coding
Our idea is to provide multiple discrete starting points in the global space and optimize the local optimum around each point by numerical algorithm efficiently.
Piecewise Linear Neural Networks and Deep Learning
To apply PWLNN methods, both the representation and the learning have long been studied.
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.
Multiple EffNet/ResNet Architectures for Melanoma Classification
Melanoma is the most malignant skin tumor and usually cancerates from normal moles, which is difficult to distinguish benign from malignant in the early stage.
Image Captioning In the Transformer Age
This drawback inspires the researchers to develop a homogeneous architecture that facilitates end-to-end training, for which Transformer is the perfect one that has proven its huge potential in both vision and language domains and thus can be used as the basic component of the visual encoder and language decoder in an IC pipeline.
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction
Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data.
aiWave: Volumetric Image Compression with 3-D Trained Affine Wavelet-like Transform
Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice.
Evolving symbolic density functionals
Systematic development of accurate density functionals has been a decades-long challenge for scientists.
On the Importance of Building High-quality Training Datasets for Neural Code Search
The performance of neural code search is significantly influenced by the quality of the training data from which the neural models are derived.
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method
This paper is focused on the trajectory planning task for autonomous driving on a curvy road.
Towards understanding retrosynthesis by energy-based models
In this paper, we propose a framework that unifies sequence- and graph-based methods as energy-based models (EBMs) with different energy functions.
DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications
Leveraging DurLAR, with a resolution exceeding that of prior benchmarks, we consider the task of monocular depth estimation and use this increased avail- ability of higher resolution, yet sparse ground truth scene depth information to propose a novel joint supervised/self- supervised loss formulation.
Attribute Artifacts Removal for Geometry-based Point Cloud Compression
In this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove the artifacts of point cloud attributes compressed by G-PCC.
Temporal Context Mining for Learned Video Compression
From the stored propagated features, we propose to learn multi-scale temporal contexts, and re-fill the learned temporal contexts into the modules of our compression scheme, including the contextual encoder-decoder, the frame generator, and the temporal context encoder.
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning
This paper studies a distributed policy gradient in collaborative multi-agent reinforcement learning (MARL), where agents over a communication network aim to find the optimal policy to maximize the average of all agents' local returns.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning
Last, to enhance the embedding space learning, an additional pixel-wise metric learning module is introduced with triplet loss formulated on the pixel-level embedding of the input image.
How Well Does Kohn-Sham Regularizer Work for Weakly Correlated Systems?
Kohn-Sham regularizer (KSR) is a differentiable machine learning approach to finding the exchange-correlation functional in Kohn-Sham density functional theory (DFT) that works for strongly correlated systems.
CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning
Github Copilot, trained on billions of lines of public code, has recently become the buzzword in the computer science research and practice community.
End-to-End Image Compression with Probabilistic Decoding
However, this nature was seldom considered in previous studies on image compression, which usually chose one possible image as reconstruction, e. g. the one with the maximal a posteriori probability.
An Information Fusion Approach to Learning with Instance-Dependent Label Noise
Instance-dependent label noise (IDN) widely exists in real-world datasets and usually misleads the training of deep neural networks.
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
Based on the implementation, we propose a memory-efficient framework called ``EXACT'', which for the first time demonstrate the potential and evaluate the feasibility of training GNNs with compressed activations.
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning
Federated learning (FL) enables edge clients to train collaboratively while preserving individual's data privacy.
iWave3D: End-to-end Brain Image Compression with Trainable 3-D Wavelet Transform
With the rapid development of whole brain imaging technology, a large number of brain images have been produced, which puts forward a great demand for efficient brain image compression methods.
CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems
Given the success of reinforcement learning (RL) in various domains, it is promising to explore the application of its methods to the development of intelligent and autonomous cyber agents.
On Faster Convergence of Scaled Sign Gradient Descent
For the first two cases, it can be shown that the scaled signGD converges at a linear rate.
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains.
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.
Bayesian forecast combination using time-varying features
In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesian Forecasting Model Averaging (FEBAMA).
Fast and Accurate Neural Machine Translation with Translation Memory
It is generally believed that a translation memory (TM) should be beneficial for machine translation tasks.
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs.
Federated Noisy Client Learning
Federated learning (FL) collaboratively trains a shared global model depending on multiple local clients, while keeping the training data decentralized in order to preserve data privacy.
Composition and Application of Current Advanced Driving Assistance System: A Review
Due to the growing awareness of driving safety and the development of sophisticated technologies, advanced driving assistance system (ADAS) has been equipped in more and more vehicles with higher accuracy and lower price.
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
For existing old ads, GMEs first build a graph to connect them with new ads, and then adaptively distill useful information.
