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Found 21 papers, 13 papers with code
Data Distribution Distilled Generative Model for Generalized Zero-Shot Recognition
To counter this, we introduce an end-to-end generative GZSL framework called D$^3$GZSL.
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
The difficulty of the information extraction task lies in dealing with the task-specific label schemas and heterogeneous data structures.
Building explainable graph neural network by sparse learning for the drug-protein binding prediction
Due to the use of the chemical-substructure-based graph, it is guaranteed that any subgraphs in a drug identified by our SLGNN are chemically valid structures.
Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning
Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals.
Inhomogeneous graph trend filtering via a l2,0 cardinality penalty
We study estimation of piecewise smooth signals over a graph.
Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection
To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration.
CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound
Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases.
Deep Isolation Forest for Anomaly Detection
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability.
Ranked #1 on
Anomaly Detection
on NB15-DoS
RayMVSNet: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo
In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth.
Provable Constrained Stochastic Convex Optimization with XOR-Projected Gradient Descent
Here we propose XOR-PGD, a novel algorithm based on Projected Gradient Descent (PGD) coupled with the XOR sampler, which is guaranteed to solve the constrained stochastic convex optimization problem still in linear convergence rate by choosing proper step size.
Resource allocation algorithm for MEC based on Deep Reinforcement Learning
In recent years, driven by the commercialization of the 6th Generation Communication Technology (6G), an increasing number of 6G devices connected to mobile networks produces computation-intensive tasks such as ultra-high-resolution video streaming, inter-active visual reality (VR) gaming, augmented reality (AR).
Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics
We demonstrate that the proposed algorithm can produce a solution of the projection problem with high precision on large scale datasets, and the algorithm is able to significantly outperform the state-of-the-art methods in terms of runtime (about 6-8 times faster than a commercial software with respect to CPU time for input vector with 1 million variables or more).
DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers.
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network
We obtain an optimal attention-guided embedding space with expanded high-level information and rich semantics, and thus outlying behaviors of the queried outlier can be better unfolded.
Wasserstein Robust Classification with Fairness Constraints
We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion.
Physical Distance Between Quantum States
We propose a physical distance between two quantum states.
Quantum Physics
Outlier Detection Ensemble with Embedded Feature Selection
Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance.
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data
Mixed-type data are pervasive in real life, but very limited outlier detection methods are available for these data.
Ranked #1 on
Outlier Detection
on Hepatitis
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data
Non-IID categorical data are ubiquitous and distinct in real-world applications.
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds
We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds.
