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Found 15 papers, 6 papers with code
TAG: Gradient Attack on Transformer-based Language Models
In this paper, as the first attempt, we formulate the gradient attack problem on the Transformer-based language models and propose a gradient attack algorithm, TAG, to reconstruct the local training data.
Federated Learning
Cryptography and Security
Edge Attention-based Multi-Relational Graph Convolutional Networks
The proposed GCN model, which we call edge attention-based multi-relational GCN (EAGCN), jointly learns attention weights and node features in graph convolution.
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model.
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure.
Ranked #28 on
Link Prediction
on FB15k-237
VIGAN: Missing View Imputation with Generative Adversarial Networks
Especially, when certain samples miss an entire view of data, it creates the missing view problem.
Diable: Efficient Dialogue State Tracking as Operations on Tables
Sequence-to-sequence state-of-the-art systems for dialogue state tracking (DST) use the full dialogue history as input, represent the current state as a list with all the slots, and generate the entire state from scratch at each dialogue turn.
A Posteriori Probabilistic Bounds of Convex Scenario Programs with Validation Tests
By synthesizing comprehensive information about support constraints and validation tests, improved risk evaluation can be achieved for randomized solutions in comparison with existing a posteriori bounds.
Robust Model Predictive Control of Irrigation Systems with Active Uncertainty Learning and Data Analytics
For evapotranspiration forecast error, the support vector clustering-based uncertainty set is adopted, which can be conveniently built from historical data.
Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer
However, there has been no attempt to exploit GNN to create taxonomies.
Open Temporal Relation Extraction for Question Answering
Understanding the temporal relations among events in text is a critical aspect of reading comprehension, which can be evaluated in the form of temporal question answering (TQA).
Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs
These questions often involve three time-related challenges that previous work fail to adequately address: 1) questions often do not specify exact timestamps of interest (e. g., "Obama" instead of 2000); 2) subtle lexical differences in time relations (e. g., "before" vs "after"); 3) off-the-shelf temporal KG embeddings that previous work builds on ignore the temporal order of timestamps, which is crucial for answering temporal-order related questions.
Ranked #2 on
Question Answering
on CronQuestions
Dimension Reduction for Efficient Data-Enabled Predictive Control
In this letter, we propose a simple yet effective singular value decomposition (SVD) based strategy to reduce the optimization problem dimension in data-enabled predictive control (DeePC).
Data-Driven Safe Controller Synthesis for Deterministic Systems: A Posteriori Method With Validation Tests
We first formulate the safety synthesis problem as a robust convex program (RCP) based on notion of control barrier function.
Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class
Most existing methods are based on convolutional networks and prevent forgetting through knowledge distillation, which (1) need to add additional convolutional layers to predict new classes, and (2) ignore to distinguish different regions corresponding to old and new classes during knowledge distillation and roughly distill all the features, thus limiting the learning of new classes.
Class-Incremental Semantic Segmentation
Knowledge Distillation
+1
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments, and propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints.