Drill the Cork of Information Bottleneck by Inputting the Most Important Data
As a promising tool to better understand the learning dynamic of minibatch SGD, the information bottleneck (IB) theory claims that the optimization process consists of an initial fitting phase and the following compression phase.
Unsupervised domain adaptation via double classifiers based on high confidence pseudo label
In addition to aligning the global distribution, the real domain adaptation should also align the meso distribution and the micro distribution.
Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions
Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models.
Distributed Cooperative Driving in Multi-Intersection Road Networks
Cooperative driving at isolated intersections attracted great interest and had been well discussed in recent years.
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash.
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
In our framework, the server evaluates devices' value of training based on their training loss.
The SARAS Endoscopic Surgeon Action Detection (ESAD) dataset: Challenges and methods
For an autonomous robotic system, monitoring surgeon actions and assisting the main surgeon during a procedure can be very challenging.
Density-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement
The key procedure of haze image translation through adversarial training lies in the disentanglement between the feature only involved in haze synthesis, i. e. style feature, and the feature representing the invariant semantic content, i. e. content feature.
Deep Learning for Android Malware Defenses: a Systematic Literature Review
In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment.
Restoring Execution Environments of Jupyter Notebooks
More than ninety percent of published Jupyter notebooks do not state dependencies on external packages.
Software Engineering
Distributed quantum phase estimation with entangled photons
Distributed quantum metrology can enhance the sensitivity for sensing spatially distributed parameters beyond the classical limits.
Quantum Physics
DEAL: Decremental Energy-Aware Learning in a Federated System
To address the conflict between learning SLO and energy efficiency, we propose DEAL, an energy efficient learning system that saves energy and preserves privacy with a decremental learning design.
A 3D Non-stationary MmWave Channel Model for Vacuum Tube Ultra-High-Speed Train Channels
As a potential development direction of future transportation, the vacuum tube ultra-high-speed train (UHST) wireless communication systems have newly different channel characteristics from existing high-speed train (HST) scenarios.
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation
To improve adaptation efficiency, we learn to recover the user policy and reward from only a few interactions via an inverse reinforcement learning method to assist a meta-level recommendation agent.
Model-based Reinforcement Learning
Recommendation Systems
+2
Learnability and Complexity of Quantum Samples
Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers.
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectures.
Explainable Recommender Systems via Resolving Learning Representations
Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge.
Defending Adversarial Examples via DNN Bottleneck Reinforcement
This paper presents a DNN bottleneck reinforcement scheme to alleviate the vulnerability of Deep Neural Networks (DNN) against adversarial attacks.
Energy-based View of Retrosynthesis
Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery.
Ranked #1 on
Single-step retrosynthesis
on USPTO-50k
Active Crowd Counting with Limited Supervision
To learn a reliable people counter from crowd images, head center annotations are normally required.
Scaling Symbolic Methods using Gradients for Neural Model Explanation
In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation.
A Real-Time Dispatching Strategy for Shared Automated Electric Vehicles with Performance Guarantees
To overcome this complexity, we employ an online minimum drift plus penalty (MDPP) approach for SAEV systems that (i) does not require a priori knowledge of customer arrival rates to the different parts of the system (i. e. it is practical from a real-world deployment perspective), (ii) ensures the stability of customer waiting times, (iii) ensures that the deviation of dispatch costs from a desirable dispatch cost can be controlled, and (iv) has a computational time-complexity that allows for real-time implementation.
A New Modal Autoencoder for Functionally Independent Feature Extraction
Autoencoders have been widely used for dimensional reduction and feature extraction.
Multiuser Full-Duplex Two-Way Communications via Intelligent Reflecting Surface
Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements.
Ontology-based systematic classification and analysis of coronaviruses, hosts, and host-coronavirus interactions towards deep understanding of COVID-19
We hypothesized that ontology can be used as an integrative platform to classify and analyze HCI and disease outcomes.
Other Quantitative Biology
Investigating Quantum Approximate Optimization Algorithms under Bang-bang Protocols
The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices.
Quantum Physics
A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices
We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer.
Warwick Image Forensics Dataset for Device Fingerprinting In Multimedia Forensics
Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication.
Incorporating Multiple Cluster Centers for Multi-Label Learning
In this article, we propose to leverage the data augmentation technique to improve the performance of multi-label learning.
Vanishing Point Guided Natural Image Stitching
Recently, works on improving the naturalness of stitching images gain more and more extensive attention.
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning
With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue.
Software tools for quantum control: Improving quantum computer performance through noise and error suppression
Manipulating quantum computing hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers.
Quantum Physics
Diversifying Topic-Coherent Response Generation for Natural Multi-turn Conversations
In this paper, we propose the Topic-coherent Hierarchical Recurrent Encoder-Decoder model (THRED) to diversify the generated responses without deviating the contextual topics for multi-turn conversations.
