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Found 13 papers, 6 papers with code
Adversarial and Clean Data Are Not Twins
Adversarial attack has cast a shadow on the massive success of deep neural networks.
Adversarial Texts with Gradient Methods
In addition, we empirically show that WMD is closely related to the quality of adversarial texts.
A Transfer-Learnable Natural Language Interface for Databases
In this work, we introduce a general purpose transfer-learnable NLI with the goal of learning one model that can be used as NLI for any relational database.
Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects
Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet.
SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension
A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS).
Deep Multi-attribute Graph Representation Learning on Protein Structures
To address the above challenges, we propose a new graph neural network architecture to represent the proteins as 3D graphs and predict both distance geometric graph representation and dihedral geometric graph representation together.
MGC: A Complex-Valued Graph Convolutional Network for Directed Graphs
We test our model on several graph datasets including directed homogeneous and heterogeneous graphs.
RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques
Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms.
Multi-modal Transformer Path Prediction for Autonomous Vehicle
To better utilize the lane information, the lanes which are in opposite direction to target agent are not likely to be taken by the target agent and are consequently filtered out.
Rethinking Graph Lottery Tickets: Graph Sparsity Matters
Therefore, we propose two techniques to improve GNN performance when the graph sparsity is high.
Dynamic Graph Representation Learning for Depression Screening with Transformer
Early detection of mental disorder is crucial as it enables prompt intervention and treatment, which can greatly improve outcomes for individuals suffering from debilitating mental affliction.
A Survey of Lottery Ticket Hypothesis
The Lottery Ticket Hypothesis (LTH) states that a dense neural network model contains a highly sparse subnetwork (i. e., winning tickets) that can achieve even better performance than the original model when trained in isolation.
VideoBadminton: A Video Dataset for Badminton Action Recognition
In this paper, we introduce the VideoBadminton dataset derived from high-quality badminton footage.
