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Found 39 papers, 3 papers with code
CGNN: Traffic Classification with Graph Neural Network
Extensive evaluation over real-world traffic data sets, including normal, encrypted and malicious labels, show that, CGNN improves the prediction accuracy by 23\% to 29\% for application classification, by 2\% to 37\% for malicious traffic classification, and reaches the same accuracy level for encrypted traffic classification.
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
While recent progress has been made in text-to-image generation, text-to-shape generation remains a challenging problem due to the unavailability of paired text and shape data at a large scale.
Multi-Semantic Image Recognition Model and Evaluating Index for explaining the deep learning models
Although deep learning models are powerful among various applications, most deep learning models are still a black box, lacking verifiability and interpretability, which means the decision-making process that human beings cannot understand.
PINGAN Omini-Sinitic at SemEval-2021 Task 4:Reading Comprehension of Abstract Meaning
This paper describes the winning system for subtask 2 and the second-placed system for subtask 1 in SemEval 2021 Task 4: ReadingComprehension of Abstract Meaning.
STRODE: Stochastic Boundary Ordinary Differential Equation
In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training.
EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks.
Behavior of Liquidity Providers in Decentralized Exchanges
In this paper, we aim to understand how liquidity providers react to market information and how they benefit from providing liquidity in DEXes.
Personalized Popular Music Generation Using Imitation and Structure
Many practices have been presented in music generation recently.
Cyclic Arbitrage in Decentralized Exchange Markets
Almost surely, the three floating exchange rates are not perfectly in sync, which opens up arbitrage possibilities for cyclic trading.
Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations
The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos.
Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models
Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks.
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation.
Minimizing Pumping Energy Cost in Real-time Operations of Water Distribution Systems using Economic Model Predictive Control
In this research, a novel economic model predictive control (EMPC) framework for real-time management of WDSs is proposed.
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users.
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner.
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond.
Robust Machine Learning via Privacy/Rate-Distortion Theory
Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples.
A Biologically Plausible Audio-Visual Integration Model for Continual Learning
Furthermore, we define a new continual learning paradigm to simulate the possible continual learning process in the human brain.
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition
Lying at the core of human intelligence, relational thinking is characterized by initially relying on innumerable unconscious percepts pertaining to relations between new sensory signals and prior knowledge, consequently becoming a recognizable concept or object through coupling and transformation of these percepts.
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning.
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction
This is motivated by the rateless property of conventional PCA, where the least important principal components can be discarded to realize variable rate dimensionality reduction that gracefully degrades the distortion.
Disentangled Adversarial Transfer Learning for Physiological Biosignals
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways.
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities.
Ranked #1 on
Face Alignment
on MERL-RAV
Fake News Detection with Different Models
This is a paper for exploring various different models aiming at developing fake news detection models and we had used certain machine learning algorithms and we had used pretrained algorithms such as TFIDF and CV and W2V as features for processing textual data.
Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation
Recently, data-driven approaches motivated by modern deep learning have been applied to optical communications in place of traditional model-based counterparts.
Adversarial Deep Learning in EEG Biometrics
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG.
Learning to Modulate for Non-coherent MIMO
The deep learning trend has recently impacted a variety of fields, including communication systems, where various approaches have explored the application of neural networks in place of traditional designs.
Deep Learning-Based Constellation Optimization for Physical Network Coding in Two-Way Relay Networks
This paper studies a new application of deep learning (DL) for optimizing constellations in two-way relaying with physical-layer network coding (PNC), where deep neural network (DNN)-based modulation and demodulation are employed at each terminal and relay node.
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects.
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks
One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location.
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders
We introduce adversarial neural networks for representation learning as a novel approach to transfer learning in brain-computer interfaces (BCIs).
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
Experimental results on DAVIS and FBMS show that the proposed method outperforms state-of-the-art unsupervised segmentation methods on various benchmark datasets.
An optimized system to solve text-based CAPTCHA
Currently, various types of CAPTCHAs need corresponding segmentation to identify single character due to the numerous different segmentation ways.
Invariant Representations from Adversarially Censored Autoencoders
In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to prevent.
English Out-of-Vocabulary Lexical Evaluation Task
Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge.
Privacy-Preserving Adversarial Networks
We propose a data-driven framework for optimizing privacy-preserving data release mechanisms to attain the information-theoretically optimal tradeoff between minimizing distortion of useful data and concealing specific sensitive information.
Semantic Segmentation with Reverse Attention
Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation.
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning.
Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach
Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings.