An Improved Historical Embedding without Alignment
Our method outperformed three other popular methods in terms of the number of words correctly identified to have changed in meaning.
Residual-Guided In-Loop Filter Using Convolution Neural Network
The block-based coding structure in the hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing, etc.
Click-Through Rate Prediction with the User Memory Network
Both offline and online experiments demonstrate the effectiveness of MA-DNN for practical CTR prediction services.
Cooperative Lane Changing via Deep Reinforcement Learning
In this paper, we study how to learn an appropriate lane changing strategy for autonomous vehicles by using deep reinforcement learning.
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
The intuitions are that ads shown together may influence each other, clicked ads reflect a user's preferences, and unclicked ads may indicate what a user dislikes to certain extent.
Ranked #1 on
Click-Through Rate Prediction
on Avito
Decoding Molecular Graph Embeddings with Reinforcement Learning
We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings.
Accelerating Minibatch Stochastic Gradient Descent using Typicality Sampling
Machine learning, especially deep neural networks, has been rapidly developed in fields including computer vision, speech recognition and reinforcement learning.
Neural-Guided Symbolic Regression with Asymptotic Constraints
The second part, which we call Neural-Guided Monte Carlo Tree Search, uses the network during a search to find an expression that conforms to a set of data points and desired leading powers.
Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier
While MVAE is notable in its impressive source separation performance, the convergence-guaranteed optimization algorithm and that it allows us to estimate source-class labels simultaneously with source separation, there are still two major drawbacks, i. e., the high computational complexity and unsatisfactory source classification accuracy.
Rebooting Research on Detecting Repackaged Android Apps: Literature Review and Benchmark
Repackaging is a serious threat to the Android ecosystem as it deprives app developers of their benefits, contributes to spreading malware on users' devices, and increases the workload of market maintainers.
Software Engineering
An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms
Up to the present, it still lacks a comprehensive study on how current diverse DL frameworks and platforms influence the DL software development process.
Optimization of Molecules via Deep Reinforcement Learning
We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double $Q$-learning and randomized value functions).
Ranked #1 on
Molecular Graph Generation
on ZINC
(QED Top-3 metric)
Molecular Graph Generation
Multi-Objective Reinforcement Learning
+2
Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation
This paper deals with a multichannel audio source separation problem under underdetermined conditions.
Multiple Combined Constraints for Image Stitching
In this paper, geometric and photometric constraints are combined to improve the alignment quality, which is based on the observation that these two kinds of constraints are complementary.
Learning Data-adaptive Nonparametric Kernels
Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility.
An Efficient Deep Reinforcement Learning Model for Urban Traffic Control
Urban Traffic Control (UTC) plays an essential role in Intelligent Transportation System (ITS) but remains difficult.
Semi-blind source separation with multichannel variational autoencoder
This paper proposes a multichannel source separation technique called the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture.
DeepMutation: Mutation Testing of Deep Learning Systems
To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i. e., training data and training programs).
Software Engineering
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer.
Demonstration of Topological Data Analysis on a Quantum Processor
Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure.
Satellite-relayed intercontinental quantum network
This was on the one hand the transmission of images in a one-time pad configuration from China to Austria as well as from Austria to China.
Quantum Physics
Improving Malware Detection Accuracy by Extracting Icon Information
While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware.
Lazy stochastic principal component analysis
Stochastic principal component analysis (SPCA) has become a popular dimensionality reduction strategy for large, high-dimensional datasets.
DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction
Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction.
Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder Network
We develop a novel deep contour detection algorithm with a top-down fully convolutional encoder-decoder network.
Convolutional Neural Network-Based Block Up-sampling for Intra Frame Coding
A block can be down-sampled before being compressed by normal intra coding, and then up-sampled to its original resolution.
Multimedia
Projection based advanced motion model for cubic mapping for 360-degree video
This paper proposes a novel advanced motion model to handle the irregular motion for the cubic map projection of 360-degree video.
Maximizing Investment Value of Small-Scale PV in a Smart Grid Environment
Determining the optimal size and orientation of small-scale residential based PV arrays will become increasingly complex in the future smart grid environment with the introduction of smart meters and dynamic tariffs.
By-passing the Kohn-Sham equations with machine learning
Last year, at least 30, 000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields, ranging from materials science to biochemistry to astrophysics.
An image compression and encryption scheme based on deep learning
As a result, SAE can be used for image compression.
Watch out for This Commit! A Study of Influential Software Changes
One single code change can significantly influence a wide range of software systems and their users.
Software Engineering
Towards Label Imbalance in Multi-label Classification with Many Labels
To the best of our knowledge, we are the first to tackle the imbalance problem in multi-label classification with many labels.
Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Accurate approximations to density functionals have recently been obtained via machine learning (ML).
Understanding Machine-learned Density Functionals
Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density.
